From 25f1ff96af6c8d97170f0ea48556d70d799f9201 Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Tue, 28 Apr 2026 11:37:58 -0500 Subject: [PATCH 01/37] fix: Update vignette and docs for BRCore 2.0.2 CRAN submission --- vignettes/BRCore-vignette.Rmd.orig | 139 +++++++----------- vignettes/images/fig1_plot_metrics-1.png | Bin 41669 -> 43561 bytes .../images/fig2_rarefaction_variance-1.png | Bin 34055 -> 35129 bytes vignettes/images/fig3_identified_core-1.png | Bin 20995 -> 21092 bytes vignettes/images/fig4_plot_increase-1.png | Bin 37955 -> 39960 bytes vignettes/images/fig5_plot_elbow-1.png | Bin 37517 -> 39349 bytes vignettes/images/fig6_plot_type_bar-1.png | Bin 23005 -> 38060 bytes vignettes/images/fig7_plot_type_line-1.png | Bin 46795 -> 62802 bytes vignettes/images/fig8_plot_type_heatmap-1.png | Bin 33767 -> 46924 bytes vignettes/images/fig9_plot_neutral_fit-1.png | Bin 48493 -> 51171 bytes 10 files changed, 54 insertions(+), 85 deletions(-) diff --git a/vignettes/BRCore-vignette.Rmd.orig b/vignettes/BRCore-vignette.Rmd.orig index d9d5464..c9bf7b3 100644 --- a/vignettes/BRCore-vignette.Rmd.orig +++ b/vignettes/BRCore-vignette.Rmd.orig @@ -71,7 +71,7 @@ The `BRCore` R package comes with a few datasets. Three 16S datasets: `switchgra `mimulus`, and `bean`, from @Shade2019Abundance; a 16S dataset, called `bcse`, from leaves of ten different cropping systems in the Bioenergy Crop Research Experiment from @Haan2023Contrasting. Here we are going to use `bcse` since it is not rarefied -in contrast of the other three. +in contrast to the other three. ```{r load bcse, echo=TRUE} data("bcse", package = "BRCore") @@ -86,13 +86,13 @@ We are not going to talk about the importance of rarefaction as it is not the go of this vignette, but if you are interested you should read @Schloss2024Rarefaction, @Schloss2024Waste, @Hong2022Rarefy, and @Weiss2017Normalization as a start. -In `BRCore`, to identified the ideal rarefaction cutoff we can plot a simple series -of visuals to guide us in the decision. As we all know, we can, more or less, +In `BRCore`, to identify the ideal rarefaction cutoff we can plot a simple series +of visuals to guide us in the decision. We can, more or less, estimate how well a DNA sample will sequence, but there are always some samples that fail to sequence (or sequence badly) and some others that will sequence "too well" for reasons that are outside our control or by chance. -In the end we will need to decide how many samples we are accepting to discard and +In the end we will need to decide how many samples we are willing to discard and how much diversity we want to retain in our data. To do that we can use some help by plotting some diagnostics plots as shown below. @@ -102,7 +102,7 @@ bcse_metrics <- add_rarefaction_metrics(data = bcse) bcse_metrics ``` -```{r fig1_plot_metrics, echo=TRUE, fig.cap="Figure 1: Rarefaction metrics. a-b, histograms of samples. c, Good's coverage per total number of sequences in a sample. d-e, log10 of sequences in a sample. f, samples ranked by sequence reads in a sample."} +```{r fig1_plot_metrics, echo=TRUE, fig.width=8, fig.height=6, fig.cap="Figure 1: Rarefaction metrics. a-b, histograms of samples. c, Good's coverage per total number of sequences in a sample. d-e, log10 of sequences in a sample. f, samples ranked by sequence reads in a sample."} rarefaction_plot <- plot_rarefaction_metrics(bcse_metrics) print(rarefaction_plot) ``` @@ -122,21 +122,12 @@ coverage indicates many rare, potentially missed, taxa. ## Rarefy the data and explore variance propagation Originally developed by @Sanders1968Marine rarefaction has been adopted widely in -ecology and microbiome research. It consists on randomly re sampling, multiple times, -the same number of species (ASV/OTUs) across all samples, to calculate the expected -number of species that would be found if all the samples were all the same. It basically -prevents samples with more individuals (e.g., more sequencing reads) from appearing -more diverse simply because they were sampled more heavily. - -Because rarefaction is a random process it add noise to the data, each re sampling -is slightly different from others due to chance. This can be problematic, especially -when a very low rarefaction cutoff is selected, when for example we want to compare -2 treatments. This is due to the fact that the lower the rarefaction cutoff the more -chance you have that rarefaction iterations will differ one another as also -showed by @Cameron2021Rarefying. - -We developed functions that can perform and store all the rarefaction iterations -so to evaluate (and visualize) the generated noise in the process as show below +ecology and microbiome research. It consists of randomly resampling the same number of species (ASV/OTUs) multiple times across all samples to estimate the expected number of species that would be observed if all samples were identical. It prevents samples with more individuals (e.g., more sequencing reads) from appearing +more diverse simply because they were sampled more in depth. + +Because rarefaction is a random process, it adds noise to the data. Each resampling event is slightly different from others due to chance. This can be problematic, especially when a very low rarefaction cutoff is selected, when, for example, we want to compare 2 treatments. With lower rarefaction cutoffs, the greater the chance you have that rarefaction iterations will differ from one another, as also shown by @Cameron2021Rarefying. + +We developed functions that can perform and store all the rarefaction iterations to evaluate (and visualize) the noise generated in the process, as shown below: ```{r rarefy bcse, echo=TRUE} bcse_rarefied_list <- @@ -144,7 +135,7 @@ bcse_rarefied_list <- physeq_obj = bcse, depth_level = 1000, num_iter = 3, - .as_array = FALSE, + .as = "list", set_seed = 7642 ) ``` @@ -156,12 +147,12 @@ class(bcse_rarefied_list) str(bcse_rarefied_list[[1]], list.len = 10) # Dimensions of iteration #1 ``` -And plot the variance (the noise we talked above) across the 10 iterations of +We can plot the variance (the noise we talked above) across the 3 iterations of rarefaction. We can use the `plot_variance_propagation()` function to visualize this variance in Alpha diversity across samples and iterations and compare Raw (non-rarefied) vs. Rarefied data. -```{r fig2_rarefaction_variance, echo=TRUE, fig.cap="Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 10 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the 'Plot' variable, which is a nested variable within 'Crop'."} +```{r fig2_rarefaction_variance, echo=TRUE, fig.cap="Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 3 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the 'Plot' variable, which is a nested variable within 'Crop'."} rarefaction_variance_plot <- plot_variance_propagation( physeq_obj = bcse, rarefied = bcse_rarefied_list, @@ -173,25 +164,24 @@ print(rarefaction_variance_plot) ``` -Review the rarefaction and variance plots to determine the ideal sampling depth. -You are looking for a "sweet spot" that maintains high diversity and sample sizes +**Review the rarefaction and variance plots to determine the ideal sampling depth.** +You are looking for a "sweet spot" that maintains high diversity and samples while keeping variance across iterations low. Pay extra attention when rarefaction -cutoff is quite low, as show by @Cameron2021Rarefying. Note this depth, as it will -serve as the cutoff for the `identify_core()` step in your pipeline. +cutoff is quite low, as show by @Cameron2021Rarefying. **Note this depth, as it will +serve as the cutoff for the `identify_core()` step in your pipeline.** + + +### Recreate a phyloseq object with a rarefied `otu_table()` + +***This is an optional step.*** -### OPTIONAL: Recreate a phyloseq object with rarefied `otu_table()` +The `identify_core()` function accepts a phyloseq object, whether rarefied or non-rarefied. When supplying a phyloseq object with a non-rarefied `otu_table`, you must provide a list of rarefied OTU tables that result from `multi_rarefy()`. In our case, we generated `bcse_rarefied_list`. -The `identify_core()` function, handles the rarefaction iterations internally, you -do not need to worry about rarefaction. The results from `add_rarefaction_metrics()`, + The results from `add_rarefaction_metrics()`, `plot_rarefaction_metrics()`, and `multi_rarefy()` are meant to explore the data -and inform the chosen parameters for `identify_core()`. You will specify how many -rarefaction iterations you decided to run within the function. +and inform the chosen parameters for `identify_core()`. Function `identify_core()` will calculate the distance matrix for all iterations supplied and rank OTUs based on the average distance across iterations. -If you have a phyloseq object with a rarefied `otu_table()` or if you want to use one -specific rarefaction iteration generated by `multi_rarefy()` you also have -these options. The choice is up to you and depends on your preferences and the -specific goals of your analysis. The `update_otu_table()` function allows for -replacing the `otu_table()` with the rarefied table you prefer. +If you have a phyloseq object with a rarefied `otu_table()` or if you want to use a specific rarefaction iteration generated by `multi_rarefy()`, you also have these options. *The choice is up to you and depends on your preferences and the specific goals of your analysis.* The `update_otu_table()` function allows for replacing the `otu_table()` with the rarefied table you prefer. ```{r update_otu_table, echo=TRUE} @@ -201,20 +191,16 @@ bcse_rare_single <- update_otu_table( iteration = 1 # Speficify which iteration to use for the updated OTU table ) ``` +Let's proceed to the core analysis. # Core analysis ### Identify the core ASV/OTUs set -We can now identify how many core ASV/OTUs are present in the dataset. One -important difference that separates `identify_core()` form other methods of -identification of core taxa is that you can specify a variable (in the case -of the `bcse` dataset is `Crop`) so that OTUs can be core for a level of the -variable but possibly not for others. This comes form the idea that core -microbiome can be transient in space and time as show by @Shade2019Abundance. +We can now identify how many core ASV/OTUs are present in the dataset. One important difference that separates `identify_core()` from other methods of identification of core taxa is that you can specify a variable (in the case of the `bcse` dataset is `Crop`) so that OTUs can be core for a level of the variable but possibly not for others. The idea stems from the assumption that the microbiome can be transient in space and time, as shown by @Shade2019Abundance. -*We continue with the `bcse` phyloseq object and the rarefied data `bcse_rarefied_list` throughout the vignette.* +**We continue with the `bcse` phyloseq object and the rarefied data `bcse_rarefied_list` throughout the vignette.** See below for example using a single iteration. ```{r identify_core, echo=TRUE} @@ -239,26 +225,12 @@ bcse_core_multi <- identify_core( ## *How the is the core identified?* -As shown by @Shade2019Abundance, an interesting way explore the abundance and -occupancy inclusion thresholds used to define the core microbiome is to evaluate how -well the resulting core membership captures the overarching patterns of beta -diversity found in the complete dataset. This approach treats the core microbiome -as a representative subset that should preserve the main ecological relationships -and sample-to-sample differences observed when using all taxa. By measuring how -closely the beta diversity patterns calculated from core taxa alone match those -from the full dataset, researchers can objectively assess whether their chosen -thresholds produce a meaningful and informative core that maintains the essential -structural information of the microbial community while reducing complexity. +As shown by @Shade2019Abundance, an interesting way to explore the abundance and occupancy inclusion thresholds used to define the core microbiome is to evaluate how well the resulting core membership captures the overarching patterns of beta-diversity found in the complete dataset. This approach treats the core microbiome as a representative subset that should preserve the main ecological relationships and sample-to-sample differences observed when using all taxa. By measuring how closely the beta-diversity patterns calculated from core taxa alone match those from the full dataset, researchers can objectively assess whether their chosen thresholds yield a meaningful and informative core that retains the essential structural information of the microbial community while reducing complexity. -To identify the optimal point where increasing the core inclusion threshold provides -diminishing returns in explanatory value, we offer two automated methods. +To identify the optimal point where increasing the core inclusion threshold provides diminishing returns in explanatory value, we offer two automated methods. * The first is a more stringent `elbow` approach that finds the bend in the -abundance-occupancy curve where adding more taxa yields progressively smaller -improvements. This method calculates the first-order difference (numerical -differentiation) by assigning a score to each potential cutoff point, splitting -the curve into two parts, and finding the point that maximizes the difference -in average rates of change between these parts. +abundance-occupancy curve where adding more taxa yields progressively smaller improvements. This method calculates the first-order difference (numerical differentiation) by assigning a score to each potential cutoff point, splitting the curve into two parts, and finding the cutoff that maximizes the difference in average rates of change between the two parts. * The second method uses a final percent `increase` threshold in beta-diversity (recommend 2% or more), which continues adding taxa until improvements fall @@ -269,11 +241,7 @@ equation $C = 1 - (BC_{core}/BC_{all})$, where $C$ represents the contribution o ranked taxa to total similarity, $BC_{core}$ is the Bray-Curtis similarity using only core taxa, and $BC_{all}$ uses the complete dataset. -The cumulative explanatory value of adding each next-ranked taxon can be plotted -using the `plot_identified_core()` function, with the vertical lines indicating -the elbow and 2% threshold cutoff methods. These approaches eliminate the need -for subjective threshold setting by automatically identifying natural breakpoints -where additional taxa provide marginal returns in explanatory power. +The cumulative explanatory value of adding each next-ranked taxon can be plotted using the `plot_identified_core()` function, with the vertical lines indicating the elbow and 2% threshold cutoff methods. These approaches eliminate the need for subjective threshold setting by automatically identifying natural breakpoints where additional taxa provide marginal returns in explanatory power. **The output is a list of 13 named items.** @@ -294,7 +262,9 @@ bcse_identified_core <- plot_identified_core( increase_value = bcse_core_multi$increase_value ) -print(bcse_identified_core) +# plot_identified_core() outputs a list with a dataframe and a ggplot + +print(bcse_identified_core$plot_identified_core) ``` @@ -332,14 +302,8 @@ plot_abund_occ_elbow + ### Visualize the core set across the variable used for detecting the core -The `plot_core_distribution()` it can be used to visualize ASV/OTUs occupancy across -the variable specified during the identification of the core. The function provides -three visualization methods—bar, line, and heatmap—which can be toggled using the -plot_type argument. The optimal choice often depends on the number of levels in -your grouping variable. For instance, bar or line plots are highly effective for -up to five levels, whereas heatmaps are better suited for more complex datasets. -While these are general recommendations, the core data is fully accessible in -`bcse_core_multi` should you wish to develop a custom visualization. +The `plot_core_distribution()` function can be used to visualize ASV/OTU occupancy across the variable specified during core identification. The function provides three visualization methods (bar, line, and heatmap) that can be toggled via the plot_type argument. The optimal choice often depends on the number of levels in your grouping variable. For instance, bar or line plots are highly effective for up to five levels, whereas heatmaps are better suited for more complex datasets. +While these are general recommendations, the core data is fully accessible in `bcse_core_multi` should you wish to develop a custom visualization. ```{r fig6_plot_type_bar, echo=TRUE, fig.cap="Figure 6: Occupancy of core ASV/OTUs across the 'Crop' variable. Each bar represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A bar plot won't work here, way too many variable levels."} plot_core_dist_bar <- plot_core_distribution( @@ -370,7 +334,7 @@ print(plot_core_dist_line) -We can reorder the variable levels following logic before plotting. +We can reorder the variable levels with the following logic before plotting. ```{r reorder variable levels, echo=TRUE} bcse_core_multi$metadata <- bcse_core_multi$metadata %>% @@ -430,14 +394,10 @@ print(plot_core_dist_heatmap) Neutral models in ecology assume that species (ASVs/OTUs) are functionally equivalent and that community patterns arise from random chance rather than -deterministic niches or selection. As a specific case of null models [@Gotelli2006Null], -they act as a baseline: if real-world data significantly differs from the model's -predictions, we can infer that non-random ecological forces are at play. +deterministic niches or selection. As a specific case of null models [@Gotelli2006Null], they act as a baseline. If real-world data significantly differ from the model's predictions, we can infer that non-random ecological forces are at play. -Drawing on the influential work of @Sloan2006Quantifying and @Burns2015Contribution, -BRCore implements accessible functions to fit and visualize these models. Our goal -is to make these powerful ecological tools easy to use without losing the -conceptual rigor of the original math. +Building on the work of @Sloan2006Quantifying and @Burns2015Contribution, +BRCore implements accessible functions to fit and visualize these models. Our goal is to make these powerful ecological tools easy to use without losing the conceptual rigor of the original math. To get started, we first fit the neutral model: @@ -449,7 +409,7 @@ bcse_core_multi_neutral_fit <- fit_neutral_model( ) ``` -What we get is +What we get is... ```{r neutral fit result, echo=TRUE} @@ -489,13 +449,20 @@ this stochasticity, if we identify the core using only the first iteration of ou `update_otu_table()`, we obtain the following results: ```{r supplemental_info, echo=TRUE} -bcse_core_multi_iter1 <- identify_core( +bcse_core_single_iter1 <- identify_core( physeq_obj = bcse_updated_rare, priority_var = "Crop", increase_value = 0.02, abundance_weight = 0, seed = 2135 ) + +bcse_identified_core_iter1 <- plot_identified_core(bray_curtis_ranked = bcse_core_single_iter1$bray_curtis_ranked, + elbow = bcse_identified_core_iter1$elbow, + lastCall = bcse_identified_core_iter1$bc_increase, + increase_value = bcse_identified_core_iter1$increase_value +) +print(bcse_identified_core_iter1$plot_idenfied_core) ``` ## Further Customization of `identify_core()` @@ -518,6 +485,8 @@ The `abundance_weight` argument (numeric, 0 to 1) allows you to blend these two * abundance_weight = 1: Ranking is based purely on mean relative abundance. +* max_otus: Limits the number of taxa processed. Useful for large datasets to improve computational efficiency. + * Intermediate Values: A value such as 0.3 creates a hybrid score, weighting occupancy at 70% and abundance at 30%. 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    +Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 3 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the 'Plot' variable, which is a nested variable within 'Crop'. +

    Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 3 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the 'Plot' variable, which is a nested variable within 'Crop'.

    +
    + -Review the rarefaction and variance plots to determine the ideal sampling depth. -You are looking for a "sweet spot" that maintains high diversity and sample sizes +**Review the rarefaction and variance plots to determine the ideal sampling depth.** +You are looking for a "sweet spot" that maintains high diversity and samples while keeping variance across iterations low. Pay extra attention when rarefaction -cutoff is quite low, as show by @Cameron2021Rarefying. Note this depth, as it will -serve as the cutoff for the `identify_core()` step in your pipeline. +cutoff is quite low, as show by @Cameron2021Rarefying. **Note this depth, as it will +serve as the cutoff for the `identify_core()` step in your pipeline.** + + +### Recreate a phyloseq object with a rarefied `otu_table()` + +***This is an optional step.*** -### OPTIONAL: Recreate a phyloseq object with rarefied `otu_table()` +The `identify_core()` function accepts a phyloseq object, whether rarefied or non-rarefied. When supplying a phyloseq object with a non-rarefied `otu_table`, you must provide a list of rarefied OTU tables that result from `multi_rarefy()`. In our case, we generated `bcse_rarefied_list`. -The `identify_core()` function, handles the rarefaction iterations internally, you -do not need to worry about rarefaction. The results from `add_rarefaction_metrics()`, + The results from `add_rarefaction_metrics()`, `plot_rarefaction_metrics()`, and `multi_rarefy()` are meant to explore the data -and inform the chosen parameters for `identify_core()`. You will specify how many -rarefaction iterations you decided to run within the function. +and inform the chosen parameters for `identify_core()`. Function `identify_core()` will calculate the distance matrix for all iterations supplied and rank OTUs based on the average distance across iterations. -If you have a phyloseq object with a rarefied `otu_table()` or if you want to use one -specific rarefaction iteration generated by `multi_rarefy()` you also have -these options. The choice is up to you and depends on your preferences and the -specific goals of your analysis. The `update_otu_table()` function allows for -replacing the `otu_table()` with the rarefied table you prefer. +If you have a phyloseq object with a rarefied `otu_table()` or if you want to use a specific rarefaction iteration generated by `multi_rarefy()`, you also have these options. *The choice is up to you and depends on your preferences and the specific goals of your analysis.* The `update_otu_table()` function allows for replacing the `otu_table()` with the rarefied table you prefer. @@ -266,22 +314,22 @@ bcse_rare_single <- update_otu_table( rarefied_otus = bcse_rarefied_list, iteration = 1 # Speficify which iteration to use for the updated OTU table ) -#> Error: object 'bcse_rarefied_list' not found +#> ℹ Extracting iteration 1 from list of 3 iterations. +#> ! Phyloseq object and rarefied otu_table sample names are NOT identical. Check samples removed by rarefaction below. +#> ! 3 samples removed due to rarefaction: "bcse108, bcse105, bcse110" +#> ℹ Building phyloseq object with 47 samples and 2861 taxa +#> ✔ Update complete! ``` +Let's proceed to the core analysis. # Core analysis ### Identify the core ASV/OTUs set -We can now identify how many core ASV/OTUs are present in the dataset. One -important difference that separates `identify_core()` form other methods of -identification of core taxa is that you can specify a variable (in the case -of the `bcse` dataset is `Crop`) so that OTUs can be core for a level of the -variable but possibly not for others. This comes form the idea that core -microbiome can be transient in space and time as show by @Shade2019Abundance. +We can now identify how many core ASV/OTUs are present in the dataset. One important difference that separates `identify_core()` from other methods of identification of core taxa is that you can specify a variable (in the case of the `bcse` dataset is `Crop`) so that OTUs can be core for a level of the variable but possibly not for others. The idea stems from the assumption that the microbiome can be transient in space and time, as shown by @Shade2019Abundance. -*We continue with the `bcse` phyloseq object and the rarefied data `bcse_rarefied_list` throughout the vignette.* +**We continue with the `bcse` phyloseq object and the rarefied data `bcse_rarefied_list` throughout the vignette.** See below for example using a single iteration. @@ -297,7 +345,13 @@ bcse_core_multi <- identify_core( ) #> Seed used: 2134 #> ✔ Input phyloseq object is valid! -#> Error: object 'bcse_rarefied_list' not found +#> ℹ Using provided `rarefied_list` (3 iterations). +#> ✔ Core prioritizing variable: Crop +#> ℹ Ranked by Rank only +#> ℹ Ranking OTUs based on BC dissimilarity, starting at 2026-04-28 12:10:58.310505 +#> ✔ Elbow method identified 7 core OTUs +#> ✔ % increase method identified 30 core OTUs +#> ✔ Analysis complete! # With a single iteration # bcse_core_single <- identify_core( @@ -310,26 +364,12 @@ bcse_core_multi <- identify_core( ## *How the is the core identified?* -As shown by @Shade2019Abundance, an interesting way explore the abundance and -occupancy inclusion thresholds used to define the core microbiome is to evaluate how -well the resulting core membership captures the overarching patterns of beta -diversity found in the complete dataset. This approach treats the core microbiome -as a representative subset that should preserve the main ecological relationships -and sample-to-sample differences observed when using all taxa. By measuring how -closely the beta diversity patterns calculated from core taxa alone match those -from the full dataset, researchers can objectively assess whether their chosen -thresholds produce a meaningful and informative core that maintains the essential -structural information of the microbial community while reducing complexity. +As shown by @Shade2019Abundance, an interesting way to explore the abundance and occupancy inclusion thresholds used to define the core microbiome is to evaluate how well the resulting core membership captures the overarching patterns of beta-diversity found in the complete dataset. This approach treats the core microbiome as a representative subset that should preserve the main ecological relationships and sample-to-sample differences observed when using all taxa. By measuring how closely the beta-diversity patterns calculated from core taxa alone match those from the full dataset, researchers can objectively assess whether their chosen thresholds yield a meaningful and informative core that retains the essential structural information of the microbial community while reducing complexity. -To identify the optimal point where increasing the core inclusion threshold provides -diminishing returns in explanatory value, we offer two automated methods. +To identify the optimal point where increasing the core inclusion threshold provides diminishing returns in explanatory value, we offer two automated methods. * The first is a more stringent `elbow` approach that finds the bend in the -abundance-occupancy curve where adding more taxa yields progressively smaller -improvements. This method calculates the first-order difference (numerical -differentiation) by assigning a score to each potential cutoff point, splitting -the curve into two parts, and finding the point that maximizes the difference -in average rates of change between these parts. +abundance-occupancy curve where adding more taxa yields progressively smaller improvements. This method calculates the first-order difference (numerical differentiation) by assigning a score to each potential cutoff point, splitting the curve into two parts, and finding the cutoff that maximizes the difference in average rates of change between the two parts. * The second method uses a final percent `increase` threshold in beta-diversity (recommend 2% or more), which continues adding taxa until improvements fall @@ -340,11 +380,7 @@ equation $C = 1 - (BC_{core}/BC_{all})$, where $C$ represents the contribution o ranked taxa to total similarity, $BC_{core}$ is the Bray-Curtis similarity using only core taxa, and $BC_{all}$ uses the complete dataset. -The cumulative explanatory value of adding each next-ranked taxon can be plotted -using the `plot_identified_core()` function, with the vertical lines indicating -the elbow and 2% threshold cutoff methods. These approaches eliminate the need -for subjective threshold setting by automatically identifying natural breakpoints -where additional taxa provide marginal returns in explanatory power. +The cumulative explanatory value of adding each next-ranked taxon can be plotted using the `plot_identified_core()` function, with the vertical lines indicating the elbow and 2% threshold cutoff methods. These approaches eliminate the need for subjective threshold setting by automatically identifying natural breakpoints where additional taxa provide marginal returns in explanatory power. **The output is a list of 13 named items.** @@ -352,7 +388,60 @@ where additional taxa provide marginal returns in explanatory power. ``` r str(bcse_core_multi) -#> Error: object 'bcse_core_multi' not found +#> List of 13 +#> $ bray_curtis_ranked : tibble [2,861 × 12] (S3: tbl_df/tbl/data.frame) +#> ..$ rank : Factor w/ 2861 levels "1","10","100",..: 1 1112 2085 2196 2307 2418 2529 2640 2751 2 ... +#> ..$ rank_num : num [1:2861] 1 2 3 4 5 6 7 8 9 10 ... +#> ..$ otu_added : chr [1:2861] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ... +#> ..$ MeanBC : num [1:2861] 0.0744 0.0862 0.1019 0.1036 0.1264 ... +#> ..$ proportionBC : num [1:2861] 0.12 0.139 0.164 0.167 0.203 ... +#> ..$ IncreaseBC : num [1:2861] NA 1.16 1.18 1.02 1.22 ... +#> ..$ elbow_slope_diffs: num [1:2861] -0.000191 0.005744 0.009008 0.007127 0.010232 ... +#> ..$ delta_pct_max_BC : num [1:2861] NA 15.96 18.22 1.63 22 ... +#> ..$ is_BC_core : logi [1:2861] TRUE TRUE TRUE TRUE TRUE TRUE ... +#> ..$ last_pctBC_cutoff: logi [1:2861] FALSE FALSE FALSE FALSE FALSE FALSE ... +#> ..$ is_elbow_core : logi [1:2861] TRUE TRUE TRUE TRUE TRUE TRUE ... +#> ..$ last_elbow_cutoff: logi [1:2861] FALSE FALSE FALSE FALSE FALSE FALSE ... +#> $ otu_ranked :'data.frame': 2861 obs. of 7 variables: +#> ..$ otu : chr [1:2861] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ... +#> ..$ otu_occ: num [1:2861] 1 1 1 1 1 1 1 1 1 1 ... +#> ..$ otu_rel: num [1:2861] 0.21728 0.01799 0.03296 0.00278 0.05897 ... +#> ..$ sumF : num [1:2861] 10 10 10 10 10 10 10 10 10 10 ... +#> ..$ sumG : num [1:2861] 10 10 10 10 10 10 10 10 10 10 ... +#> ..$ nS : int [1:2861] 10 10 10 10 10 10 10 10 10 10 ... +#> ..$ rank : num [1:2861] 2 2 2 2 2 2 2 2 2 2 ... +#> $ abundance_occupancy:'data.frame': 2861 obs. of 3 variables: +#> ..$ otu : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ... +#> ..$ otu_occ: num [1:2861] 0.08 0.46 0.04 0.74 1 0.32 0.3 0.06 0.96 0.04 ... +#> ..$ otu_rel: num [1:2861] 2.47e-05 5.55e-04 4.90e-06 4.82e-03 2.17e-01 ... +#> $ priority_var : chr "Crop" +#> $ abundance_weight : num 0 +#> $ elbow : int 7 +#> $ bc_increase : int 30 +#> $ increase_value : num 0.02 +#> $ elbow_core : chr [1:7] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ... +#> $ increase_core : chr [1:30] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ... +#> $ otu_table : int [1:2861, 1:50] 0 1 0 0 27777 0 7 2 119 0 ... +#> ..- attr(*, "dimnames")=List of 2 +#> .. ..$ : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ... +#> .. ..$ : chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ... +#> $ metadata :'data.frame': 50 obs. of 4 variables: +#> ..$ Niche : chr [1:50] "Leaf" "Leaf" "Leaf" "Leaf" ... +#> ..$ Crop : chr [1:50] "Corn" "Sorghum" "Switchgrass" "Miscanthus" ... +#> ..$ Plot : chr [1:50] "R2" "R4" "R5" "R5" ... +#> ..$ sample_id: chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ... +#> $ taxonomy :'data.frame': 2861 obs. of 10 variables: +#> ..$ OTU_ID : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ... +#> ..$ Kingdom : chr [1:2861] "Bacteria" "Bacteria" "Bacteria" "Bacteria" ... +#> ..$ Phylum : chr [1:2861] "Myxococcota" "Actinobacteriota" "Proteobacteria" "Proteobacteria" ... +#> ..$ Class : chr [1:2861] "Polyangia" "Actinobacteria" "Alphaproteobacteria" "Gammaproteobacteria" ... +#> ..$ Order : chr [1:2861] "Polyangiales" "Streptomycetales" "Rhizobiales" "Enterobacterales" ... +#> ..$ Family : chr [1:2861] "Polyangiaceae" "Streptomycetaceae" "Rhizobiaceae" "Enterobacteriaceae" ... +#> ..$ Genus : chr [1:2861] "Aetherobacter" "Streptomyces" "Mesorhizobium" NA ... +#> ..$ Species : chr [1:2861] "Uncultured_bacterium_3097" NA "Mesorhizobium_sophorae" NA ... +#> ..$ BestMatch: chr [1:2861] "Uncultured bacterium 3097" "Streptomyces" "Mesorhizobium sophorae" "Enterobacteriaceae" ... +#> ..$ Taxonomy : chr [1:2861] "OTU_427-Uncultured bacterium 3097" "OTU_11-Streptomyces" "OTU_253-Mesorhizobium sophorae" "OTU_148-Enterobacteriaceae" ... +#> - attr(*, "class")= chr [1:2] "identify_core_result" "list" ``` ### Visualize the abundance-occupancy distribution and the core ASV/OTUs set @@ -367,12 +456,17 @@ bcse_identified_core <- plot_identified_core( lastCall = bcse_core_multi$bc_increase, increase_value = bcse_core_multi$increase_value ) -#> Error: object 'bcse_core_multi' not found -print(bcse_identified_core) -#> Error: object 'bcse_identified_core' not found +# plot_identified_core() outputs a list with a dataframe and a ggplot + +print(bcse_identified_core$plot_identified_core) ``` +
    +Figure 3: Percent Bray-Curtis similarity between samples per ranked ASV/OTUs. Number of core ASV/OTUs identified by Elbow and Last 2% increase in Bray-Curtis similarity are shown. +

    Figure 3: Percent Bray-Curtis similarity between samples per ranked ASV/OTUs. Number of core ASV/OTUs identified by Elbow and Last 2% increase in Bray-Curtis similarity are shown.

    +
    + @@ -388,11 +482,14 @@ plot_abund_occ_increase <- plot_abundance_occupancy( core_result = bcse_core_multi, core_set = "increase" ) -#> Error: object 'bcse_core_multi' not found print(plot_abund_occ_increase) -#> Error: object 'plot_abund_occ_increase' not found ``` +
    +Figure 4: Abundance-occupancy distribution for the 'bcse' dataset. The core ASV/OTUs identified by the last 2% increase method are highlighted in red. +

    Figure 4: Abundance-occupancy distribution for the 'bcse' dataset. The core ASV/OTUs identified by the last 2% increase method are highlighted in red.

    +
    + And for the `elbow` method. @@ -402,24 +499,23 @@ plot_abund_occ_elbow <- plot_abundance_occupancy( core_result = bcse_core_multi, core_set = "elbow" ) -#> Error: object 'bcse_core_multi' not found plot_abund_occ_elbow + scale_fill_manual(values = c("darkgreen", "grey")) -#> Error: object 'plot_abund_occ_elbow' not found +#> Scale for fill is already present. +#> Adding another scale for fill, which will replace the existing scale. ``` +
    +Figure 5: Abundance-occupancy distribution for the 'bcse' dataset. The core ASV/OTUs identified by the elbow method are highlighted in dark green. +

    Figure 5: Abundance-occupancy distribution for the 'bcse' dataset. The core ASV/OTUs identified by the elbow method are highlighted in dark green.

    +
    + ### Visualize the core set across the variable used for detecting the core -The `plot_core_distribution()` it can be used to visualize ASV/OTUs occupancy across -the variable specified during the identification of the core. The function provides -three visualization methods—bar, line, and heatmap—which can be toggled using the -plot_type argument. The optimal choice often depends on the number of levels in -your grouping variable. For instance, bar or line plots are highly effective for -up to five levels, whereas heatmaps are better suited for more complex datasets. -While these are general recommendations, the core data is fully accessible in -`bcse_core_multi` should you wish to develop a custom visualization. +The `plot_core_distribution()` function can be used to visualize ASV/OTU occupancy across the variable specified during core identification. The function provides three visualization methods (bar, line, and heatmap) that can be toggled via the plot_type argument. The optimal choice often depends on the number of levels in your grouping variable. For instance, bar or line plots are highly effective for up to five levels, whereas heatmaps are better suited for more complex datasets. +While these are general recommendations, the core data is fully accessible in `bcse_core_multi` should you wish to develop a custom visualization. ``` r @@ -429,12 +525,15 @@ plot_core_dist_bar <- plot_core_distribution( group_var = "Crop", plot_type = "bar" ) -#> Error: object 'bcse_core_multi' not found print(plot_core_dist_bar) -#> Error: object 'plot_core_dist_bar' not found ``` +
    +Figure 6: Occupancy of core ASV/OTUs across the 'Crop' variable. Each bar represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A bar plot won't work here, way too many variable levels. +

    Figure 6: Occupancy of core ASV/OTUs across the 'Crop' variable. Each bar represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A bar plot won't work here, way too many variable levels.

    +
    + @@ -447,14 +546,17 @@ plot_core_dist_line <- plot_core_distribution( group_var = "Crop", plot_type = "line" ) -#> Error: object 'bcse_core_multi' not found print(plot_core_dist_line) -#> Error: object 'plot_core_dist_line' not found ``` +
    +Figure 7: Occupancy of core ASV/OTUs across the 'Crop' variable. Each point represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A line plot is better than a bar plot but still not ideal for this many variable levels. +

    Figure 7: Occupancy of core ASV/OTUs across the 'Crop' variable. Each point represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A line plot is better than a bar plot but still not ideal for this many variable levels.

    +
    + -We can reorder the variable levels following logic before plotting. +We can reorder the variable levels with the following logic before plotting. ``` r @@ -489,7 +591,6 @@ bcse_core_multi$metadata <- bcse_core_multi$metadata %>% ) ) ) -#> Error: object 'bcse_core_multi' not found ``` @@ -505,26 +606,27 @@ plot_core_dist_heatmap <- plot_core_distribution( plot_type = "heatmap" ) + viridis::scale_fill_viridis(option = "plasma", name = "Occupancy") -#> Error: object 'bcse_core_multi' not found +#> Scale for fill is already present. +#> Adding another scale for fill, which will replace the existing scale. print(plot_core_dist_heatmap) -#> Error: object 'plot_core_dist_heatmap' not found ``` +
    +Figure 8: Occupancy of core ASV/OTUs across the 'Crop' variable. Each cell represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A heatmap is more compact and shows well enough the average occupancy across samples in each variable level. +

    Figure 8: Occupancy of core ASV/OTUs across the 'Crop' variable. Each cell represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A heatmap is more compact and shows well enough the average occupancy across samples in each variable level.

    +
    + # Fitting Neutral Model Neutral models in ecology assume that species (ASVs/OTUs) are functionally equivalent and that community patterns arise from random chance rather than -deterministic niches or selection. As a specific case of null models [@Gotelli2006Null], -they act as a baseline: if real-world data significantly differs from the model's -predictions, we can infer that non-random ecological forces are at play. +deterministic niches or selection. As a specific case of null models [@Gotelli2006Null], they act as a baseline. If real-world data significantly differ from the model's predictions, we can infer that non-random ecological forces are at play. -Drawing on the influential work of @Sloan2006Quantifying and @Burns2015Contribution, -BRCore implements accessible functions to fit and visualize these models. Our goal -is to make these powerful ecological tools easy to use without losing the -conceptual rigor of the original math. +Building on the work of @Sloan2006Quantifying and @Burns2015Contribution, +BRCore implements accessible functions to fit and visualize these models. Our goal is to make these powerful ecological tools easy to use without losing the conceptual rigor of the original math. To get started, we first fit the neutral model: @@ -535,16 +637,63 @@ bcse_core_multi_neutral_fit <- fit_neutral_model( core_set = bcse_core_multi$increase_core, abundance_occupancy = bcse_core_multi$abundance_occupancy ) -#> Error: object 'bcse_core_multi' not found +#> Waiting for profiling to be done... +#> ℹ Neutral model fitting: +#> • Average individuals per community (N): 40276.38 +#> • Binomial model using rounded N: 40276 +#> • Poisson model using N: 40276.38 +#> • Maximum likelihood estimation using N: 40276.38, and starting parameters: mu = 0, sigma = 0.1 +#> ✔ Model fitting complete! ``` -What we get is +What we get is... ``` r str(bcse_core_multi_neutral_fit) -#> Error: object 'bcse_core_multi_neutral_fit' not found +#> List of 2 +#> $ goodness_of_fit :'data.frame': 1 obs. of 24 variables: +#> ..$ m : num 0.0205 +#> ..$ m.ci : num 0.00186 +#> ..$ m.mle : num 0.0205 +#> ..$ maxLL : num -2279 +#> ..$ binoLL : num -720 +#> ..$ poisLL : num -720 +#> ..$ Rsqr : num 0.528 +#> ..$ Rsqr.bino : num -0.833 +#> ..$ Rsqr.pois : num -0.833 +#> ..$ RMSE : num 0.109 +#> ..$ RMSE.bino : num 0.215 +#> ..$ RMSE.pois : num 0.215 +#> ..$ AIC : num -4554 +#> ..$ BIC : num -4542 +#> ..$ AIC.bino : num -1435 +#> ..$ BIC.bino : num -1423 +#> ..$ AIC.pois : num -1435 +#> ..$ BIC.pois : num -1423 +#> ..$ N : num 40276 +#> ..$ Samples : num 50 +#> ..$ Richness : num 2861 +#> ..$ Detect : num 2.48e-05 +#> ..$ above.pred: num 0.165 +#> ..$ below.pred: num 0.035 +#> $ model_prediction:'data.frame': 2861 obs. of 14 variables: +#> ..$ otu : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ... +#> ..$ otu_occ : num [1:2861] 0.08 0.46 0.04 0.74 1 0.32 0.3 0.06 0.96 0.04 ... +#> ..$ otu_rel : num [1:2861] 2.47e-05 5.55e-04 4.90e-06 4.82e-03 2.17e-01 ... +#> ..$ membership: chr [1:2861] "Not core" "Not core" "Not core" "Not core" ... +#> ..$ p : num [1:2861] 3.48e-06 6.31e-05 2.48e-06 7.98e-03 2.54e-01 ... +#> ..$ freq : num [1:2861] 0.08 0.46 0.04 0.74 1 0.32 0.3 0.06 0.96 0.04 ... +#> ..$ freq.pred : num [1:2861] 0.00951 0.16077 0.0068 1 1 ... +#> ..$ pred.lwr : num [1:2861] 0.000953 0.083916 0.000514 0.928652 0.928652 ... +#> ..$ pred.upr : num [1:2861] 0.088 0.286 0.0835 1 1 ... +#> ..$ bino.pred : num [1:2861] 0.1306 0.9211 0.0952 1 1 ... +#> ..$ bino.lwr : num [1:2861] 0.0632 0.8131 0.0405 0.9287 0.9287 ... +#> ..$ bino.upr : num [1:2861] 0.251 0.969 0.208 1 1 ... +#> ..$ y : chr [1:2861] NA NA NA NA ... +#> ..$ fit_class : chr [1:2861] "As predicted" "Above prediction" "As predicted" "Below prediction" ... +#> - attr(*, "class")= chr [1:2] "fit_neutral_model" "list" ``` ### Plot the neutral model and the core set @@ -552,11 +701,14 @@ str(bcse_core_multi_neutral_fit) ``` r plot_bcse_neutral_fit <- plot_neutral_model(bcse_core_multi_neutral_fit) -#> Error: object 'bcse_core_multi_neutral_fit' not found print(plot_bcse_neutral_fit) -#> Error: object 'plot_bcse_neutral_fit' not found ``` + +
    +Figure 9: Neutral model fit illustrates the neutral model of abundance-occupancy distributions for the 'bcse' dataset. The $R^2$ value represents a standard coefficient of determination, calculated as $R^2 = 1 - \frac{SS_{err}}{SS_{total}}$, providing a measure of goodness of fit. The immigration parameter ($m$) estimates the probability that an individual in a local sample originates from the metacommunity rather than from local dispersal. We also observe that the neutral model provides a poor fit; the $R^2$ is notably low, and the majority of taxa (including both core and non-core ASV/OTUs) fall above the model's prediction line. Furthermore, the estimated immigration parameter ($m = 0.63$) suggests that the local community is dominated by strong dispersal and mixing from the metacommunity. +

    Figure 9: Neutral model fit illustrates the neutral model of abundance-occupancy distributions for the 'bcse' dataset. The $R^2$ value represents a standard coefficient of determination, calculated as $R^2 = 1 - \frac{SS_{err}}{SS_{total}}$, providing a measure of goodness of fit. The immigration parameter ($m$) estimates the probability that an individual in a local sample originates from the metacommunity rather than from local dispersal. We also observe that the neutral model provides a poor fit; the $R^2$ is notably low, and the majority of taxa (including both core and non-core ASV/OTUs) fall above the model's prediction line. Furthermore, the estimated immigration parameter ($m = 0.63$) suggests that the local community is dominated by strong dispersal and mixing from the metacommunity.

    +
    @@ -584,7 +736,7 @@ this stochasticity, if we identify the core using only the first iteration of ou ``` r -bcse_core_multi_iter1 <- identify_core( +bcse_core_single_iter1 <- identify_core( physeq_obj = bcse_updated_rare, priority_var = "Crop", increase_value = 0.02, @@ -593,6 +745,15 @@ bcse_core_multi_iter1 <- identify_core( ) #> Seed used: 2135 #> Error: object 'bcse_updated_rare' not found + +bcse_identified_core_iter1 <- plot_identified_core(bray_curtis_ranked = bcse_core_single_iter1$bray_curtis_ranked, + elbow = bcse_identified_core_iter1$elbow, + lastCall = bcse_identified_core_iter1$bc_increase, + increase_value = bcse_identified_core_iter1$increase_value +) +#> Error: object 'bcse_core_single_iter1' not found +print(bcse_identified_core_iter1$plot_idenfied_core) +#> Error: object 'bcse_identified_core_iter1' not found ``` ## Further Customization of `identify_core()` @@ -615,6 +776,8 @@ The `abundance_weight` argument (numeric, 0 to 1) allows you to blend these two * abundance_weight = 1: Ranking is based purely on mean relative abundance. +* max_otus: Limits the number of taxa processed. Useful for large datasets to improve computational efficiency. + * Intermediate Values: A value such as 0.3 creates a hybrid score, weighting occupancy at 70% and abundance at 30%. 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a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,23 @@ -## R CMD check results (v2.0.1 resubmission) +## Resubmission notes (v2.0.2) + +This is a resubmission. Previous submissions: + +- v2.0.0: Rejected by CRAN auto-check service +- v2.0.1: Passed auto-checks; retracted by maintainer due to typos + +The following issues from v2.0.0 were resolved: + +1. **URL in DESCRIPTION**: Changed `http://` to `https://` for the package + website URL. +2. **Description field**: Reworded to not start with "This package" per CRAN + policy. +3. **Possibly misspelled words** (`BRCore`, `Bioenergy`, `microbiome`, and `microbiomes`): These are intentional. `BRCore` is the package name and `Bioenergy` , `microbiome`, and `microbiomes` are domain-specific terms. +4. **Invalid file URIs in README.md**: Relative links to `CONTRIBUTING.md` and + `CODE_OF_CONDUCT.md` replaced with full GitHub URLs. + + + +### R CMD check results 0 errors | 0 warnings | 2 notes @@ -14,22 +33,7 @@ environment, skipping math rendering check. This is a local environment issue. -### Resubmission notes (v2.0.1) - -This is a resubmission of v2.0.0, which was rejected by the CRAN auto-check -service (win-builder, r-devel-windows-x86_64). The following issues were -identified and resolved: - -1. **URL in DESCRIPTION**: Changed `http://` to `https://` for the package - website URL. - -2. **Description field**: Reworded to not start with "This package" per CRAN - policy. - -3. **Possibly misspelled words** (`BRCore`, `Bioenergy`, `microbiome`, and `microbiomes`): These are intentional. `BRCore` is the package name and `Bioenergy` , `microbiome`, and `microbiomes` are domain-specific terms. - -4. **Invalid file URIs in README.md**: Relative links to `CONTRIBUTING.md` and - `CODE_OF_CONDUCT.md` replaced with full GitHub URLs. +--- ### win-builder notes @@ -39,5 +43,6 @@ declared imports (`phyloseq`, `Hmisc`, `knitr`) that were absent in the win-builder guest environment. The CRAN incoming checks on both Windows and Debian passed with only NOTEs, confirming these are not direct imports in package code. + + -* This is a new submission to CRAN. From 4871eebe9c78032618aedb6d3f6b49a0c8a21cce Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Tue, 28 Apr 2026 15:51:32 -0500 Subject: [PATCH 04/37] Increment version number to 2.0.2 --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3008363..c276037 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: BRCore Title: Unified Framework for Identification and Ecological Interpretation of Microbial Data from Bioenergy Research Centers -Version: 2.0.1 +Version: 2.0.2 Authors@R: c( person("Bolívar", "Aponte Rolón", email = "bolaponte@pm.me", From 1daeec40b3c0777986e34c67e90a7759678e9910 Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Wed, 29 Apr 2026 14:20:31 -0500 Subject: [PATCH 05/37] docs: expand package description with methodology references and citations --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index c276037..980dbf6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -27,7 +27,7 @@ Authors@R: c( person("Great Lakes Bioenergy Research Center", role = c("cph", "fnd")) ) -Description: A unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. +Description: A unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. BRCore implements the workflow proposed by Shade and Stopnisek (2019) and incorporates additional rarefaction steps. The proposed workflow aims to identify persistent microbiomes using abundance-occupancy distributions and neutral community model fitting. For more details on abundance-occupancy distributions see Shade A, Stopnisek N (2019) , for neutral models, see Sloan et al. (2006) and Burns et al. (2015) . License: MIT + file LICENSE Encoding: UTF-8 Language: en-US From 2de5f48a06d5d907ea1df6eece915029c272664d Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Wed, 29 Apr 2026 14:46:18 -0500 Subject: [PATCH 06/37] docs: improve documentation formatting and examples across neutral model functions --- R/fit_neutral_model.R | 13 +++--- R/plot_neutral_model.R | 24 +++++++--- R/sncm.fit.R | 95 +++++++++++++++++++++----------------- man/fit_neutral_model.Rd | 13 +++--- man/plot_neutral_model.Rd | 24 +++++++--- man/sncm.fit.Rd | 96 ++++++++++++++++++++++----------------- 6 files changed, 155 insertions(+), 110 deletions(-) diff --git a/R/fit_neutral_model.R b/R/fit_neutral_model.R index 32aac9f..bc8daa9 100644 --- a/R/fit_neutral_model.R +++ b/R/fit_neutral_model.R @@ -1,7 +1,7 @@ #' Fit a Neutral Model to Microbial Community Data #' #' The function fits the neutral distribution model developed by Sloan et al. -#' 2006, and implemented in R by Burns et al. 2015, to an OTU/ASV table and +#' 2006, and implemented in R by Burns et al. (2015), to an OTU/ASV table and #' returns several goodness of fit statistics alongside a data.frame #' with predicted occurrence frequencies for each OTU/ASV based on their #' abundance in the metacommunity for plotting the abundance-occupancy @@ -28,16 +28,16 @@ #' Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. (2006) Quantifying #' the roles of immigration and chance in shaping prokaryote community #' structure. Environ Microbiol. 8(4):732-40. -#' doi: https://doi.org/10.1111/j.1462-2920.2005.00956.x +#' #' #' Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, Bohannan #' BJ. (2015) Contribution of neutral processes to the assembly of gut microbial #' communities in the zebrafish over host development. ISME J. -#' 10(3):655-64. doi: https://doi.org/10.1038/ismej.2015.142 +#' 10(3):655-64. #' #' Shade A, Stopnisek N (2019) Abundance-occupancy distributions to prioritize #' plant core microbiome membership. Current Opinion in Microbiology, 49:50-58 -#' doi: https://doi.org/10.1016/j.mib.2019.09.008 +#' #' #' @seealso \code{\link{plot_neutral_model}} #' @@ -45,8 +45,6 @@ #' \donttest{ #' library(phyloseq) #' library(BRCore) -#' # Example using your switchgrass phyloseq object and grouping variable -#' # 'sampling_date' #' data("switchgrass", package = "BRCore") #' #' switchgrass_core <- identify_core( @@ -64,7 +62,8 @@ #' #' str(switchgrass_core_fit) #' switchgrass_core_fit$goodness_of_fit -#' switchgrass_core_fit$model_prediction |> head() +#' switchgrass_core_fit$model_prediction |> +#' head() #' } #' #' @importFrom stats confint pbeta pbinom ppois dnorm AIC BIC coef diff --git a/R/plot_neutral_model.R b/R/plot_neutral_model.R index 3405d61..46dda94 100644 --- a/R/plot_neutral_model.R +++ b/R/plot_neutral_model.R @@ -1,8 +1,9 @@ #' Plot a fitted Neutral Model to Microbial Community Data #' #' This function plots ASV/OTUs log abundances into a fitted neutral model of -#' microbial abundance-occupancy distribution. ASV/OTUs that are *Core*, *As predicted* (i.e. Neutral), -#' *Below* and *Above* model predictions are drawn with distinct point colors, see details. +#' microbial abundance-occupancy distribution. ASV/OTUs that are *Core*, *As +#' predicted* (i.e. Neutral), *Below* and *Above* model predictions are drawn +#' with distinct point colors, see details. #' #' @param fit_result A list-like object returned by \link[=fit_neutral_model]{fit_neutral_model()}. #' @@ -32,10 +33,21 @@ #' predictions and metrics. #' #' @examples -#' \dontrun{ -#' # Assuming you have run a neutral model fit on the switchgrass data.frames -#' is.data.frame(switchgrass_core_fit$model_prediction) -#' is.data.frame(switchgrass_core_fit$goodness_of_fit) +#' \donttest{ +#' data("switchgrass", package = "BRCore") +#' +#' switchgrass_core <- identify_core( +#' physeq_obj = switchgrass, +#' priority_var = "sampling_date", +#' increase_value = 0.02, +#' seed = 092825 +#' ) +#' +#' switchgrass_core_fit <- fit_neutral_model( +#' otu_table = switchgrass_core$otu_table, +#' core_set = switchgrass_core$increase_core, +#' abundance_occupancy = switchgrass_core$abundance_occupancy +#' ) #' #' p <- plot_neutral_model(switchgrass_core_fit) #' print(p) diff --git a/R/sncm.fit.R b/R/sncm.fit.R index 778ed1f..d04e0aa 100644 --- a/R/sncm.fit.R +++ b/R/sncm.fit.R @@ -1,60 +1,71 @@ #' Fit Sloan Neutral Community Model (SNCM) #' -#' This function fits the Sloan Neutral Community Model to community data to assess -#' neutral processes in community assembly. It compares the neutral model against -#' binomial and Poisson null models using various statistical measures. +#' The function implements the Sloan Neutral Community Model which +#' predicts the occurrence frequency of taxa based on their relative abundance +#' in the source pool and a migration parameter (\code{m}). It compares the +#' neutral model against binomial and Poisson null models using various +#' statistical measures. #' -#' @param spp A matrix or data frame where rows represent communities (samples) and -#' columns represent species/taxa. Values should be abundances or counts. +#' @param spp A matrix or data frame where rows represent communities (samples) +#' and columns represent species/taxa. Values should be abundances or counts. #' @param pool Optional. A matrix or data frame representing the source pool for -#' calculating relative abundances. If NULL, the source pool is calculated from -#' the spp matrix. Default is NULL. -#' @param stats Logical. If TRUE, returns fit statistics including AIC, BIC, R-squared, -#' and RMSE for model comparison. If FALSE, returns predicted vs observed frequencies -#' with confidence intervals. Default is TRUE. -#' @param taxon Optional. A data frame containing taxonomic information to merge with -#' results when stats=FALSE. Should have row names matching species names. Default is NULL. +#' calculating relative abundances. If NULL, the source pool is calculated +#' from the spp matrix. Default is \code{NULL}. +#' @param stats Logical. If \code{TRUE}, returns fit statistics including AIC, BIC, +#' R-squared, and RMSE for model comparison. If \code{FALSE}, returns predicted vs. +#' observed frequencies with confidence intervals. Default is \code{TRUE}. +#' @param taxon Optional. A data frame containing taxonomic information to +#' merge with results when \code{stats = FALSE}. Should have row names matching species +#' names. Default is \code{NULL}. #' -#' @return If stats=TRUE, returns a data frame with model fit statistics including: +#' @return If \code{stats = TRUE}, returns a data frame with model fit statistics including: #' \itemize{ -#' \item m: Migration rate parameter from NLS fit -#' \item m.ci: Confidence interval for m parameter -#' \item m.mle: Migration rate from maximum likelihood estimation -#' \item maxLL, binoLL, poisLL: Log-likelihood values for SNCM, binomial, and Poisson models -#' \item Rsqr, Rsqr.bino, Rsqr.pois: R-squared values for each model -#' \item RMSE, RMSE.bino, RMSE.pois: Root mean squared error for each model -#' \item AIC, BIC: Information criteria for model selection -#' \item N: Average number of individuals per community -#' \item Samples: Number of samples/communities -#' \item Richness: Number of taxa analyzed -#' \item Detect: Detection limit (1/N) +#' \item \code{m}: Migration rate parameter from NLS fit +#' \item \code{m.ci}: Confidence interval for m parameter +#' \item \code{m.mle}: Migration rate from maximum likelihood estimation +#' \item \code{maxLL}, \code{binoLL}, \code{poisLL}: Log-likelihood values for SNCM, binomial, and Poisson models +#' \item \code{Rsqr}, \code{Rsqr.bino}, \code{Rsqr.pois}: R-squared values for each model +#' \item \code{RMSE}, \code{RMSE.bino}, \code{RMSE.pois}: Root mean squared error for each model +#' \item \code{AIC}, \code{BIC}: Information criteria for model selection +#' \item \code{N}: Average number of individuals per community +#' \item \code{Samples}: Number of samples/communities +#' \item \code{Richness}: Number of taxa analyzed +#' \item \code{Detect}: Detection limit (1/N) #' } -#' If stats=FALSE, returns a data frame with observed and predicted frequencies -#' along with confidence intervals for visualization. +#' If \code{stats = FALSE}, returns a data frame with observed and predicted +#' frequencies along with confidence intervals for visualization. #' -#' @details The function implements the Sloan Neutral Community Model which predicts -#' the occurrence frequency of taxa based on their relative abundance in the source -#' pool and a migration parameter (m). The model assumes neutral processes govern -#' community assembly. Three models are compared: SNCM (beta distribution), -#' binomial null model, and Poisson null model. +#' @details The model assumes neutral processes govern community assembly. Three models are compared: SNCM (beta distribution), binomial null model, and Poisson null model. #' #' @references -#' Sloan, W.T. et al. (2006) Quantifying the roles of immigration and chance in -#' shaping prokaryote community structure. Environmental Microbiology, 8, 732-740. +#' Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. (2006) Quantifying +#' the roles of immigration and chance in shaping prokaryote community +#' structure. Environ Microbiol. 8(4):732-40. +#' #' #' @examples -#' \dontrun{ -#' # Generate example community data -#' spp_data <- matrix(rpois(100 * 50, 5), nrow = 100, ncol = 50) -#' colnames(spp_data) <- paste0("Species_", 1:50) +#' # Generate community data +#' set.seed(42) +#' n_samples <- 100 +#' n_species <- 80 #' -#' # Fit SNCM and get statistics -#' fit_stats <- sncm.fit(spp_data, stats = TRUE) -#' print(fit_stats) +#' # Log-normal abundances +#' mean_abund <- rlnorm(n_species, meanlog = 2, sdlog = 1.5) +#' # Simulate community matrix with multinomial sampling +#' spp_data <- t(sapply(seq_len(n_samples), function(i) { +#' rmultinom( +#' 1, +#' size = sample(500:2000, 1), +#' prob = mean_abund / sum(mean_abund) +#' )[, 1] +#'})) +#' colnames(spp_data) <- paste0("Species_", seq_len(n_species)) #' -#' # Get predictions for plotting +#' fit_stats <- sncm.fit(spp_data, stats = TRUE) #' predictions <- sncm.fit(spp_data, stats = FALSE) -#' } +#' +#' +#' @seealso \code{\link{plot_neutral_model}}, \code{\link{fit_neutral_model}} #' #' @importFrom minpack.lm nlsLM #' @importFrom Hmisc binconf diff --git a/man/fit_neutral_model.Rd b/man/fit_neutral_model.Rd index 462fd56..2881961 100644 --- a/man/fit_neutral_model.Rd +++ b/man/fit_neutral_model.Rd @@ -28,7 +28,7 @@ joined to per-taxon predictions } \description{ The function fits the neutral distribution model developed by Sloan et al. -2006, and implemented in R by Burns et al. 2015, to an OTU/ASV table and +2006, and implemented in R by Burns et al. (2015), to an OTU/ASV table and returns several goodness of fit statistics alongside a data.frame with predicted occurrence frequencies for each OTU/ASV based on their abundance in the metacommunity for plotting the abundance-occupancy @@ -41,8 +41,6 @@ below (i.e. interpreted as dispersally limited) the model predictions. \donttest{ library(phyloseq) library(BRCore) -# Example using your switchgrass phyloseq object and grouping variable -# 'sampling_date' data("switchgrass", package = "BRCore") switchgrass_core <- identify_core( @@ -60,7 +58,8 @@ switchgrass_core_fit <- fit_neutral_model( str(switchgrass_core_fit) switchgrass_core_fit$goodness_of_fit -switchgrass_core_fit$model_prediction |> head() +switchgrass_core_fit$model_prediction |> +head() } } @@ -68,16 +67,16 @@ switchgrass_core_fit$model_prediction |> head() Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. (2006) Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol. 8(4):732-40. -doi: https://doi.org/10.1111/j.1462-2920.2005.00956.x +\url{https://doi.org/10.1111/j.1462-2920.2005.00956.x} Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, Bohannan BJ. (2015) Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. -10(3):655-64. doi: https://doi.org/10.1038/ismej.2015.142 +10(3):655-64. \url{https://doi.org/10.1038/ismej.2015.142} Shade A, Stopnisek N (2019) Abundance-occupancy distributions to prioritize plant core microbiome membership. Current Opinion in Microbiology, 49:50-58 -doi: https://doi.org/10.1016/j.mib.2019.09.008 +\url{https://doi.org/10.1016/j.mib.2019.09.008} } \seealso{ \code{\link{plot_neutral_model}} diff --git a/man/plot_neutral_model.Rd b/man/plot_neutral_model.Rd index 8581c78..17b3d14 100644 --- a/man/plot_neutral_model.Rd +++ b/man/plot_neutral_model.Rd @@ -27,8 +27,9 @@ predictions and metrics. } \description{ This function plots ASV/OTUs log abundances into a fitted neutral model of -microbial abundance-occupancy distribution. ASV/OTUs that are \emph{Core}, \emph{As predicted} (i.e. Neutral), -\emph{Below} and \emph{Above} model predictions are drawn with distinct point colors, see details. +microbial abundance-occupancy distribution. ASV/OTUs that are \emph{Core}, \emph{As +predicted} (i.e. Neutral), \emph{Below} and \emph{Above} model predictions are drawn +with distinct point colors, see details. } \details{ Points are split into four groups for display: @@ -40,10 +41,21 @@ Points are split into four groups for display: } } \examples{ -\dontrun{ -# Assuming you have run a neutral model fit on the switchgrass data.frames -is.data.frame(switchgrass_core_fit$model_prediction) -is.data.frame(switchgrass_core_fit$goodness_of_fit) +\donttest{ +data("switchgrass", package = "BRCore") + +switchgrass_core <- identify_core( + physeq_obj = switchgrass, + priority_var = "sampling_date", + increase_value = 0.02, + seed = 092825 +) + +switchgrass_core_fit <- fit_neutral_model( + otu_table = switchgrass_core$otu_table, + core_set = switchgrass_core$increase_core, + abundance_occupancy = switchgrass_core$abundance_occupancy +) p <- plot_neutral_model(switchgrass_core_fit) print(p) diff --git a/man/sncm.fit.Rd b/man/sncm.fit.Rd index f540100..3e5a665 100644 --- a/man/sncm.fit.Rd +++ b/man/sncm.fit.Rd @@ -7,66 +7,78 @@ sncm.fit(spp, pool = NULL, stats = TRUE, taxon = NULL) } \arguments{ -\item{spp}{A matrix or data frame where rows represent communities (samples) and -columns represent species/taxa. Values should be abundances or counts.} +\item{spp}{A matrix or data frame where rows represent communities (samples) +and columns represent species/taxa. Values should be abundances or counts.} \item{pool}{Optional. A matrix or data frame representing the source pool for -calculating relative abundances. If NULL, the source pool is calculated from -the spp matrix. Default is NULL.} +calculating relative abundances. If NULL, the source pool is calculated +from the spp matrix. Default is \code{NULL}.} -\item{stats}{Logical. If TRUE, returns fit statistics including AIC, BIC, R-squared, -and RMSE for model comparison. If FALSE, returns predicted vs observed frequencies -with confidence intervals. Default is TRUE.} +\item{stats}{Logical. If \code{TRUE}, returns fit statistics including AIC, BIC, +R-squared, and RMSE for model comparison. If \code{FALSE}, returns predicted vs. +observed frequencies with confidence intervals. Default is \code{TRUE}.} -\item{taxon}{Optional. A data frame containing taxonomic information to merge with -results when stats=FALSE. Should have row names matching species names. Default is NULL.} +\item{taxon}{Optional. A data frame containing taxonomic information to +merge with results when \code{stats = FALSE}. Should have row names matching species +names. Default is \code{NULL}.} } \value{ -If stats=TRUE, returns a data frame with model fit statistics including: +If \code{stats = TRUE}, returns a data frame with model fit statistics including: \itemize{ -\item m: Migration rate parameter from NLS fit -\item m.ci: Confidence interval for m parameter -\item m.mle: Migration rate from maximum likelihood estimation -\item maxLL, binoLL, poisLL: Log-likelihood values for SNCM, binomial, and Poisson models -\item Rsqr, Rsqr.bino, Rsqr.pois: R-squared values for each model -\item RMSE, RMSE.bino, RMSE.pois: Root mean squared error for each model -\item AIC, BIC: Information criteria for model selection -\item N: Average number of individuals per community -\item Samples: Number of samples/communities -\item Richness: Number of taxa analyzed -\item Detect: Detection limit (1/N) +\item \code{m}: Migration rate parameter from NLS fit +\item \code{m.ci}: Confidence interval for m parameter +\item \code{m.mle}: Migration rate from maximum likelihood estimation +\item \code{maxLL}, \code{binoLL}, \code{poisLL}: Log-likelihood values for SNCM, binomial, and Poisson models +\item \code{Rsqr}, \code{Rsqr.bino}, \code{Rsqr.pois}: R-squared values for each model +\item \code{RMSE}, \code{RMSE.bino}, \code{RMSE.pois}: Root mean squared error for each model +\item \code{AIC}, \code{BIC}: Information criteria for model selection +\item \code{N}: Average number of individuals per community +\item \code{Samples}: Number of samples/communities +\item \code{Richness}: Number of taxa analyzed +\item \code{Detect}: Detection limit (1/N) } -If stats=FALSE, returns a data frame with observed and predicted frequencies -along with confidence intervals for visualization. +If \code{stats = FALSE}, returns a data frame with observed and predicted +frequencies along with confidence intervals for visualization. } \description{ -This function fits the Sloan Neutral Community Model to community data to assess -neutral processes in community assembly. It compares the neutral model against -binomial and Poisson null models using various statistical measures. +The function implements the Sloan Neutral Community Model which +predicts the occurrence frequency of taxa based on their relative abundance +in the source pool and a migration parameter (\code{m}). It compares the +neutral model against binomial and Poisson null models using various +statistical measures. } \details{ -The function implements the Sloan Neutral Community Model which predicts -the occurrence frequency of taxa based on their relative abundance in the source -pool and a migration parameter (m). The model assumes neutral processes govern -community assembly. Three models are compared: SNCM (beta distribution), -binomial null model, and Poisson null model. +The model assumes neutral processes govern community assembly. Three models are compared: SNCM (beta distribution), binomial null model, and Poisson null model. } \examples{ -\dontrun{ -# Generate example community data -spp_data <- matrix(rpois(100 * 50, 5), nrow = 100, ncol = 50) -colnames(spp_data) <- paste0("Species_", 1:50) +# Generate community data +set.seed(42) +n_samples <- 100 +n_species <- 80 -# Fit SNCM and get statistics -fit_stats <- sncm.fit(spp_data, stats = TRUE) -print(fit_stats) +# Log-normal abundances +mean_abund <- rlnorm(n_species, meanlog = 2, sdlog = 1.5) +# Simulate community matrix with multinomial sampling +spp_data <- t(sapply(seq_len(n_samples), function(i) { + rmultinom( + 1, + size = sample(500:2000, 1), + prob = mean_abund / sum(mean_abund) + )[, 1] +})) +colnames(spp_data) <- paste0("Species_", seq_len(n_species)) -# Get predictions for plotting +fit_stats <- sncm.fit(spp_data, stats = TRUE) predictions <- sncm.fit(spp_data, stats = FALSE) -} + } \references{ -Sloan, W.T. et al. (2006) Quantifying the roles of immigration and chance in -shaping prokaryote community structure. Environmental Microbiology, 8, 732-740. +Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. (2006) Quantifying +the roles of immigration and chance in shaping prokaryote community +structure. Environ Microbiol. 8(4):732-40. +\url{https://doi.org/10.1111/j.1462-2920.2005.00956.x} +} +\seealso{ +\code{\link{plot_neutral_model}}, \code{\link{fit_neutral_model}} } From a12cd00ca63bbbfcb8bd072468ff2ec54abf8e66 Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Thu, 30 Apr 2026 10:17:06 -0500 Subject: [PATCH 07/37] docs: improve examples, add `seealso` links, and remove \donttest wrappers --- R/add_rarefaction_metrics.R | 25 +++++++++------- R/brcore_theme.R | 2 -- R/fit_neutral_model.R | 4 +-- R/identify_core.R | 49 +++++++++++++++++++++++--------- R/multi_rarefy.R | 32 +++++++++++---------- R/plot_abundance_occupancy.R | 3 +- R/plot_core_distribution.R | 4 ++- R/plot_identified_core.R | 13 +++------ R/plot_rarefaction_metrics.R | 5 ++-- R/plot_variance_propagation.R | 24 +++++++++------- R/update_otu_table.R | 19 ++++++------- man/add_rarefaction_metrics.Rd | 26 ++++++++++------- man/fit_neutral_model.Rd | 3 +- man/identify_core.Rd | 31 +++++++++++++++++--- man/multi_rarefy.Rd | 16 +++++++---- man/plot_abundance_occupancy.Rd | 3 +- man/plot_core_distribution.Rd | 5 +++- man/plot_identified_core.Rd | 1 - man/plot_rarefaction_metrics.Rd | 5 ++-- man/plot_variance_propagation.Rd | 8 +++--- man/update_otu_table.Rd | 9 +++--- 21 files changed, 173 insertions(+), 114 deletions(-) diff --git a/R/add_rarefaction_metrics.R b/R/add_rarefaction_metrics.R index b1eaff0..b9c6cd9 100644 --- a/R/add_rarefaction_metrics.R +++ b/R/add_rarefaction_metrics.R @@ -28,29 +28,34 @@ #' 0.95, means that 5% of the reads in that sample are from OTUs #' that appear only once. #' +#' @seealso [plot_rarefaction_metrics()] for visualizing these metrics, and +#' [multi_rarefy()] for performing rarefaction on a `phyloseq` object. +#' #' @examples -#' \donttest{ #' library(phyloseq) #' library(BRCore) -#' # From an object class "phyloseq" with added alpha metrics #' #' data("bcse", package = "BRCore") +#' +#' # Adding metrics to a "phyloseq" object #' bcse_metrics <- add_rarefaction_metrics(data = bcse) -#' sample_data(bcse_metrics) +#' sample_data(bcse_metrics)|> +#' head(10) #' -#' # From a class "data.frame" count table object #' +#' # Adding metrics to a "data.frame" count table object #' bcse_otutable <- as.data.frame( -#' as(otu_table(bcse), "matrix") +#' as.matrix(otu_table(bcse)) #' ) -#' test_otutable_metrics <- add_rarefaction_metrics( +#' +#' bcse_otutable_metrics <- add_rarefaction_metrics( #' data = bcse_otutable #' ) -#' test_otutable_metrics[ -#' utils::tail(seq_len(nrow(test_otutable_metrics)), 10), -#' utils::tail(seq_len(ncol(test_otutable_metrics)), 20) +#' bcse_otutable_metrics[ +#' head(seq_len(nrow(bcse_otutable_metrics)), 10), +#' tail(seq_len(ncol(bcse_otutable_metrics)), 20) #' ] -#' } +#' #' #' @importFrom phyloseq otu_table sample_data sample_data<- taxa_are_rows #' @importFrom tibble rownames_to_column column_to_rownames diff --git a/R/brcore_theme.R b/R/brcore_theme.R index 9bf0f36..a9c32f4 100644 --- a/R/brcore_theme.R +++ b/R/brcore_theme.R @@ -34,7 +34,6 @@ #' to any \code{ggplot} with \code{+}. #' #' @examples -#' \donttest{ #' library(ggplot2) #' ggplot(mtcars, aes(wt, mpg)) + #' geom_point() + @@ -47,7 +46,6 @@ #' dashed_grid = TRUE, #' extra_themes = list(no_legend = theme(legend.position = "none")) #' ) -#' } #' #' @importFrom ggplot2 theme_classic theme element_text element_line #' element_blank element_rect diff --git a/R/fit_neutral_model.R b/R/fit_neutral_model.R index bc8daa9..236be59 100644 --- a/R/fit_neutral_model.R +++ b/R/fit_neutral_model.R @@ -39,11 +39,10 @@ #' plant core microbiome membership. Current Opinion in Microbiology, 49:50-58 #' #' -#' @seealso \code{\link{plot_neutral_model}} +#' @seealso [plot_neutral_model()] #' #' @examples #' \donttest{ -#' library(phyloseq) #' library(BRCore) #' data("switchgrass", package = "BRCore") #' @@ -70,6 +69,7 @@ #' @importFrom stats4 mle #' @importFrom minpack.lm nlsLM #' @importFrom Hmisc binconf +#' #' @export fit_neutral_model <- function( otu_table, diff --git a/R/identify_core.R b/R/identify_core.R index 99bebb2..44fcda3 100644 --- a/R/identify_core.R +++ b/R/identify_core.R @@ -94,25 +94,17 @@ #' Requires \pkg{phyloseq}, \pkg{dplyr}, \pkg{tidyr}, \pkg{tibble}, \pkg{rlang}, #' and \pkg{vegan}. #' -#' @importFrom phyloseq sample_sums taxa_are_rows otu_table sample_data -#' @importFrom phyloseq tax_table -#' @importFrom dplyr left_join group_by summarise transmute arrange desc mutate -#' @importFrom dplyr n last select slice_head -#' @importFrom tidyr pivot_longer -#' @importFrom tibble rownames_to_column column_to_rownames -#' @importFrom rlang ensym as_name .data -#' @importFrom vegan decostand -#' @importFrom cli cli_text cli_warn cli_abort cli_alert_success cli_alert_info -#' @importFrom utils combn tail +#' @seealso [multi_rarefy()], [plot_identified_core()], and +#' [plot_core_distribution()] #' #' @examples #' \donttest{ -#' library(phyloseq) #' library(BRCore) -#' # Example using switchgrass phyloseq object and grouping variable -#' # 'sampling_date' +#' #' data("switchgrass", package = "BRCore") +#' data("bcse", package = "BRCore") #' +#' # With rarefied data #' res <- identify_core( #' physeq_obj = switchgrass, #' priority_var = "sampling_date", @@ -120,10 +112,39 @@ #' seed = 091825 #' ) #' -#' # Inspect results #' str(res) +#' +#' # With unrarefied data (requires multi_rarefy step) +#' rarefied_list <- multi_rarefy( +#' physeq_obj = bcse, +#' depth_level = 1000, +#' num_iter = 3, +#' .as = "list", +#' set_seed = 7642 +#' ) +#' +#' +#' res_rare <- identify_core( +#' physeq_obj = bcse, +#' rarefied_list = rarefied_list, +#' priority_var = "sampling_date", +#' increase_value = 0.02, +#' seed = 091825 +#' ) +#' +#' str(res_rare) #' } #' +#' @importFrom phyloseq sample_sums taxa_are_rows otu_table sample_data +#' @importFrom phyloseq tax_table +#' @importFrom dplyr left_join group_by summarise transmute arrange desc mutate +#' @importFrom dplyr n last select slice_head +#' @importFrom tidyr pivot_longer +#' @importFrom tibble rownames_to_column column_to_rownames +#' @importFrom rlang ensym as_name .data +#' @importFrom vegan decostand +#' @importFrom cli cli_text cli_warn cli_abort cli_alert_success cli_alert_info +#' @importFrom utils combn tail #' @export identify_core <- function( physeq_obj, diff --git a/R/multi_rarefy.R b/R/multi_rarefy.R index 3c4bb52..eef9e4a 100644 --- a/R/multi_rarefy.R +++ b/R/multi_rarefy.R @@ -1,8 +1,9 @@ #' Run rarefaction for microbiome count tables #' #' This function performs rarefaction on a `phyloseq` object by randomly -#' sub-sampling OTUs/ASVs within samples without replacement for a number of iterations specified by the user. -#' Samples with fewer OTUs/ASVs than the specified `depth_level` are discarded. +#' sub-sampling OTUs/ASVs within samples without replacement for a number of +#' iterations specified by the user. Samples with fewer OTUs/ASVs than the +#' specified `depth_level` are discarded. #' #' @param physeq_obj A `phyloseq` object containing an OTU/ASV table. #' @param depth_level An integer specifying the sequencing depth (number of @@ -21,19 +22,11 @@ #' represent the average sequence counts calculated across all iterations. #' Samples with less than `depth_level` sequences are discarded. #' -#' -#' @importFrom dplyr group_by summarise across everything filter near -#' @importFrom dplyr where -#' @importFrom tibble rownames_to_column column_to_rownames -#' @importFrom phyloseq otu_table -#' @importFrom cli cli_h1 cli_h2 cli_alert_info cli_alert_warning -#' @importFrom cli cli_alert_success cli_alert_danger -#' @importFrom utils head -#' @importFrom vegan rrarefy +#' @seealso [update_otu_table()] for updating the OTU table in a `phyloseq` +#' object and [vegan::rrarefy()] for the underlying rarefaction method used in +#' this function. #' #' @examples -#' \donttest{ -#' library(phyloseq) #' library(BRCore) #' data("bcse", package = "BRCore") #' @@ -41,13 +34,22 @@ #' otu_table_rare <- multi_rarefy( #' physeq_obj = bcse, #' depth_level = 1000, -#' num_iter = 100, +#' num_iter = 10, #' .as = "list", #' set_seed = 7642 #' ) #' #' rowSums(otu_table_rare[[1]]) -#' } +#' +#' +#' @importFrom dplyr group_by summarise across everything filter near +#' @importFrom dplyr where +#' @importFrom tibble rownames_to_column column_to_rownames +#' @importFrom phyloseq otu_table +#' @importFrom cli cli_h1 cli_h2 cli_alert_info cli_alert_warning +#' @importFrom cli cli_alert_success cli_alert_danger +#' @importFrom utils head +#' @importFrom vegan rrarefy #' #' @export multi_rarefy <- function( diff --git a/R/plot_abundance_occupancy.R b/R/plot_abundance_occupancy.R index e4ba022..454b0b4 100644 --- a/R/plot_abundance_occupancy.R +++ b/R/plot_abundance_occupancy.R @@ -26,11 +26,10 @@ #' highlighted in red and non-core taxa in grey. The x-axis shows log10-transformed #' mean abundance and the y-axis shows occupancy (0-1). #' -#' @seealso \code{\link{plot_core_distribution}} and \code{\link{identify_core}} +#' @seealso [plot_core_distribution()] and [identify_core()] #' #' @examples #' \donttest{ -#' library(phyloseq) #' library(BRCore) #' # Generate an object from `identify_core()` and then plot #' diff --git a/R/plot_core_distribution.R b/R/plot_core_distribution.R index 8e09135..e18a38b 100644 --- a/R/plot_core_distribution.R +++ b/R/plot_core_distribution.R @@ -27,9 +27,11 @@ #' #' @return A ggplot2 object that can be further customized. #' +#' @seealso [identify_core()], [plot_abundance_occupancy()], and +#' [plot_identified_core()] +#' #' @examples #' \donttest{ -#' library(phyloseq) #' library(BRCore) #' # Generate an object from identify_core and then plot #' data("switchgrass", package = "BRCore") diff --git a/R/plot_identified_core.R b/R/plot_identified_core.R index 9d56fc4..436985d 100644 --- a/R/plot_identified_core.R +++ b/R/plot_identified_core.R @@ -26,7 +26,6 @@ #' #' @examples #' \donttest{ -#' library(phyloseq) #' library(BRCore) #' # Example with the package switchgrass dataset #' data("switchgrass", package = "BRCore") @@ -52,7 +51,10 @@ #' #' @importFrom grid unit #' @importFrom dplyr mutate filter -#' @importFrom ggplot2 ggplot aes geom_point geom_vline annotate labs theme_classic theme element_text scale_x_continuous scale_y_continuous expansion +#' @importFrom ggplot2 ggplot aes geom_point geom_vline annotate labs +#' @importFrom ggplot2 theme_classic theme element_text scale_x_continuous +#' @importFrom ggplot2 scale_y_continuous expansion +#' #' @export plot_identified_core <- function( bray_curtis_ranked, @@ -176,13 +178,6 @@ plot_identified_core <- function( .brcore_theme() + scale_x_continuous(breaks = seq(0, max(new_data$rank_num), by = 5)) - # To add some more fancy graphics later... - # axis.title = element_text(size = 11), - # axis.text = element_text(size = 9), - # panel.grid.minor = element_blank(), - # panel.grid.major.x = element_line(color = "gray90", size = 0.3), - # panel.grid.major.y = element_line(color = "gray90", size = 0.3)) - return(list( df_for_plot = new_data, plot_identified_core = p diff --git a/R/plot_rarefaction_metrics.R b/R/plot_rarefaction_metrics.R index 317a646..bb1f95a 100644 --- a/R/plot_rarefaction_metrics.R +++ b/R/plot_rarefaction_metrics.R @@ -9,8 +9,9 @@ #' #' @return A `ggarrange` object with six plots. #' +#' @seealso [add_rarefaction_metrics()] +#' #' @examples -#' \donttest{ #' library(phyloseq) #' library(BRCore) #' @@ -26,7 +27,6 @@ #' # pre-computed read_num, goods_cov, and outlier columns #' sample_data_df <- data.frame(sample_data(bcse_metrics)) #' plot_rarefaction_metrics(sample_data_df) -#' } #' #' @importFrom ggplot2 ggplot aes geom_histogram geom_point geom_jitter #' @importFrom ggplot2 geom_boxplot geom_bar theme_bw theme labs @@ -39,7 +39,6 @@ #' @importFrom scales comma #' @importFrom cli cli_alert_info cli_progress_step cli_alert_success cli_abort #' @importFrom phyloseq sample_data - #' #' @export #' diff --git a/R/plot_variance_propagation.R b/R/plot_variance_propagation.R index d929e17..f55b23b 100644 --- a/R/plot_variance_propagation.R +++ b/R/plot_variance_propagation.R @@ -14,27 +14,24 @@ #' scale (\code{scale_color_viridis_d}) is used; otherwise the continuous #' scale (\code{scale_color_viridis_c}) is used. Default \code{FALSE}. #' -#' @importFrom tibble rownames_to_column column_to_rownames -#' @importFrom phyloseq otu_table sample_data taxa_are_rows -#' @importFrom cli cli_h1 cli_h2 cli_alert_info cli_alert_warning cli_alert_success cli_alert_danger -#' @importFrom utils head -#' @importFrom vegan rrarefy -#' @importFrom dplyr left_join all_of + #' #' @return ggplot object comparing raw vs rarefied diversity distributions across iterations. #' +#' #' @examples -#' \donttest{ #' library(phyloseq) #' library(BRCore) -#' # Example using the bcse dataset, comparing hill q=1 between Poplar and Switchgrass plots +#' # Example comparing hill q=1 between Poplar and Switchgrass plots +#' data("bcse", package = "BRCore") #' bcse_filt <- bcse |> #' subset_samples(Crop %in% c("Poplar", "Switchgrass")) +#' #' bcse_rarefied_otutable_filt <- #' multi_rarefy( #' physeq_obj = bcse_filt, #' depth_level = 1000, -#' num_iter = 100, +#' num_iter = 10, #' .as = "list", #' set_seed = 7643 #' ) @@ -46,7 +43,14 @@ #' group_var = "Crop", #' group_color = "Plot" #') -#' } +#' +#' @importFrom tibble rownames_to_column column_to_rownames +#' @importFrom phyloseq otu_table sample_data taxa_are_rows +#' @importFrom cli cli_h1 cli_h2 cli_alert_info cli_alert_warning +#' @importFrom cli cli_alert_success cli_alert_danger +#' @importFrom utils head +#' @importFrom vegan rrarefy +#' @importFrom dplyr left_join all_of #' #' @export plot_variance_propagation <- function( diff --git a/R/update_otu_table.R b/R/update_otu_table.R index a667bf8..b87d681 100644 --- a/R/update_otu_table.R +++ b/R/update_otu_table.R @@ -16,20 +16,14 @@ #' #' @return A `phyloseq` object. #' -#' @importFrom phyloseq otu_table sample_data tax_table phy_tree refseq -#' @importFrom phyloseq phyloseq sample_names prune_taxa prune_samples -#' @importFrom phyloseq sample_sums taxa_sums -#' @importFrom cli cli_inform cli_alert_success cli_alert_warning cli_alert_info -#' @importFrom cli cli_abort -#' @importFrom magrittr %>% +#' @seealso [multi_rarefy()] #' #' @examples -#' \donttest{ #' library(phyloseq) #' library(BRCore) #' data(GlobalPatterns, package = "phyloseq") #' -#' # List output (.as = "list") +#' # List output #' otu_list <- #' multi_rarefy( #' physeq_obj = GlobalPatterns, @@ -42,7 +36,7 @@ #' # Extract iteration 2 #' rarefied_gp <- update_otu_table(GlobalPatterns, otu_list, iteration = 2) #' -#' # Array output (.as = "array") +#' # Array output #' otu_array <- #' multi_rarefy( #' physeq_obj = GlobalPatterns, @@ -54,8 +48,13 @@ #' #' # Extract iteration 1 #' rarefied_gp2 <- update_otu_table(GlobalPatterns, otu_array, iteration = 1) -#' } #' +#' @importFrom phyloseq otu_table sample_data tax_table phy_tree refseq +#' @importFrom phyloseq phyloseq sample_names prune_taxa prune_samples +#' @importFrom phyloseq sample_sums taxa_sums +#' @importFrom cli cli_inform cli_alert_success cli_alert_warning cli_alert_info +#' @importFrom cli cli_abort +#' @importFrom magrittr %>% #' @export update_otu_table <- function(physeq_obj, rarefied_otus, iteration = NULL) { suppressMessages(.phyloseq_class_check(physeq_obj)) diff --git a/man/add_rarefaction_metrics.Rd b/man/add_rarefaction_metrics.Rd index 37f9ea9..f9c6397 100644 --- a/man/add_rarefaction_metrics.Rd +++ b/man/add_rarefaction_metrics.Rd @@ -39,27 +39,33 @@ individuals in the sample. For example, a Good's coverage of that appear only once. } \examples{ -\donttest{ library(phyloseq) library(BRCore) -# From an object class "phyloseq" with added alpha metrics data("bcse", package = "BRCore") + +# Adding metrics to a "phyloseq" object bcse_metrics <- add_rarefaction_metrics(data = bcse) -sample_data(bcse_metrics) +sample_data(bcse_metrics)|> +head(10) -# From a class "data.frame" count table object +# Adding metrics to a "data.frame" count table object bcse_otutable <- as.data.frame( - as(otu_table(bcse), "matrix") + as.matrix(otu_table(bcse)) ) -test_otutable_metrics <- add_rarefaction_metrics( + +bcse_otutable_metrics <- add_rarefaction_metrics( data = bcse_otutable ) -test_otutable_metrics[ - utils::tail(seq_len(nrow(test_otutable_metrics)), 10), - utils::tail(seq_len(ncol(test_otutable_metrics)), 20) +bcse_otutable_metrics[ + head(seq_len(nrow(bcse_otutable_metrics)), 10), + tail(seq_len(ncol(bcse_otutable_metrics)), 20) ] -} + +} +\seealso{ +\code{\link[=plot_rarefaction_metrics]{plot_rarefaction_metrics()}} for visualizing these metrics, and +\code{\link[=multi_rarefy]{multi_rarefy()}} for performing rarefaction on a \code{phyloseq} object. } diff --git a/man/fit_neutral_model.Rd b/man/fit_neutral_model.Rd index 2881961..123dbe3 100644 --- a/man/fit_neutral_model.Rd +++ b/man/fit_neutral_model.Rd @@ -39,7 +39,6 @@ below (i.e. interpreted as dispersally limited) the model predictions. } \examples{ \donttest{ -library(phyloseq) library(BRCore) data("switchgrass", package = "BRCore") @@ -79,5 +78,5 @@ plant core microbiome membership. Current Opinion in Microbiology, 49:50-58 \url{https://doi.org/10.1016/j.mib.2019.09.008} } \seealso{ -\code{\link{plot_neutral_model}} +\code{\link[=plot_neutral_model]{plot_neutral_model()}} } diff --git a/man/identify_core.Rd b/man/identify_core.Rd index 276fa64..dc0a09a 100644 --- a/man/identify_core.Rd +++ b/man/identify_core.Rd @@ -123,12 +123,12 @@ and \pkg{vegan}. \examples{ \donttest{ -library(phyloseq) library(BRCore) -# Example using switchgrass phyloseq object and grouping variable -# 'sampling_date' + data("switchgrass", package = "BRCore") +data("bcse", package = "BRCore") +# With rarefied data res <- identify_core( physeq_obj = switchgrass, priority_var = "sampling_date", @@ -136,8 +136,27 @@ res <- identify_core( seed = 091825 ) -# Inspect results str(res) + +# With unrarefied data (requires multi_rarefy step) +rarefied_list <- multi_rarefy( + physeq_obj = bcse, + depth_level = 1000, + num_iter = 3, + .as = "list", + set_seed = 7642 +) + + +res_rare <- identify_core( + physeq_obj = bcse, + rarefied_list = rarefied_list, + priority_var = "sampling_date", + increase_value = 0.02, + seed = 091825 +) + +str(res_rare) } } @@ -147,3 +166,7 @@ distributions to prioritize plant core microbiome membership. Current Opinion in Microbiology, 49:50-58 doi:https://doi.org/10.1016/j.mib.2019.09.008 } +\seealso{ +\code{\link[=multi_rarefy]{multi_rarefy()}}, \code{\link[=plot_identified_core]{plot_identified_core()}}, and +\code{\link[=plot_core_distribution]{plot_core_distribution()}} +} diff --git a/man/multi_rarefy.Rd b/man/multi_rarefy.Rd index d1f8de1..664f520 100644 --- a/man/multi_rarefy.Rd +++ b/man/multi_rarefy.Rd @@ -37,12 +37,11 @@ Samples with less than \code{depth_level} sequences are discarded. } \description{ This function performs rarefaction on a \code{phyloseq} object by randomly -sub-sampling OTUs/ASVs within samples without replacement for a number of iterations specified by the user. -Samples with fewer OTUs/ASVs than the specified \code{depth_level} are discarded. +sub-sampling OTUs/ASVs within samples without replacement for a number of +iterations specified by the user. Samples with fewer OTUs/ASVs than the +specified \code{depth_level} are discarded. } \examples{ -\donttest{ -library(phyloseq) library(BRCore) data("bcse", package = "BRCore") @@ -50,12 +49,17 @@ data("bcse", package = "BRCore") otu_table_rare <- multi_rarefy( physeq_obj = bcse, depth_level = 1000, - num_iter = 100, + num_iter = 10, .as = "list", set_seed = 7642 ) rowSums(otu_table_rare[[1]]) -} + +} +\seealso{ +\code{\link[=update_otu_table]{update_otu_table()}} for updating the OTU table in a \code{phyloseq} +object and \code{\link[vegan:rarefy]{vegan::rrarefy()}} for the underlying rarefaction method used in +this function. } diff --git a/man/plot_abundance_occupancy.Rd b/man/plot_abundance_occupancy.Rd index beeda2c..b2df20c 100644 --- a/man/plot_abundance_occupancy.Rd +++ b/man/plot_abundance_occupancy.Rd @@ -39,7 +39,6 @@ found in microbiome data. } \examples{ \donttest{ -library(phyloseq) library(BRCore) # Generate an object from `identify_core()` and then plot @@ -61,5 +60,5 @@ plot_abundance_occupancy( } \seealso{ -\code{\link{plot_core_distribution}} and \code{\link{identify_core}} +\code{\link[=plot_core_distribution]{plot_core_distribution()}} and \code{\link[=identify_core]{identify_core()}} } diff --git a/man/plot_core_distribution.Rd b/man/plot_core_distribution.Rd index 755bc6d..d00227a 100644 --- a/man/plot_core_distribution.Rd +++ b/man/plot_core_distribution.Rd @@ -45,7 +45,6 @@ a grouping variable. } \examples{ \donttest{ -library(phyloseq) library(BRCore) # Generate an object from identify_core and then plot data("switchgrass", package = "BRCore") @@ -67,3 +66,7 @@ plot_core_distribution( } } +\seealso{ +\code{\link[=identify_core]{identify_core()}}, \code{\link[=plot_abundance_occupancy]{plot_abundance_occupancy()}}, and +\code{\link[=plot_identified_core]{plot_identified_core()}} +} diff --git a/man/plot_identified_core.Rd b/man/plot_identified_core.Rd index c0c9a6b..6cad70c 100644 --- a/man/plot_identified_core.Rd +++ b/man/plot_identified_core.Rd @@ -42,7 +42,6 @@ observed \code{proportionBC} range to avoid overlap. } \examples{ \donttest{ -library(phyloseq) library(BRCore) # Example with the package switchgrass dataset data("switchgrass", package = "BRCore") diff --git a/man/plot_rarefaction_metrics.Rd b/man/plot_rarefaction_metrics.Rd index 931b452..3658186 100644 --- a/man/plot_rarefaction_metrics.Rd +++ b/man/plot_rarefaction_metrics.Rd @@ -19,7 +19,6 @@ Good's coverage, and outlier behavior based on a data frame or a \code{phyloseq} object with rare stats already added via \code{add_rare_stats()}. } \examples{ -\donttest{ library(phyloseq) library(BRCore) @@ -35,6 +34,8 @@ plot_rarefaction_metrics(bcse_metrics) # pre-computed read_num, goods_cov, and outlier columns sample_data_df <- data.frame(sample_data(bcse_metrics)) plot_rarefaction_metrics(sample_data_df) -} } +\seealso{ +\code{\link[=add_rarefaction_metrics]{add_rarefaction_metrics()}} +} diff --git a/man/plot_variance_propagation.Rd b/man/plot_variance_propagation.Rd index 39f6f04..4c28ee6 100644 --- a/man/plot_variance_propagation.Rd +++ b/man/plot_variance_propagation.Rd @@ -38,17 +38,18 @@ ggplot object comparing raw vs rarefied diversity distributions across iteration This function evaluate the variance between rarefaction iterations from \code{multi_rarefy()} by visually comparing raw vs. rarefied alpha diversity metrics calculated at each iterations. It is possible to plot observed richness (q=0), Shannon diversity (q=1), or Simpson diversity (q=2) by setting the \code{q} parameter to "richness" or \code{q} = 0, "shannon" or \code{q} = 1, or "shannon" or \code{q} = 2. The plot is faceted by method (raw vs rarefied) and colored by a specified grouping variable from the sample data. } \examples{ -\donttest{ library(phyloseq) library(BRCore) -# Example using the bcse dataset, comparing hill q=1 between Poplar and Switchgrass plots +# Example comparing hill q=1 between Poplar and Switchgrass plots +data("bcse", package = "BRCore") bcse_filt <- bcse |> subset_samples(Crop \%in\% c("Poplar", "Switchgrass")) + bcse_rarefied_otutable_filt <- multi_rarefy( physeq_obj = bcse_filt, depth_level = 1000, - num_iter = 100, + num_iter = 10, .as = "list", set_seed = 7643 ) @@ -60,6 +61,5 @@ plot_variance_propagation( group_var = "Crop", group_color = "Plot" ) -} } diff --git a/man/update_otu_table.Rd b/man/update_otu_table.Rd index 996b47c..52fd987 100644 --- a/man/update_otu_table.Rd +++ b/man/update_otu_table.Rd @@ -28,12 +28,11 @@ be a data frame, a list of data frames (\code{.as = "list"}), or a 3D array to use via the \code{iteration} parameter. } \examples{ -\donttest{ library(phyloseq) library(BRCore) data(GlobalPatterns, package = "phyloseq") -# List output (.as = "list") +# List output otu_list <- multi_rarefy( physeq_obj = GlobalPatterns, @@ -46,7 +45,7 @@ otu_list <- # Extract iteration 2 rarefied_gp <- update_otu_table(GlobalPatterns, otu_list, iteration = 2) -# Array output (.as = "array") +# Array output otu_array <- multi_rarefy( physeq_obj = GlobalPatterns, @@ -58,6 +57,8 @@ otu_array <- # Extract iteration 1 rarefied_gp2 <- update_otu_table(GlobalPatterns, otu_array, iteration = 1) -} } +\seealso{ +\code{\link[=multi_rarefy]{multi_rarefy()}} +} From a5c72fa4f91fb4a69f64bd675417fcb898a18638 Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Thu, 30 Apr 2026 10:18:12 -0500 Subject: [PATCH 08/37] chore: Remove parallel computing utilities from pkgdown reference and fix OTU table formatting --- _pkgdown.yml | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index be2674b..8f90ce9 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -28,14 +28,9 @@ reference: - add_rarefaction_metrics - multi_rarefy - plot_rarefaction_metrics - - title: Parallel Computing Utilities - contents: - - detect_hpc_type - - get_available_cores - - is_hpc_environment - - setup_parallel_backend - title: OTU Table Utilities - contents: update_otu_table + contents: + - update_otu_table - title: Datasets contents: - bcse From e365934a68951b09704705ef258b7e73a6d4ac6a Mon Sep 17 00:00:00 2001 From: jibarozzo Date: Thu, 30 Apr 2026 10:59:08 -0500 Subject: [PATCH 09/37] docs: Add `switchgrass_core` precomputed dataset and refactor examples to use it. Updated citation style to APA 7th edition. --- R/data.R | 54 ++++++++++++++++++++++++-------- R/fit_neutral_model.R | 26 ++++++--------- R/plot_abundance_occupancy.R | 16 ++-------- R/plot_core_distribution.R | 16 ++-------- R/plot_identified_core.R | 25 +++++---------- R/plot_neutral_model.R | 12 ++----- data/switchgrass_core.rda | Bin 0 -> 35964 bytes man/bcse.Rd | 8 ++--- man/bean.Rd | 6 ++-- man/fit_neutral_model.Rd | 26 ++++++--------- man/mimulus.Rd | 6 ++-- man/plot_abundance_occupancy.Rd | 16 ++-------- man/plot_core_distribution.Rd | 16 ++-------- man/plot_identified_core.Rd | 25 +++++---------- man/plot_neutral_model.Rd | 12 ++----- man/switchgrass.Rd | 6 ++-- man/switchgrass_core.Rd | 35 +++++++++++++++++++++ 17 files changed, 137 insertions(+), 168 deletions(-) create mode 100644 data/switchgrass_core.rda create mode 100644 man/switchgrass_core.Rd diff --git a/R/data.R b/R/data.R index d0e6f5e..8002f3f 100644 --- a/R/data.R +++ b/R/data.R @@ -7,13 +7,39 @@ #' @docType data #' @format An object class "phyloseq". #' -#' @references Shade A, Stopnisek N. Abundance-occupancy distributions to -#' prioritize plant core microbiome membership. Curr Opin Microbiol. 2019 Jun -#' 49:50-58. doi:10.1016/j.mib.2019.09.008. Epub 2019 Nov 10. PMID: 31715441. +#' @references Shade A, Stopnisek N (2019) Abundance-occupancy distributions to +#' prioritize plant core microbiome membership. Current Opinion in +#' Microbiology, 49:50-58 #' #' @source Internal test dataset. "switchgrass" +#' Identified core microbiome members for the switchgrass dataset +#' +#' @description A phyloseq containing the identified core microbiome members +#' for the switchgrass dataset. This object is used for BRCore function +#' examples. +#' It was generated by running the following on BRCore 2.0.2 (2026-04-30): +#' ```R +#' switchgrass_core <- identify_core( +#' physeq_obj = switchgrass, +#' priority_var = "sampling_date", +#' increase_value = 0.02, +#' seed = 092825 +#' ) +#' ``` +#' +#' @usage data("switchgrass_core") +#' @docType data +#' @format An object class "phyloseq". +#' +#' @references Shade A, Stopnisek N (2019) Abundance-occupancy distributions to +#' prioritize plant core microbiome membership. Current Opinion in +#' Microbiology, 49:50-58 +#' +#' @source Internal test dataset. +"switchgrass_core" + #' 16S amplicon dataset from yellow monkeyflower (Mimulus guttatus) #' #' @description A phyloseq containing 11085 ASVs (Amplicon Sequence Variants) @@ -23,9 +49,9 @@ #' @docType data #' @format An object class "phyloseq". #' -#' @references Shade A, Stopnisek N. Abundance-occupancy distributions to -#' prioritize plant core microbiome membership. Curr Opin Microbiol. 2019 Jun -#' 49:50-58. doi:10.1016/j.mib.2019.09.008. Epub 2019 Nov 10. PMID: 31715441 +#' @references Shade A, Stopnisek N (2019) Abundance-occupancy distributions to +#' prioritize plant core microbiome membership. Current Opinion in +#' Microbiology, 49:50-58 #' #' @source Internal test dataset. "mimulus" @@ -39,9 +65,10 @@ #' @docType data #' @format An object class "phyloseq". #' -#' @references Shade A, Stopnisek N. Abundance-occupancy distributions to -#' prioritize plant core microbiome membership. Curr Opin Microbiol. 2019 Jun -#' 49:50-58. doi:10.1016/j.mib.2019.09.008. Epub 2019 Nov 10. PMID: 31715441. +#' @references Shade A, Stopnisek N (2019) Abundance-occupancy distributions to +#' prioritize plant core microbiome membership. Current Opinion in +#' Microbiology, 49:50-58 +#' #' #' @source Internal test dataset. "bean" @@ -57,10 +84,11 @@ #' @docType data #' @format An object class "phyloseq". #' -#' @references Haan NL, Benucci GNM, Fiser CM, Bonito G, Landis DA. -#' Contrasting effects of bioenergy crops on biodiversity. Sci Adv. 2023 Sep -#' 22;9(38):eadh7960. doi: 10.1126/sciadv.adh7960. Epub 2023 Sep 22. PMID: -#' 37738354; PMCID: PMC10516493. +#' @references Haan, N. L., Benucci, G. N. M., Fiser, C. M., Bonito, G., & +#' Landis, D. A. (2023). Contrasting effects of bioenergy crops on +#' biodiversity. Science Advances, 9(38), eadh7960. +#' + #' #' @source Internal test dataset. "bcse" diff --git a/R/fit_neutral_model.R b/R/fit_neutral_model.R index 236be59..a6d4884 100644 --- a/R/fit_neutral_model.R +++ b/R/fit_neutral_model.R @@ -25,15 +25,16 @@ #' joined to per-taxon predictions #' #' @references -#' Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. (2006) Quantifying -#' the roles of immigration and chance in shaping prokaryote community -#' structure. Environ Microbiol. 8(4):732-40. +#' Sloan, W. T., Lunn, M., Woodcock, S., Head, I. M., Nee, S., & Curtis, T. P. +#' (2006). Quantifying the roles of immigration and chance in shaping +#' prokaryote community structure. Environmental Microbiology, 8(4), 732–740. #' #' -#' Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, Bohannan -#' BJ. (2015) Contribution of neutral processes to the assembly of gut microbial -#' communities in the zebrafish over host development. ISME J. -#' 10(3):655-64. +#' Burns, A. R., Stephens, W. Z., Stagaman, K., Wong, S., Rawls, J. F., +#' Guillemin, K., & Bohannan, B. J. M. (2016). Contribution of neutral +#' processes to the assembly of gut microbial communities in the zebrafish over +#' host development. The ISME Journal, 10(3), 655–664. +#' #' #' Shade A, Stopnisek N (2019) Abundance-occupancy distributions to prioritize #' plant core microbiome membership. Current Opinion in Microbiology, 49:50-58 @@ -42,16 +43,8 @@ #' @seealso [plot_neutral_model()] #' #' @examples -#' \donttest{ #' library(BRCore) -#' data("switchgrass", package = "BRCore") -#' -#' switchgrass_core <- identify_core( -#' physeq_obj = switchgrass, -#' priority_var = "sampling_date", -#' increase_value = 0.02, -#' seed = 092825 -#' ) +#' data("switchgrass_core", package = "BRCore") #' #' switchgrass_core_fit <- fit_neutral_model( #' otu_table = switchgrass_core$otu_table, @@ -63,7 +56,6 @@ #' switchgrass_core_fit$goodness_of_fit #' switchgrass_core_fit$model_prediction |> #' head() -#' } #' #' @importFrom stats confint pbeta pbinom ppois dnorm AIC BIC coef #' @importFrom stats4 mle diff --git a/R/plot_abundance_occupancy.R b/R/plot_abundance_occupancy.R index 454b0b4..cdd8ef2 100644 --- a/R/plot_abundance_occupancy.R +++ b/R/plot_abundance_occupancy.R @@ -29,25 +29,15 @@ #' @seealso [plot_core_distribution()] and [identify_core()] #' #' @examples -#' \donttest{ #' library(BRCore) -#' # Generate an object from `identify_core()` and then plot #' -#' data("switchgrass", package = "BRCore") +#' data("switchgrass_core", package = "BRCore") #' -#' switchgrass_core <- identify_core( -#' physeq_obj = switchgrass, -#' priority_var = "sampling_date", -#' increase_value = 0.02, -#' abundance_weight = 0, -#' seed = 1234 -#' ) -#' -#' plot_abundance_occupancy( +#' p <- plot_abundance_occupancy( #' core_result = switchgrass_core, #' core_set = "increase" #' ) -#' } +#' print(p) #' #' @importFrom dplyr mutate #' @importFrom ggplot2 ggplot aes geom_point scale_fill_manual theme_classic diff --git a/R/plot_core_distribution.R b/R/plot_core_distribution.R index e18a38b..ed7e9cb 100644 --- a/R/plot_core_distribution.R +++ b/R/plot_core_distribution.R @@ -31,26 +31,16 @@ #' [plot_identified_core()] #' #' @examples -#' \donttest{ #' library(BRCore) -#' # Generate an object from identify_core and then plot -#' data("switchgrass", package = "BRCore") +#' data("switchgrass_core", package = "BRCore") #' -#' switchgrass_core <- identify_core( -#' physeq_obj = switchgrass, -#' priority_var = "sampling_date", -#' increase_value = 0.02, -#' abundance_weight = 0, -#' seed = 1234 -#' ) -#' -#' plot_core_distribution( +#' p <- plot_core_distribution( #' core_result = switchgrass_core, #' core_set = "increase", #' group_var = "sampling_date", #' plot_type = "bar" #' ) -#' } +#' print(p) #' #' @importFrom tibble rownames_to_column #' @importFrom tidyr pivot_longer diff --git a/R/plot_identified_core.R b/R/plot_identified_core.R index 436985d..eb7990b 100644 --- a/R/plot_identified_core.R +++ b/R/plot_identified_core.R @@ -25,27 +25,16 @@ #' @seealso \link[=identify_core]{identify_core()} #' #' @examples -#' \donttest{ #' library(BRCore) -#' # Example with the package switchgrass dataset -#' data("switchgrass", package = "BRCore") +#' data("switchgrass_core", package = "BRCore") #' -#' # Identify core taxa -#' res <- identify_core( -#' physeq_obj = switchgrass, -#' priority_var = "sampling_date", -#' increase_value = 0.02, -#' seed = 48821 +#' p <- plot_identified_core( +#' bray_curtis_ranked = switchgrass_core$bray_curtis_ranked, +#' elbow = switchgrass_core$elbow, +#' lastCall = switchgrass_core$bc_increase, +#' increase_value = switchgrass_core$increase_value #' ) -#' -#' # Plot using the returned curve and cut indices; label from increase_value -#' plot_identified_core( -#' bray_curtis_ranked = res$bray_curtis_ranked, -#' elbow = res$elbow, -#' lastCall = res$bc_increase, -#' increase_value = res$increase_value -#' ) -#' } +#' print(p) #' #' @seealso \code{\link{identify_core}} #' diff --git a/R/plot_neutral_model.R b/R/plot_neutral_model.R index 46dda94..0c01c00 100644 --- a/R/plot_neutral_model.R +++ b/R/plot_neutral_model.R @@ -33,15 +33,8 @@ #' predictions and metrics. #' #' @examples -#' \donttest{ -#' data("switchgrass", package = "BRCore") -#' -#' switchgrass_core <- identify_core( -#' physeq_obj = switchgrass, -#' priority_var = "sampling_date", -#' increase_value = 0.02, -#' seed = 092825 -#' ) +#' library(BRCore) +#' data("switchgrass_core", package = "BRCore") #' #' switchgrass_core_fit <- fit_neutral_model( #' otu_table = switchgrass_core$otu_table, @@ -51,7 +44,6 @@ #' #' p <- plot_neutral_model(switchgrass_core_fit) #' print(p) -#' } #' #' @seealso \code{\link{fit_neutral_model}} #' diff --git a/data/switchgrass_core.rda b/data/switchgrass_core.rda new file mode 100644 index 0000000000000000000000000000000000000000..a966935f28071c75db4270df0c0dcf9e054f6705 GIT binary patch literal 35964 zcmV(tKvQ&2UKVgRpfklJ z)Hd78#Oenx$cBnP&=P&%8luk|GfWgvE;$P|kp};b!HNZ{g?j>I=g4AIWbnoC)WFmR z=j^Up6P|ieX-DY`s8c>86?LY|v8TH*e3%1Sq^@tJj(4*4(SZv6b0$R#JA>}ALx|U9 zZzdfSOaz;Cx2^fLflT17<$3O}l}@b)b^NiOI%1656`$DW&xEExR((vw$nM`Bs8($d zOS5B!o=tXjpg@Z7wv>()v`=4Xl+J@AIWz_P1i8mIwD(&~L8VA?EFqXT`Y1N%UqgJE zU%Mp!5VHViG#Yh3@hgr1)y$#B?GPXAp!(FPwAU_p!#0E{?rpl9aG(lxI8H!?nq=Ov 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aek=YQ9xQY3&)c~Pf3(zf)e2OsgZ~$qyx Date: Tue, 28 Apr 2026 15:11:57 -0500 Subject: [PATCH 02/37] chore: remove parallel dependencies and clean up docs for CRAN v2.0.2 - Removal of "multiple" rarefaction concept. Replace by "multiple iterations" --- DESCRIPTION | 5 +- NAMESPACE | 12 - NEWS.md | 10 +- R/identify_core.R | 14 +- R/multi_rarefy.R | 15 +- R/parallel_helpers.R | 189 --------- R/plot_core_distribution.R | 3 +- R/plot_variance_propagation.R | 4 +- README.Rmd | 10 +- README.md | 10 +- _pkgdown.yml | 2 + man/detect_hpc_type.Rd | 19 - man/get_available_cores.Rd | 23 -- man/identify_core.Rd | 5 +- man/is_hpc_environment.Rd | 18 - man/multi_rarefy.Rd | 7 +- man/plot_core_distribution.Rd | 2 +- man/plot_variance_propagation.Rd | 4 +- man/setup_parallel_backend.Rd | 22 - tests/testthat/test-vignette-workflow.R | 2 +- .../testthat/test_sets/regenerate_test_data.R | 2 +- vignettes/BRCore-vignette.R | 52 ++- vignettes/BRCore-vignette.Rmd | 383 +++++++++++++----- vignettes/images/fig1_plot_metrics-1.png | Bin 43561 -> 43915 bytes 24 files changed, 359 insertions(+), 454 deletions(-) delete mode 100644 R/parallel_helpers.R delete mode 100644 man/detect_hpc_type.Rd delete mode 100644 man/get_available_cores.Rd delete mode 100644 man/is_hpc_environment.Rd delete mode 100644 man/setup_parallel_backend.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 7f9349d..3008363 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,7 +30,7 @@ Authors@R: c( Description: A unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. License: MIT + file LICENSE Encoding: UTF-8 -Language: en +Language: en-US Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 Imports: @@ -39,12 +39,9 @@ Imports: ggplot2 (>= 4.0.1), ggpubr (>= 0.6.2), ggrepel (>= 0.9.6), - ggsci (>= 4.1.0), Hmisc (>= 5.2.4), magrittr (>= 2.0.4), minpack.lm (>= 1.2.4), - parallel (>= 4.5.2), - parallelly (>= 1.45.1), phyloseq (>= 1.54.0), rlang (>= 1.1.6), scales (>= 1.4.0), diff --git a/NAMESPACE b/NAMESPACE index 433a4af..7de4704 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,11 +2,8 @@ export("%>%") export(add_rarefaction_metrics) -export(detect_hpc_type) export(fit_neutral_model) -export(get_available_cores) export(identify_core) -export(is_hpc_environment) export(multi_rarefy) export(plot_abundance_occupancy) export(plot_core_distribution) @@ -14,7 +11,6 @@ export(plot_identified_core) export(plot_neutral_model) export(plot_rarefaction_metrics) export(plot_variance_propagation) -export(setup_parallel_backend) export(sncm.fit) export(update_otu_table) importFrom(Hmisc,binconf) @@ -83,17 +79,9 @@ importFrom(ggplot2,theme_bw) importFrom(ggplot2,theme_classic) importFrom(ggpubr,ggarrange) importFrom(ggrepel,geom_text_repel) -importFrom(ggsci,scale_fill_npg) importFrom(grid,unit) importFrom(magrittr,"%>%") importFrom(minpack.lm,nlsLM) -importFrom(parallel,clusterEvalQ) -importFrom(parallel,clusterExport) -importFrom(parallel,makeCluster) -importFrom(parallel,parLapply) -importFrom(parallel,stopCluster) -importFrom(parallelly,availableCores) -importFrom(parallelly,makeClusterPSOCK) importFrom(phyloseq,"sample_data<-") importFrom(phyloseq,otu_table) importFrom(phyloseq,phy_tree) diff --git a/NEWS.md b/NEWS.md index f24139a..304ad28 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,9 +1,17 @@ +# BRCore 2.0.2 +Date: 2026-04-28 + +* Fixes to vignette and function documentation prior to CRAN submission. +* Removed "multiple rarefaction" concept from documentation. +* Removed `parallel_helpers.R` since we have moved away from parallel implementations. +* Cleaned NAMESPACE and DESCRIPTION. + # BRCore 2.0.1 Date: 2026-04-25 * Resubmission to CRAN to address auto-check issues on Debian and Windows. -BRCore 2.0.0 +# BRCore 2.0.0 Date: 2026-04-24 ## Breaking Changes diff --git a/R/identify_core.R b/R/identify_core.R index c04e1fb..99bebb2 100644 --- a/R/identify_core.R +++ b/R/identify_core.R @@ -19,7 +19,8 @@ #' @param priority_var The column name in the `sample_data` (e.g. #' sampling_date", "site") that is used for prioritizing the core microbiome. #' @param increase_value Increase value (numeric, scalar) used in the -#' calculation (default 0.02) for "increase". The "elbow" is always calculated and returned as \code{elbow_core} (see below for details). +#' calculation (default 0.02) for "increase". The "elbow" is always calculated +#' and returned as \code{elbow_core} (see below for details). #' @param abundance_weight Numeric in `[0,1]`; how much to weight mean relative #' abundance in the ranking score. `0` (default) uses occupancy/composite only. #' `1` ranks purely by abundance. Values in between blend the two (e.g., @@ -93,8 +94,10 @@ #' Requires \pkg{phyloseq}, \pkg{dplyr}, \pkg{tidyr}, \pkg{tibble}, \pkg{rlang}, #' and \pkg{vegan}. #' -#' @importFrom phyloseq sample_sums taxa_are_rows otu_table sample_data tax_table -#' @importFrom dplyr left_join group_by summarise transmute arrange desc mutate n last select slice_head +#' @importFrom phyloseq sample_sums taxa_are_rows otu_table sample_data +#' @importFrom phyloseq tax_table +#' @importFrom dplyr left_join group_by summarise transmute arrange desc mutate +#' @importFrom dplyr n last select slice_head #' @importFrom tidyr pivot_longer #' @importFrom tibble rownames_to_column column_to_rownames #' @importFrom rlang ensym as_name .data @@ -106,7 +109,7 @@ #' \donttest{ #' library(phyloseq) #' library(BRCore) -#' # Example using your switchgrass phyloseq object and grouping variable +#' # Example using switchgrass phyloseq object and grouping variable #' # 'sampling_date' #' data("switchgrass", package = "BRCore") #' @@ -144,7 +147,8 @@ identify_core <- function( .phyloseq_class_check(physeq_obj) # Define arguments ---- - # Check if samples are rarefied (all have same depth, accounting for floating-point precision) + # Check if samples are rarefied (all have same depth, accounting for + # floating-point precision) ## Validate / generate rarefied_list ---- min_sum <- min(sample_sums(physeq_obj)) max_sum <- max(sample_sums(physeq_obj)) diff --git a/R/multi_rarefy.R b/R/multi_rarefy.R index b3bbcf4..3c4bb52 100644 --- a/R/multi_rarefy.R +++ b/R/multi_rarefy.R @@ -1,8 +1,7 @@ -#' Run multiple rarefaction for microbiome count tables +#' Run rarefaction for microbiome count tables #' -#' This function performs multiple rarefaction on a `phyloseq` object by randomly -#' sub-sampling OTUs/ASVs within samples without replacement. The process is -#' repeated for a specified number of iterations, and the results are averaged. +#' This function performs rarefaction on a `phyloseq` object by randomly +#' sub-sampling OTUs/ASVs within samples without replacement for a number of iterations specified by the user. #' Samples with fewer OTUs/ASVs than the specified `depth_level` are discarded. #' #' @param physeq_obj A `phyloseq` object containing an OTU/ASV table. @@ -23,14 +22,12 @@ #' Samples with less than `depth_level` sequences are discarded. #' #' -#' @importFrom parallelly availableCores -#' @importFrom parallel makeCluster stopCluster clusterExport parLapply -#' @importFrom parallel clusterEvalQ #' @importFrom dplyr group_by summarise across everything filter near #' @importFrom dplyr where #' @importFrom tibble rownames_to_column column_to_rownames #' @importFrom phyloseq otu_table -#' @importFrom cli cli_h1 cli_h2 cli_alert_info cli_alert_warning cli_alert_success cli_alert_danger +#' @importFrom cli cli_h1 cli_h2 cli_alert_info cli_alert_warning +#' @importFrom cli cli_alert_success cli_alert_danger #' @importFrom utils head #' @importFrom vegan rrarefy #' @@ -61,7 +58,7 @@ multi_rarefy <- function( set_seed = NULL ) { # Input validation ---- - cli::cli_h1("Multiple Rarefaction") + cli::cli_h1("Rarefaction iterations starting...") cli::cli_h2("Input Validation") if (!requireNamespace("phyloseq", quietly = TRUE)) { diff --git a/R/parallel_helpers.R b/R/parallel_helpers.R deleted file mode 100644 index 38997ea..0000000 --- a/R/parallel_helpers.R +++ /dev/null @@ -1,189 +0,0 @@ -#' Detect HPC environment type -#' -#' Detects if code is running in an HPC environment and identifies which scheduler. -#' -#' @return Character string identifying the HPC environment type: -#' "slurm", "pbs", "sge", "lsf", "condor", "generic_hpc", or "local" -#' -#' @examples -#' detect_hpc_type() -#' -#' @export -detect_hpc_type <- function() { - # Map of environment variables to HPC types - hpc_type_vars <- list( - slurm = c("SLURM_JOB_ID", "SLURM_CPUS_PER_TASK", "SLURM_JOB_NAME"), - pbs = c("PBS_JOBID", "PBS_NUM_PPN", "PBS_O_WORKDIR"), - sge = c("SGE_TASK_ID", "NSLOTS", "SGE_ROOT"), - lsf = c("LSF_JOBID", "LSB_DJOB_NUMPROC", "LSB_JOBID"), - condor = c("CONDOR_JOB_ID", "_CONDOR_JOB_AD", "CONDOR_SLOT") - ) - - # Generic HPC variables (not specific to a scheduler) - generic_vars <- c("OMP_NUM_THREADS", "JOB_ID", "HOSTNAME") - - # Check each HPC type - for (type in names(hpc_type_vars)) { - if ( - any(vapply( - hpc_type_vars[[type]], - function(var) { - val <- Sys.getenv(var, "") - !identical(val, "") - }, - FUN.VALUE = logical(1) - )) - ) { - return(type) - } - } - - # Check generic HPC indicators - if ( - any(vapply( - generic_vars, - function(var) { - val <- Sys.getenv(var, "") - !identical(val, "") - }, - FUN.VALUE = logical(1) - )) - ) { - return("generic_hpc") - } - - # Not in an HPC environment - return("local") -} - -#' Check if running in an HPC environment -#' -#' @return Logical indicating whether we're in any HPC environment -#' @examples -#' is_hpc_environment() -#' -#' @export -is_hpc_environment <- function() { - !identical(detect_hpc_type(), "local") -} - -#' Get the number of available CPU cores based on environment -#' -#' Determines the appropriate number of cores to use based on whether -#' we're in an HPC environment or a local machine. -#' -#' @param default Default number of cores to use if detection fails -#' @return Integer number of cores available -#' @examples -#' get_available_cores() -#' get_available_cores(default = 2) -#' -#' @importFrom parallelly availableCores makeClusterPSOCK -#' @importFrom parallel makeCluster stopCluster clusterExport parLapply -#' -#' @export -get_available_cores <- function(default = 1) { - if (is_hpc_environment()) { - # Check HPC-specific environment variables in a prioritized order - hpc_core_vars <- c( - "SLURM_CPUS_PER_TASK", # SLURM - "PBS_NUM_PPN", # PBS/TORQUE - "NSLOTS", # SGE - "LSB_DJOB_NUMPROC", # LSF - "OMP_NUM_THREADS" # OpenMP (generic) - ) - - # Try each variable in order - for (var in hpc_core_vars) { - core_str <- Sys.getenv(var, "") - if (core_str != "") { - cores <- suppressWarnings(as.integer(core_str) - 1L) # Keeping 1 core available - if (!is.na(cores) && cores > 0) { - cli::cli_alert_info( - "HPC environment detected. Using {cores} core(s)/worker(s) from {var}. - Keeping 1 core/worker available for system." - ) - return(cores) - } - } - } - - # If no specific HPC variable found, try parallelly if available - if (requireNamespace("parallelly", quietly = TRUE)) { - cores <- parallelly::availableCores() - 1L - cli::cli_alert_info( - "HPC environment detected. Using {cores} core(s)/worker(s) via parallelly::availableCores(). - Keeping 1 core/worker available for system." - ) - return(cores) - } - - # Last resort for HPC: use a safe default - cli::cli_alert_warning( - "HPC environment detected but could not determine core count. Using {default} core(s)." - ) - return(default) - } else { - # Local machine: use parallelly if available, otherwise safer detectCores - if (requireNamespace("parallelly", quietly = TRUE)) { - cores <- parallelly::availableCores() - 1L - cli::cli_alert_info( - "Local environment. Using {cores} core(s)/worker(s) via parallelly::availableCores(). - Keeping 1 core/worker available for system." - ) - return(cores) - } else { - # Safe local default: leave one core free for system - cores <- max(1, parallel::detectCores() - 1L) - if (is.na(cores)) { - cores <- default - } - cli::cli_alert_info( - "Local environment. Using {cores} core(s)/worker(s) via parallel::detectCores(). - Keeping 1 core/worker available for system." - ) - return(cores) - } - } -} - -#' Set up an appropriate parallel backend -#' -#' Creates a parallel cluster if in an HPC environment or returns -#' the number of cores to use if on a local machine. -#' -#' @param default Default number of cores to use if detection fails -#' @return Either a cluster object (HPC) or integer number of cores (local) -#' @examples -#' setup_parallel_backend() -#' -#' @importFrom parallelly availableCores makeClusterPSOCK -#' @importFrom parallel makeCluster stopCluster clusterExport parLapply -#' @export -setup_parallel_backend <- function(default = 1) { - if (is_hpc_environment()) { - if (requireNamespace("parallelly", quietly = TRUE)) { - # Create a PSOCK cluster with available workers - workers <- parallelly::availableWorkers() - cli::cli_alert_info( - "Creating HPC cluster with {length(workers) - 1L} core(s)/worker(s) via parallelly::makeClusterPSOCK(). - Keeping 1 core/worker available for system." - ) - cl <- parallelly::makeClusterPSOCK(length(workers) - 1L, autoStop = TRUE) # Keep one for other processes - return(cl) - } else { - # Fallback if parallelly is not available - cores <- get_available_cores(default) - cli::cli_alert_info( - "Creating HPC cluster with {cores - 1L} core(s)/worker(s) using parallel::makeCluster(). - Keeping 1 core/worker available for system." - ) - cl <- parallel::makeCluster(cores - 1L) - return(cl) - } - } else { - # Just return the number of cores for local processing - cores <- get_available_cores(default) - return(cores) - } -} diff --git a/R/plot_core_distribution.R b/R/plot_core_distribution.R index 4baabf8..8e09135 100644 --- a/R/plot_core_distribution.R +++ b/R/plot_core_distribution.R @@ -1,6 +1,6 @@ #' Plot Core Taxa Occupancy Across Metadata Groups #' -#' Creates a bar plot showing core taxa (i.e. OTUs/ASVs) occupancy patterns across +#' Creates a plot showing core taxa (i.e. OTUs/ASVs) occupancy patterns across #' a grouping variable. #' #' @param core_result A list object returned by \code{\link{identify_core}}, @@ -57,7 +57,6 @@ #' @importFrom ggplot2 theme_classic theme element_text element_line #' @importFrom ggplot2 element_blank labs geom_line geom_point facet_wrap #' @importFrom ggplot2 scale_y_continuous geom_tile -#' @importFrom ggsci scale_fill_npg #' @importFrom vegan decostand #' @importFrom grid unit #' diff --git a/R/plot_variance_propagation.R b/R/plot_variance_propagation.R index 0a47d66..d929e17 100644 --- a/R/plot_variance_propagation.R +++ b/R/plot_variance_propagation.R @@ -1,6 +1,6 @@ #' Variance propagation diagnostic for rarefaction #' -#' This function evaluate the variance generated during multiple rarefaction by plotting comparing raw vs. rarefied alpha diversity metrics calculated at each iterations. It is possible to plot observed richness (q=0), Shannon diversity (q=1), or Simpson diversity (q=2) by setting the `q` parameter to "richness" or `q` = 0, "shannon" or `q` = 1, or "shannon" or `q` = 2. The plot is faceted by method (raw vs rarefied) and colored by a specified grouping variable from the sample data. +#' This function evaluate the variance between rarefaction iterations from \code{multi_rarefy()} by visually comparing raw vs. rarefied alpha diversity metrics calculated at each iterations. It is possible to plot observed richness (q=0), Shannon diversity (q=1), or Simpson diversity (q=2) by setting the `q` parameter to "richness" or `q` = 0, "shannon" or `q` = 1, or "shannon" or `q` = 2. The plot is faceted by method (raw vs rarefied) and colored by a specified grouping variable from the sample data. #' #' @param physeq_obj Raw phyloseq object #' @param rarefied Output from multi_rarefy(). Either a list of dataframes or and array. @@ -10,7 +10,7 @@ #' @param convert_to_factor Logical. If \code{TRUE}, both \code{group_var} and #' \code{group_color} are coerced to \code{factor} before plotting, which is #' useful when those columns are numeric/continuous (e.g. dates, counts) but -#' should be treated as discrete groups. When \code{TRUE} a discrete colour +#' should be treated as discrete groups. When \code{TRUE} a discrete color #' scale (\code{scale_color_viridis_d}) is used; otherwise the continuous #' scale (\code{scale_color_viridis_c}) is used. Default \code{FALSE}. #' diff --git a/README.Rmd b/README.Rmd index 48ce392..75826f9 100644 --- a/README.Rmd +++ b/README.Rmd @@ -26,10 +26,11 @@ BRCore provides a unified framework for identification and ecological interpreta BRCore provides tools for: -**Rarefaction analysis**: Calculate pre-rarefaction metrics and perform multiple rarefactions +**Rarefaction analysis**: Calculate pre-rarefaction metrics and perform rarefactions - `add_rarefaction_metrics()` - `multi_rarefy()` +- `plot_variance_propagation()` **Core microbiome identification**: Identify core microbial taxa using abundance-occupancy distributions @@ -77,7 +78,7 @@ data("bcse", package = "BRCore") # Add rarefaction metrics bcse_metrics <- add_rarefaction_metrics(data = bcse) -# Perform multiple rarefaction +# Perform rarefaction bcse_rarefied_list <- multi_rarefy( physeq_obj = bcse, depth_level = 1000, @@ -86,7 +87,10 @@ bcse_rarefied_list <- multi_rarefy( ) # Update phyloseq object with rarefied data -bcse_rare_single <- update_otu_table(physeq_obj = bcse, rarefied_otus = bcse_rarefied_list, iteration = 2) # Your preffered iteration can be used here +bcse_rare_single <- update_otu_table( + physeq_obj = bcse, + rarefied_otus = bcse_rarefied_list, + iteration = 2) # Your preffered iteration can be used here # Identify core microbiome diff --git a/README.md b/README.md index 86a9a9f..d22f13e 100644 --- a/README.md +++ b/README.md @@ -24,10 +24,11 @@ robustness and reproducibility in microbiome data analysis. BRCore provides tools for: **Rarefaction analysis**: Calculate pre-rarefaction metrics and perform -multiple rarefactions +rarefactions - `add_rarefaction_metrics()` - `multi_rarefy()` +- `plot_variance_propagation()` **Core microbiome identification**: Identify core microbial taxa using abundance-occupancy distributions @@ -78,7 +79,7 @@ data("bcse", package = "BRCore") # Add rarefaction metrics bcse_metrics <- add_rarefaction_metrics(data = bcse) -# Perform multiple rarefaction +# Perform rarefaction bcse_rarefied_list <- multi_rarefy( physeq_obj = bcse, depth_level = 1000, @@ -87,7 +88,10 @@ bcse_rarefied_list <- multi_rarefy( ) # Update phyloseq object with rarefied data -bcse_rare_single <- update_otu_table(physeq_obj = bcse, rarefied_otus = bcse_rarefied_list, iteration = 2) # Your preffered iteration can be used here +bcse_rare_single <- update_otu_table( + physeq_obj = bcse, + rarefied_otus = bcse_rarefied_list, + iteration = 2) # Your preffered iteration can be used here # Identify core microbiome diff --git a/_pkgdown.yml b/_pkgdown.yml index 912024e..be2674b 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -46,6 +46,8 @@ reference: authors: Bolívar Aponte Rolón: href: "https://www.bolaponte.com/" + Gian Maria Niccolò Benucci: + href: "https://github.com/Gian77" Adina Howe: href: "https://www.germslab.org/about/" Center for Advanced Bioenergy and Bioproducts Innovation: diff --git a/man/detect_hpc_type.Rd b/man/detect_hpc_type.Rd deleted file mode 100644 index 4db427f..0000000 --- a/man/detect_hpc_type.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/parallel_helpers.R -\name{detect_hpc_type} -\alias{detect_hpc_type} -\title{Detect HPC environment type} -\usage{ -detect_hpc_type() -} -\value{ -Character string identifying the HPC environment type: -"slurm", "pbs", "sge", "lsf", "condor", "generic_hpc", or "local" -} -\description{ -Detects if code is running in an HPC environment and identifies which scheduler. -} -\examples{ -detect_hpc_type() - -} diff --git a/man/get_available_cores.Rd b/man/get_available_cores.Rd deleted file mode 100644 index a79e471..0000000 --- a/man/get_available_cores.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/parallel_helpers.R -\name{get_available_cores} -\alias{get_available_cores} -\title{Get the number of available CPU cores based on environment} -\usage{ -get_available_cores(default = 1) -} -\arguments{ -\item{default}{Default number of cores to use if detection fails} -} -\value{ -Integer number of cores available -} -\description{ -Determines the appropriate number of cores to use based on whether -we're in an HPC environment or a local machine. -} -\examples{ -get_available_cores() -get_available_cores(default = 2) - -} diff --git a/man/identify_core.Rd b/man/identify_core.Rd index d7a976c..276fa64 100644 --- a/man/identify_core.Rd +++ b/man/identify_core.Rd @@ -38,7 +38,8 @@ iteration.} sampling_date", "site") that is used for prioritizing the core microbiome.} \item{increase_value}{Increase value (numeric, scalar) used in the -calculation (default 0.02) for "increase". The "elbow" is always calculated and returned as \code{elbow_core} (see below for details).} +calculation (default 0.02) for "increase". The "elbow" is always calculated +and returned as \code{elbow_core} (see below for details).} \item{abundance_weight}{Numeric in \verb{[0,1]}; how much to weight mean relative abundance in the ranking score. \code{0} (default) uses occupancy/composite only. @@ -124,7 +125,7 @@ and \pkg{vegan}. \donttest{ library(phyloseq) library(BRCore) -# Example using your switchgrass phyloseq object and grouping variable +# Example using switchgrass phyloseq object and grouping variable # 'sampling_date' data("switchgrass", package = "BRCore") diff --git a/man/is_hpc_environment.Rd b/man/is_hpc_environment.Rd deleted file mode 100644 index 90ce9f5..0000000 --- a/man/is_hpc_environment.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/parallel_helpers.R -\name{is_hpc_environment} -\alias{is_hpc_environment} -\title{Check if running in an HPC environment} -\usage{ -is_hpc_environment() -} -\value{ -Logical indicating whether we're in any HPC environment -} -\description{ -Check if running in an HPC environment -} -\examples{ -is_hpc_environment() - -} diff --git a/man/multi_rarefy.Rd b/man/multi_rarefy.Rd index 472207b..d1f8de1 100644 --- a/man/multi_rarefy.Rd +++ b/man/multi_rarefy.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/multi_rarefy.R \name{multi_rarefy} \alias{multi_rarefy} -\title{Run multiple rarefaction for microbiome count tables} +\title{Run rarefaction for microbiome count tables} \usage{ multi_rarefy( physeq_obj, @@ -36,9 +36,8 @@ represent the average sequence counts calculated across all iterations. Samples with less than \code{depth_level} sequences are discarded. } \description{ -This function performs multiple rarefaction on a \code{phyloseq} object by randomly -sub-sampling OTUs/ASVs within samples without replacement. The process is -repeated for a specified number of iterations, and the results are averaged. +This function performs rarefaction on a \code{phyloseq} object by randomly +sub-sampling OTUs/ASVs within samples without replacement for a number of iterations specified by the user. Samples with fewer OTUs/ASVs than the specified \code{depth_level} are discarded. } \examples{ diff --git a/man/plot_core_distribution.Rd b/man/plot_core_distribution.Rd index 13d9bcf..755bc6d 100644 --- a/man/plot_core_distribution.Rd +++ b/man/plot_core_distribution.Rd @@ -40,7 +40,7 @@ using the increase method. A ggplot2 object that can be further customized. } \description{ -Creates a bar plot showing core taxa (i.e. OTUs/ASVs) occupancy patterns across +Creates a plot showing core taxa (i.e. OTUs/ASVs) occupancy patterns across a grouping variable. } \examples{ diff --git a/man/plot_variance_propagation.Rd b/man/plot_variance_propagation.Rd index 8409f5a..39f6f04 100644 --- a/man/plot_variance_propagation.Rd +++ b/man/plot_variance_propagation.Rd @@ -27,7 +27,7 @@ plot_variance_propagation( \item{convert_to_factor}{Logical. If \code{TRUE}, both \code{group_var} and \code{group_color} are coerced to \code{factor} before plotting, which is useful when those columns are numeric/continuous (e.g. dates, counts) but -should be treated as discrete groups. When \code{TRUE} a discrete colour +should be treated as discrete groups. When \code{TRUE} a discrete color scale (\code{scale_color_viridis_d}) is used; otherwise the continuous scale (\code{scale_color_viridis_c}) is used. Default \code{FALSE}.} } @@ -35,7 +35,7 @@ scale (\code{scale_color_viridis_c}) is used. Default \code{FALSE}.} ggplot object comparing raw vs rarefied diversity distributions across iterations. } \description{ -This function evaluate the variance generated during multiple rarefaction by plotting comparing raw vs. rarefied alpha diversity metrics calculated at each iterations. It is possible to plot observed richness (q=0), Shannon diversity (q=1), or Simpson diversity (q=2) by setting the \code{q} parameter to "richness" or \code{q} = 0, "shannon" or \code{q} = 1, or "shannon" or \code{q} = 2. The plot is faceted by method (raw vs rarefied) and colored by a specified grouping variable from the sample data. +This function evaluate the variance between rarefaction iterations from \code{multi_rarefy()} by visually comparing raw vs. rarefied alpha diversity metrics calculated at each iterations. It is possible to plot observed richness (q=0), Shannon diversity (q=1), or Simpson diversity (q=2) by setting the \code{q} parameter to "richness" or \code{q} = 0, "shannon" or \code{q} = 1, or "shannon" or \code{q} = 2. The plot is faceted by method (raw vs rarefied) and colored by a specified grouping variable from the sample data. } \examples{ \donttest{ diff --git a/man/setup_parallel_backend.Rd b/man/setup_parallel_backend.Rd deleted file mode 100644 index ebb2000..0000000 --- a/man/setup_parallel_backend.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/parallel_helpers.R -\name{setup_parallel_backend} -\alias{setup_parallel_backend} -\title{Set up an appropriate parallel backend} -\usage{ -setup_parallel_backend(default = 1) -} -\arguments{ -\item{default}{Default number of cores to use if detection fails} -} -\value{ -Either a cluster object (HPC) or integer number of cores (local) -} -\description{ -Creates a parallel cluster if in an HPC environment or returns -the number of cores to use if on a local machine. -} -\examples{ -setup_parallel_backend() - -} diff --git a/tests/testthat/test-vignette-workflow.R b/tests/testthat/test-vignette-workflow.R index d53238c..a3479f1 100644 --- a/tests/testthat/test-vignette-workflow.R +++ b/tests/testthat/test-vignette-workflow.R @@ -120,7 +120,7 @@ test_that("2: Vignette workflow produces consistent results", { test_vignette_data$test_rarefaction_plot$labels ) - # Step 4: Multiple rarefaction + # Step 4: Rarefaction bcse_rarefied_list <- multi_rarefy( physeq_obj = bcse, diff --git a/tests/testthat/test_sets/regenerate_test_data.R b/tests/testthat/test_sets/regenerate_test_data.R index c0c6bc9..b1da36c 100644 --- a/tests/testthat/test_sets/regenerate_test_data.R +++ b/tests/testthat/test_sets/regenerate_test_data.R @@ -27,7 +27,7 @@ test_bcse_metrics <- add_rarefaction_metrics(data = bcse) cat("Plotting rarefaction metrics...\n") test_rarefaction_plot <- plot_rarefaction_metrics(test_bcse_metrics) -# Step 4: Multiple rarefaction +# Step 4: Rarefaction # Use single thread for consistency with test expectations cat("Running multi_rarefy (this may take a while)...\n") test_bcse_rarefied_otutable <- multi_rarefy( diff --git a/vignettes/BRCore-vignette.R b/vignettes/BRCore-vignette.R index fd30b9d..97cc046 100644 --- a/vignettes/BRCore-vignette.R +++ b/vignettes/BRCore-vignette.R @@ -1,4 +1,4 @@ -## ----chunk setup, include = FALSE----------------------------------- +## ----chunk setup, include = FALSE----------------------------------------------------------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", @@ -35,7 +35,7 @@ options(cli.progress_show_after = Inf) # Disable progress bar for cleaner output ## text-align: justify; ## } -## ----load libraries, echo=TRUE-------------------------------------- +## ----load libraries, echo=TRUE-------------------------------------------------------------------------------------------------------------------------- invisible( lapply( c("BRCore", "phyloseq", "tidyverse", "viridis"), @@ -45,38 +45,38 @@ invisible( ) -## ----load bcse, echo=TRUE------------------------------------------- +## ----load bcse, echo=TRUE------------------------------------------------------------------------------------------------------------------------------- data("bcse", package = "BRCore") str(bcse) -## ----calculate metrics, echo=TRUE----------------------------------- +## ----calculate metrics, echo=TRUE----------------------------------------------------------------------------------------------------------------------- bcse_metrics <- add_rarefaction_metrics(data = bcse) bcse_metrics -## ----fig1_plot_metrics, echo=TRUE, fig.cap="Figure 1: Rarefaction metrics. a-b, histograms of samples. c, Good's coverage per total number of sequences in a sample. d-e, log10 of sequences in a sample. f, samples ranked by sequence reads in a sample."---- +## ----fig1_plot_metrics, echo=TRUE, fig.width=8, fig.height=6, fig.cap="Figure 1: Rarefaction metrics. a-b, histograms of samples. c, Good's coverage per total number of sequences in a sample. d-e, log10 of sequences in a sample. f, samples ranked by sequence reads in a sample."---- rarefaction_plot <- plot_rarefaction_metrics(bcse_metrics) print(rarefaction_plot) -## ----rarefy bcse, echo=TRUE----------------------------------------- +## ----rarefy bcse, echo=TRUE----------------------------------------------------------------------------------------------------------------------------- bcse_rarefied_list <- multi_rarefy( physeq_obj = bcse, depth_level = 1000, num_iter = 3, - .as_array = FALSE, + .as = "list", set_seed = 7642 ) -## ----verify success rarefaction, echo=TRUE-------------------------- +## ----verify success rarefaction, echo=TRUE-------------------------------------------------------------------------------------------------------------- class(bcse_rarefied_list) str(bcse_rarefied_list[[1]], list.len = 10) # Dimensions of iteration #1 -## ----fig2_rarefaction_variance, echo=TRUE, fig.cap="Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 10 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the 'Plot' variable, which is a nested variable within 'Crop'."---- +## ----fig2_rarefaction_variance, echo=TRUE, fig.cap="Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 3 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the 'Plot' variable, which is a nested variable within 'Crop'."---- rarefaction_variance_plot <- plot_variance_propagation( physeq_obj = bcse, rarefied = bcse_rarefied_list, @@ -87,7 +87,7 @@ rarefaction_variance_plot <- plot_variance_propagation( print(rarefaction_variance_plot) -## ----update_otu_table, echo=TRUE------------------------------------ +## ----update_otu_table, echo=TRUE------------------------------------------------------------------------------------------------------------------------ bcse_rare_single <- update_otu_table( physeq_obj = bcse, rarefied_otus = bcse_rarefied_list, @@ -95,7 +95,7 @@ bcse_rare_single <- update_otu_table( ) -## ----identify_core, echo=TRUE--------------------------------------- +## ----identify_core, echo=TRUE--------------------------------------------------------------------------------------------------------------------------- bcse_core_multi <- identify_core( physeq_obj = bcse, rarefied_list = bcse_rarefied_list, @@ -114,7 +114,7 @@ bcse_core_multi <- identify_core( # seed = 2134 # ) -## ----check the identified core, echo=TRUE--------------------------- +## ----check the identified core, echo=TRUE--------------------------------------------------------------------------------------------------------------- str(bcse_core_multi) @@ -126,10 +126,12 @@ bcse_identified_core <- plot_identified_core( increase_value = bcse_core_multi$increase_value ) -print(bcse_identified_core) +# plot_identified_core() outputs a list with a dataframe and a ggplot +print(bcse_identified_core$plot_identified_core) -## ----plot abundance occupany and increase core set, echo=TRUE------- + +## ----plot abundance occupany and increase core set, echo=TRUE------------------------------------------------------------------------------------------- ## ----fig4_plot_increase, echo=TRUE, fig.cap="Figure 4: Abundance-occupancy distribution for the 'bcse' dataset. The core ASV/OTUs identified by the last 2% increase method are highlighted in red."---- plot_abund_occ_increase <- plot_abundance_occupancy( @@ -160,7 +162,7 @@ plot_core_dist_bar <- plot_core_distribution( print(plot_core_dist_bar) -## ----plot_type line------------------------------------------------- +## ----plot_type line------------------------------------------------------------------------------------------------------------------------------------- ## ----fig7_plot_type_line, echo=TRUE, fig.width=7, fig.height=10, fig.cap="Figure 7: Occupancy of core ASV/OTUs across the 'Crop' variable. Each point represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A line plot is better than a bar plot but still not ideal for this many variable levels."---- plot_core_dist_line <- plot_core_distribution( @@ -172,7 +174,7 @@ plot_core_dist_line <- plot_core_distribution( print(plot_core_dist_line) -## ----reorder variable levels, echo=TRUE----------------------------- +## ----reorder variable levels, echo=TRUE----------------------------------------------------------------------------------------------------------------- bcse_core_multi$metadata <- bcse_core_multi$metadata %>% mutate( Crop = recode( @@ -206,7 +208,7 @@ bcse_core_multi$metadata <- bcse_core_multi$metadata %>% ) -## ----plot_type heatmap---------------------------------------------- +## ----plot_type heatmap---------------------------------------------------------------------------------------------------------------------------------- ## ----fig8_plot_type_heatmap, echo=TRUE, fig.cap="Figure 8: Occupancy of core ASV/OTUs across the 'Crop' variable. Each cell represents the average occupancy of core ASV/OTUs in samples belonging to each level of the 'Crop' variable. A heatmap is more compact and shows well enough the average occupancy across samples in each variable level."---- plot_core_dist_heatmap <- plot_core_distribution( @@ -220,7 +222,7 @@ plot_core_dist_heatmap <- plot_core_distribution( print(plot_core_dist_heatmap) -## ----fit neutral model, echo=TRUE----------------------------------- +## ----fit neutral model, echo=TRUE----------------------------------------------------------------------------------------------------------------------- bcse_core_multi_neutral_fit <- fit_neutral_model( otu_table = bcse_core_multi$otu_table, core_set = bcse_core_multi$increase_core, @@ -228,7 +230,7 @@ bcse_core_multi_neutral_fit <- fit_neutral_model( ) -## ----neutral fit result, echo=TRUE---------------------------------- +## ----neutral fit result, echo=TRUE---------------------------------------------------------------------------------------------------------------------- str(bcse_core_multi_neutral_fit) @@ -238,11 +240,19 @@ plot_bcse_neutral_fit <- plot_neutral_model(bcse_core_multi_neutral_fit) print(plot_bcse_neutral_fit) -## ----supplemental_info, echo=TRUE----------------------------------- -bcse_core_multi_iter1 <- identify_core( +## ----supplemental_info, echo=TRUE----------------------------------------------------------------------------------------------------------------------- +bcse_core_single_iter1 <- identify_core( physeq_obj = bcse_updated_rare, priority_var = "Crop", increase_value = 0.02, abundance_weight = 0, seed = 2135 ) + +bcse_identified_core_iter1 <- plot_identified_core( + bray_curtis_ranked = bcse_core_single_iter1$bray_curtis_ranked, + elbow = bcse_identified_core_iter1$elbow, + lastCall = bcse_identified_core_iter1$bc_increase, + increase_value = bcse_identified_core_iter1$increase_value +) +print(bcse_identified_core_iter1$plot_idenfied_core) diff --git a/vignettes/BRCore-vignette.Rmd b/vignettes/BRCore-vignette.Rmd index 752810a..0452231 100644 --- a/vignettes/BRCore-vignette.Rmd +++ b/vignettes/BRCore-vignette.Rmd @@ -1,7 +1,7 @@ --- title: "Introduction to BRCore" author: "By Bolívar Aponte Rolón and Gian Maria Niccolò Benucci" -date: "Last modified: 24 April, 2026" +date: "Last modified: 28 April, 2026" output: rmarkdown::html_vignette: toc: true @@ -62,7 +62,7 @@ The `BRCore` R package comes with a few datasets. Three 16S datasets: `switchgra `mimulus`, and `bean`, from @Shade2019Abundance; a 16S dataset, called `bcse`, from leaves of ten different cropping systems in the Bioenergy Crop Research Experiment from @Haan2023Contrasting. Here we are going to use `bcse` since it is not rarefied -in contrast of the other three. +in contrast to the other three. ``` r @@ -103,7 +103,7 @@ str(bcse) #> .. .. .. .. ..@ xp_list :List of 1 #> .. .. .. .. .. ..$ : #> .. .. .. .. ..@ .link_to_cached_object_list:List of 1 -#> .. .. .. .. .. ..$ : +#> .. .. .. .. .. ..$ : #> .. .. ..@ ranges :Formal class 'GroupedIRanges' [package "XVector"] with 7 slots #> .. .. .. .. ..@ group : int [1:2861] 1 1 1 1 1 1 1 1 1 1 ... #> .. .. .. .. ..@ start : int [1:2861] 106501 2501 63001 36751 501 19251 14251 37751 3751 8501 ... @@ -125,13 +125,13 @@ We are not going to talk about the importance of rarefaction as it is not the go of this vignette, but if you are interested you should read @Schloss2024Rarefaction, @Schloss2024Waste, @Hong2022Rarefy, and @Weiss2017Normalization as a start. -In `BRCore`, to identified the ideal rarefaction cutoff we can plot a simple series -of visuals to guide us in the decision. As we all know, we can, more or less, +In `BRCore`, to identify the ideal rarefaction cutoff we can plot a simple series +of visuals to guide us in the decision. We can, more or less, estimate how well a DNA sample will sequence, but there are always some samples that fail to sequence (or sequence badly) and some others that will sequence "too well" for reasons that are outside our control or by chance. -In the end we will need to decide how many samples we are accepting to discard and +In the end we will need to decide how many samples we are willing to discard and how much diversity we want to retain in our data. To do that we can use some help by plotting some diagnostics plots as shown below. @@ -152,7 +152,7 @@ bcse_metrics rarefaction_plot <- plot_rarefaction_metrics(bcse_metrics) #> ℹ Processing 50 samples #> ℹ Generating rarefaction diagnostic plots ✔ Rarefaction diagnostic plots generated successfully -#> ℹ Generating rarefaction diagnostic plots ✔ Generating rarefaction diagnostic plots [935ms] +#> ℹ Generating rarefaction diagnostic plots ✔ Generating rarefaction diagnostic plots [1.2s] print(rarefaction_plot) ``` @@ -176,21 +176,12 @@ coverage indicates many rare, potentially missed, taxa. ## Rarefy the data and explore variance propagation Originally developed by @Sanders1968Marine rarefaction has been adopted widely in -ecology and microbiome research. It consists on randomly re sampling, multiple times, -the same number of species (ASV/OTUs) across all samples, to calculate the expected -number of species that would be found if all the samples were all the same. It basically -prevents samples with more individuals (e.g., more sequencing reads) from appearing -more diverse simply because they were sampled more heavily. +ecology and microbiome research. It consists of randomly resampling the same number of species (ASV/OTUs) multiple times across all samples to estimate the expected number of species that would be observed if all samples were identical. It prevents samples with more individuals (e.g., more sequencing reads) from appearing +more diverse simply because they were sampled more in depth. -Because rarefaction is a random process it add noise to the data, each re sampling -is slightly different from others due to chance. This can be problematic, especially -when a very low rarefaction cutoff is selected, when for example we want to compare -2 treatments. This is due to the fact that the lower the rarefaction cutoff the more -chance you have that rarefaction iterations will differ one another as also -showed by @Cameron2021Rarefying. +Because rarefaction is a random process, it adds noise to the data. Each resampling event is slightly different from others due to chance. This can be problematic, especially when a very low rarefaction cutoff is selected, when, for example, we want to compare 2 treatments. With lower rarefaction cutoffs, the greater the chance you have that rarefaction iterations will differ from one another, as also shown by @Cameron2021Rarefying. -We developed functions that can perform and store all the rarefaction iterations -so to evaluate (and visualize) the generated noise in the process as show below +We developed functions that can perform and store all the rarefaction iterations to evaluate (and visualize) the noise generated in the process, as shown below: ``` r @@ -199,10 +190,51 @@ bcse_rarefied_list <- physeq_obj = bcse, depth_level = 1000, num_iter = 3, - .as_array = FALSE, + .as = "list", set_seed = 7642 ) -#> Error in multi_rarefy(physeq_obj = bcse, depth_level = 1000, num_iter = 3, : unused argument (.as_array = FALSE) +#> +#> ── Rarefaction iterations starting... ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────── +#> +#> ── Input Validation ── +#> +#> ✔ Input phyloseq object is valid! +#> ℹ Seed: 7642 +#> ℹ Input (matrix/df dim): 47 samples x 2861 taxa +#> ℹ Rarefaction depth: 1000 +#> ℹ Iterations: 3 +#> ℹ taxa_are_rows: TRUE +#> ℹ OTU matrix/df rownames head: bcse50, bcse69, bcse73, bcse191, bcse82, bcse102 +#> ℹ OTU matrix/df colnames head: OTU_427, OTU_11, OTU_253, OTU_148, OTU_3, OTU_78 +#> ℹ Row sums summary: Min=1193, Max=107643, Median=25209 +#> +#> ── Rarefaction Results ── +#> +#> ── Sample Removal +#> ! 3 samples removed (depth < 1000) +#> ! Samples removed: "bcse108, bcse105, bcse110" +#> +#> ── Taxa Removal +#> ✔ No taxa removed. +#> ! Taxa are not removed across iterations to maintain consistent dimensions. +#> Downstream analyses should handle zero-abundance taxa appropriately. +#> +#> ── Data Sparsity +#> ℹ Returning list of data frames for each iteration. +#> • Rarefied matrix (across 3 iterations): +#> • Min: 130570 zeros (97.1% sparsity) out of 134467 entries +#> • Max: 130663 zeros (97.17% sparsity) out of 134467 entries +#> • Avg: 130608 zeros (97.13% sparsity) out of 134467 entries +#> +#> ── Final Data Dimensions +#> ✔ Output: 3 iterations with 50 unique samples +#> • Samples per iteration: +#> • Min: 47 +#> • Max: 47 +#> • Non-zero taxa per iteration: +#> • Min: 1046 +#> • Max: 1098 +#> • Avg: 1073 ``` Let's explore the results. @@ -210,12 +242,23 @@ Let's explore the results. ``` r class(bcse_rarefied_list) -#> Error: object 'bcse_rarefied_list' not found +#> [1] "list" str(bcse_rarefied_list[[1]], list.len = 10) # Dimensions of iteration #1 -#> Error: object 'bcse_rarefied_list' not found +#> 'data.frame': 47 obs. of 2861 variables: +#> $ OTU_427 : int 0 0 0 0 0 0 0 0 0 0 ... +#> $ OTU_11 : int 0 0 0 0 0 0 0 0 0 0 ... +#> $ OTU_253 : int 0 0 0 0 0 0 0 0 0 0 ... +#> $ OTU_148 : int 0 1 0 11 2 0 0 0 0 78 ... +#> $ OTU_3 : int 261 86 651 272 249 114 192 73 96 320 ... +#> $ OTU_78 : int 0 0 0 0 0 0 0 0 0 0 ... +#> $ OTU_58 : int 0 0 0 0 0 0 0 0 0 0 ... +#> $ OTU_152 : int 0 0 0 0 0 0 0 0 0 0 ... +#> $ OTU_16 : int 2 0 0 1 1 0 0 1 0 0 ... +#> $ OTU_35 : int 0 0 0 0 0 0 0 0 0 0 ... +#> [list output truncated] ``` -And plot the variance (the noise we talked above) across the 10 iterations of +We can plot the variance (the noise we talked above) across the 3 iterations of rarefaction. We can use the `plot_variance_propagation()` function to visualize this variance in Alpha diversity across samples and iterations and compare Raw (non-rarefied) vs. Rarefied data. @@ -231,32 +274,37 @@ rarefaction_variance_plot <- plot_variance_propagation( ) #> ✔ Input phyloseq object is valid! #> -#> ── Rarefaction Variance Propagation Visualization ──────────────────── -#> Error: object 'bcse_rarefied_list' not found +#> ── Rarefaction Variance Propagation Visualization ──────────────────────────────────────────────────────────────────────────────────────────────────────── +#> ℹ Hill number order selected, q= 0 +#> ℹ Number of rarefaction iterations, n_iter= 3 +#> ℹ Comparison plot generated! print(rarefaction_variance_plot) -#> Error: object 'rarefaction_variance_plot' not found ``` +