Integration missing files

[ab4cp]

Hi, thanks so much for making this program it looks really promising. I had a couple of issues I was hoping to get help with.

My specifications are:
OS is ubuntu 20.04, linux 5.4
conda version 23.9.0
mamba version 1.5.3
python version 3.10.13.final.0

I downloaded cellsnake with no issues
installed the R packages and got the 'all packages are OK' message
and ran cellsnake standard data on the practice fetal-brain dataset provided in the workflow tutorial.
This is the path to the data files (cellsnake) unam@IP:~/Downloads/data$ ls
10X_17_028 10X_17_029

When I run 'cellsnake standard data'
(cellsnake) unam@IP:~/Downloads$ cellsnake standard data

It runs and I get some of the output files but then it terminates due to this following error. Any suggestions on how I can resolve this? Thanks!

Execution halted
[Fri Nov 3 15:52:34 2023]
Error in rule normalization_pca_rds:
jobid: 9
input: analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds
output: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds
shell:
/home/unam@IP/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-normalization-pca.R --rds analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds --doublet.filter --normalization.method LogNormalize --cpu 1 --scale.factor 10000 --reference BlueprintEncodeData --variable.selection.method vst --nfeature 2000 --resolution 0.8 --output.rds analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds --umap --tsne
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Exiting because a job execution failed. Look above for error message
Complete log: .snakemake/log/2023-11-03T153327.979328.snakemake.log
Traceback (most recent call last):
File "/home/#####/#####/miniforge3/envs/cellsnake/bin/cellsnake", line 10, in
sys.exit(main())
File "/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/command_line.py", line 379, in main
run_workflow(cli_arguments)
File "/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/command_line.py", line 351, in run_workflow
subprocess.check_call(str(snakemake_argument),shell=True)
File "/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/subprocess.py", line 373, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command 'snakemake --retries 5 --rerun-incomplete -k -j 1 -s /home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/Snakefile --config datafolder=data cellsnake_path=/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/ show_labels=True marker_plots_per_cluster_n=20 min_percentage_to_plot=5 reduction=cca highly_variable_features=2000 umap_markers_plot=True test_use=wilcox resolution=0.8 singler_ref=BlueprintEncodeData celltypist_model=Immune_All_Low.pkl mapping=org.Hs.eg.db min_features=200 percent_mt=10 max_molecules=Inf min_molecules=0 normalization_method=LogNormalize scale_factor=10000 metadata_column=condition confidence=0.05 organism=hsa percent_rp=0 variable_selection_method=vst doublet_filter=True species=human microbiome_min_features=3 metadata=None microbiome_min_cells=1 max_features=Inf logfc_threshold=0.25 tsne_markers_plot=False min_cells=3 min_hit_groups=4 taxa=genus dims=30 runid=haz29875 option=standard' returned non-zero exit status 1

I then attempted to integrate the data to see if it would run but I get the following output:

(cellsnake) unam:IP:~/Downloads$ cellsnake integrate data
{'10X_17_029', '10X_17_028'}
Building DAG of jobs...
Using shell: /usr/bin/bash
Provided cores: 1 (use --cores to define parallelism)
Rules claiming more threads will be scaled down.
Job stats:
job count

---

all 1
total 1

Select jobs to execute...

[Fri Nov 3 15:53:54 2023]
localrule all:
jobid: 0
reason: Rules with neither input nor output files are always executed.
resources: tmpdir=/tmp

[ab4cp]

I attempted to run it again with the snakemake pathway. Not surprisingly I got the same error. Here's the script below which might be more helpful than the one I previously posted.

(scrna-workflow) unam@IP:~/scrna-workflow$ snakemake -j 10 --config datafolder=data option=minimal
{'10X_17_028'}
Building DAG of jobs...
Using shell: /usr/bin/bash
Provided cores: 10
Rules claiming more threads will be scaled down.
Job stats:
job count

---

all 1
normalization_pca_rds 1
plot_dimplots 3
plot_selected_marker_plots_separately 2
plot_singler_celltype 1
plot_singler_dimplots 3
plot_some_metrics 1
plot_some_technicals 1
singler_annotation 1
total 14

Select jobs to execute...

[Sat Nov 4 11:35:23 2023]
rule normalization_pca_rds:
input: analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds
output: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds
jobid: 2
reason: Missing output files: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds
wildcards: sample=10X_17_028, percent_mt=10, resolution=0.8
threads: 5
resources: tmpdir=/tmp, mem_mb=5000, mem_mib=4769

Loading required package: optparse
Warning message:
package ‘optparse’ was built under R version 4.2.3
Loading required package: SingleR
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: ‘MatrixGenerics’

The following objects are masked from ‘package:matrixStats’:

colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:stats’:

IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

anyDuplicated, aperm, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

The following objects are masked from ‘package:base’:

expand.grid, I, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: ‘Biobase’

The following object is masked from ‘package:MatrixGenerics’:

rowMedians

The following objects are masked from ‘package:matrixStats’:

anyMissing, rowMedians

Warning messages:
1: package ‘SingleR’ was built under R version 4.2.3
2: package ‘matrixStats’ was built under R version 4.2.3
3: package ‘GenomicRanges’ was built under R version 4.2.3
4: package ‘S4Vectors’ was built under R version 4.2.3
5: package ‘IRanges’ was built under R version 4.2.3
6: package ‘GenomeInfoDb’ was built under R version 4.2.3
7: package ‘Biobase’ was built under R version 4.2.3
Loading required package: celldex

Attaching package: ‘celldex’

The following objects are masked from ‘package:SingleR’:

BlueprintEncodeData, DatabaseImmuneCellExpressionData,
HumanPrimaryCellAtlasData, ImmGenData, MonacoImmuneData,
MouseRNAseqData, NovershternHematopoieticData

Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.4 ✔ purrr 1.0.2
✔ tibble 3.2.1 ✔ dplyr 1.1.3
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.4 ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::collapse() masks IRanges::collapse()
✖ dplyr::combine() masks Biobase::combine(), BiocGenerics::combine()
✖ dplyr::count() masks matrixStats::count()
✖ dplyr::desc() masks IRanges::desc()
✖ tidyr::expand() masks S4Vectors::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::first() masks S4Vectors::first()
✖ dplyr::lag() masks stats::lag()
✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()
✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
✖ dplyr::rename() masks S4Vectors::rename()
✖ dplyr::slice() masks IRanges::slice()
Warning messages:
1: package ‘ggplot2’ was built under R version 4.2.3
2: package ‘tibble’ was built under R version 4.2.3
3: package ‘tidyr’ was built under R version 4.2.3
4: package ‘readr’ was built under R version 4.2.3
5: package ‘purrr’ was built under R version 4.2.3
6: package ‘dplyr’ was built under R version 4.2.3
7: package ‘stringr’ was built under R version 4.2.3
8: package ‘forcats’ was built under R version 4.2.3
Loading required package: Seurat
Attaching SeuratObject
Seurat v4 was just loaded with SeuratObject v5; disabling v5 assays and
validation routines, and ensuring assays work in strict v3/v4
compatibility mode

Attaching package: ‘Seurat’

The following object is masked from ‘package:SummarizedExperiment’:

Assays

Warning message:
package ‘Seurat’ was built under R version 4.2.3
Loading required package: patchwork
Warning message:
package ‘patchwork’ was built under R version 4.2.3
Traceback (most recent call last):
File "", line 1, in
ModuleNotFoundError: No module named 'cellsnake'
Warning messages:
1: In system("python -c 'import os; import cellsnake; print(os.path.dirname(cellsnake.file))'", :
running command 'python -c 'import os; import cellsnake; print(os.path.dirname(cellsnake.file))'' had status 1
2: In file(filename, "r", encoding = encoding) :
cannot open file '/scrna/workflow/scripts/scrna-functions.R': No such file or directory
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|======================================================================| 100%
PC_ 1
Positive: STMN2, MYT1L, NSG2, SYT1, CNTNAP2, CCSER1, RTN1, DSCAM, LRRC7, GAD1
DCC, NRXN3, CELF4, SOX11, MAPT, PTPRD, HECW1, GAD2, RBFOX1, FAM155A
PCSK1N, BCL11B, ADARB2, GRIA2, GATA3, LINC01210, SOX14, KIZ, OTX2-AS1, IGFBPL1
Negative: VIM, ZFP36L1, SPARC, HES1, CD99, TTYH1, B2M, CCN1, CREB5, PON2
HSPB1, SOX2, FOS, HES4, CLU, PTN, SLC1A3, TAGLN2, ZFP36L2, EGR1
CLIC1, RHOC, GPM6B, PLPP3, HMGB2, RCN1, SAMD4A, QKI, FABP7, PEA15
PC_ 2
Positive: HMGB2, NUSAP1, TOP2A, SOX2, CENPF, MKI67, SMC4, CREB5, PBK, UBE2C
CDK1, BIRC5, MAD2L1, NUF2, H2AFX, PTTG1, CKS2, PIMREG, TPX2, PRC1
DIAPH3, HMGN2, KIFC1, NDC80, SGO1, PANTR1, AURKB, GTSE1, CCNA2, CKAP2L
Negative: BGN, CYTOR, IGFBP7, CAVIN3, NDUFA4L2, PLXDC1, AC017002.5, COL4A1, FOXS1, TMEM204
RGS5, ITIH5, EPAS1, DLC1, OLFML3, TMEM173, VAMP5, S100A11, ITGA1, EDNRA
LINC02147, COL3A1, PPP1R14A, ENG, EVA1B, PRELP, ARHGAP29, MIR4435-2HG, ADGRF5, RASL12
PC_ 3
Positive: BCAN, NTRK2, CFAP126, EDNRB, FIBIN, GDPD2, PIFO, SPON1, SPARCL1, NDRG2
TTYH1, FGFBP3, HEPN1, GFAP, CLU, ARMC3, LRRC4C, MGST1, FABP7, PLP1
PMP2, CST3, SULF1, RSPH1, CPXM2, GLIS3, VWA3A, HAS2, ANKFN1, VCAM1
Negative: UBE2C, MKI67, TOP2A, CENPF, NUSAP1, AURKB, BIRC5, NUF2, GTSE1, TPX2
CDK1, PIMREG, CCNA2, HMGB2, CDC20, KIFC1, PBK, ASPM, CCNB2, SGO1
NDC80, PLK1, CDCA8, CKAP2L, PTTG1, SMC4, MAD2L1, PRC1, SGO2, TACC3
PC_ 4
Positive: NEUROD2, NEUROD6, FRMD4B, NFIB, TBR1, NFIA, SLA, SOX5, KCNQ3, SLC24A2
CACNA1E, NRN1, PPP1R1B, PPP2R2B, CACNA2D1, MPPED1, FSTL5, SLC4A10, EPHA5, GRIA1
FAM49A, EMX1, NELL2, RBFOX3, ANKS1B, NEUROD1, UNC5D, EPHA7, ARPP21, BCL11B
Negative: OTX2-AS1, OTX2, GATA3, LINC01210, GAD1, SOX14, ADARB2, LHX5-AS1, LHX5, GAD2
ASIC4, NRXN3, SV2C, KIZ, PAX7, PAX3, LHX1, TENM3, CACNA2D3, PBX3
RELN, LHX1-DT, AL033504.1, SAMD5, ZFPM2-AS1, CEP112, GATA2, TENM2, ZFHX3, ZNF385D
PC_ 5
Positive: SLCO2B1, SRGN, VSIR, RGS10, INPP5D, AIF1, SPP1, TYROBP, C1QB, LAPTM5
CCL4, PDGFB, RASGRP3, C1QC, CCL3, IL1B, C1QA, ARPC1B, CCL3L1, CCL4L2
HHEX, CD68, CD74, RGS1, LY86, CXCL8, TREM2, SYNGR2, ALOX5AP, TMSB4X
Negative: TENM2, GRID2, PRKG1, LRRC4C, CEP112, PLXDC1, CSMD1, MEG3, RORA, NTM
NRG3, NDUFA4L2, CCDC3, EDNRA, ARL6IP1, RGS5, FAM155A, CACNA1C, DLG2, GPC5
CADM2, ZNF385D, MDGA2, LRRTM4, GRM7, DAB1, NRXN1, FOXP2, DSCAM, RMST
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 6809
Number of edges: 239085

Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9127
Number of communities: 23
Elapsed time: 0 seconds
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
11:35:47 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
11:35:47 Read 6809 rows and found 23 numeric columns
11:35:47 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
11:35:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:35:47 Writing NN index file to temp file /tmp/RtmpDyhI2v/file38dab932e3acf7
11:35:47 Searching Annoy index using 1 thread, search_k = 3000
11:35:49 Annoy recall = 100%
11:35:50 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
11:35:51 Initializing from normalized Laplacian + noise (using irlba)
11:35:52 Commencing optimization for 500 epochs, with 280188 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:36:00 Optimization finished
Loading required package: DoubletFinder
Loading required package: fields
Loading required package: spam
Spam version 2.10-0 (2023-10-23) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: ‘spam’

The following object is masked from ‘package:stats4’:

mle

The following objects are masked from ‘package:base’:

backsolve, forwardsolve

Loading required package: viridisLite

Try help(fields) to get started.
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
[1] "Creating 2270 artificial doublets..."
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Finding variable genes..."
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Scaling data..."
Centering and scaling data matrix
|======================================================================| 100%
[1] "Running PCA..."
[1] "Calculating PC distance matrix..."
[1] "Computing pANN..."
[1] "Classifying doublets.."
Warning messages:
1: package ‘fields’ was built under R version 4.2.3
2: package ‘spam’ was built under R version 4.2.3
3: package ‘viridisLite’ was built under R version 4.2.3
4: package ‘KernSmooth’ was built under R version 4.2.3
Error in collect():
! Failed to collect lazy table.
Caused by error in db_collect():
! Arguments in ... must be used.
✖ Problematic argument:
• ..1 = Inf
ℹ Did you misspell an argument name?
Backtrace:
▆

1. ├─get(opt$reference)()
2. │ └─celldex:::.create_se(...)
3. │ └─celldex:::.ExperimentHub()
4. │ └─ExperimentHub::ExperimentHub()
5. │ └─AnnotationHub::.Hub(...)
6. │ └─AnnotationHub:::.create_cache(...)
7. │ └─BiocFileCache::BiocFileCache(cache = cache, ask = ask)
8. │ └─BiocFileCache:::.sql_create_db(bfc)
9. │ └─BiocFileCache:::.sql_validate_version(bfc)
10. │ └─BiocFileCache:::.sql_schema_version(bfc)
11. │ ├─base::tryCatch(...)
12. │ │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
13. │ └─tbl(src, "metadata") %>% collect(Inf)
14. ├─dplyr::collect(., Inf)
15. └─dbplyr:::collect.tbl_sql(., Inf)
16. ├─base::tryCatch(...)
17. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
18. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
19. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
20. └─dbplyr::db_collect(x$src$con, sql, n = n, warn_incomplete = warn_incomplete, ...)

21. └─rlang (local) `<fn>`()
    

22.   └─rlang:::check_dots(env, error, action, call)
    

23.     └─rlang:::action_dots(...)
    

24.       ├─base (local) try_dots(...)
    

25.       └─rlang (local) action(...)
    

Execution halted
[Sat Nov 4 11:36:51 2023]
Error in rule normalization_pca_rds:
jobid: 2
input: analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds
output: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds
shell:
workflow/scripts/scrna-normalization-pca.R --rds analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds --doublet.filter --normalization.method LogNormalize --cpu 5 --scale.factor 10000 --reference BlueprintEncodeData --variable.selection.method vst --nfeature 2000 --resolution 0.8 --output.rds analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds --umap --tsne
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Shutting down, this might take some time.
Exiting because a job execution failed. Look above for error message
Complete log: .snakemake/log/2023-11-04T113522.930249.snakemake.log
(scrna-workflow) unam@IP:~/scrna-workflow$

[sinanugur]

Hi,
Thanks for your interest in cellsnake.

First can you share the specifications of your computer. Second can you conda list your environment?

conda list

can you also type R and send me the sessionInfo().

I suspect some packages changed in the environment due to updates which may cause some issues.

Cheers,

[sinanugur]

I tested myself. Just as I suspected, this is related to an updated package. I will pinpoint the problematic one and offer you a solution. Meantime, I can suggest our Docker container. You do not have to reinstall anything and it is pretty much run in the same way.

https://hub.docker.com/r/sinanugur/cellsnake

Cheers,

[sinanugur]

I think I found the problematic package as explained here https://stackoverflow.com/questions/77370659/error-failed-to-collect-lazy-table-caused-by-error-in-db-collect-using

In short, just run this in your environment to downgrade the problematic package:

conda install r-dbplyr=2.3.2 -c conda-forge

Then run from the start. The problem was the prepocessing never finished so you cannot integrate. Therefore:

cellsnake standard data
cellsnake integrate data

Now the conclusion of standard part may take more time since the clustering will be done. I recommend using --jobs 5 argument to use multiprocessing.

Sorry for the inconvenience. I will fix the Bioconda recipe if this creates a problem for everyone before an update of that package.

Cheers,

[ab4cp]

Thanks for the help. I came across that bug as well and it's all up and running now!

[sinanugur]

@ab4cp No worries, please share if you notice any other bugs or problems. Cellsnake is based on Seurat4 and usually requires more resources if you want to integrate more than 50,000 cells and after 100,000 cells Seurat4 is not that reliable.

Seurat5 is better however the resource requirement still grows rapidly. I will push a new version on Seurat5. Unfortunately, high-performance computers are still required and laptops/desktops make single-cell analysis difficult.
Cheers,

[ab4cp]

@sinanugur is the seurat 5 version available? I am on linux with 32vCPU 2000RAM and 5TB of free disk space. I was going to trial cellsnake to integrate about 25 datasets the number of cells would easily be over 1M.

[ab4cp]

@sinanugur

On the fetal brain dataset I tried running:

cellsnake: cellsnake integrated advanced analyses_integrated/seurat/integrated.rds --resolution auto

It drops out at about 86% completion.

I'm not sure if this again is a related to an update in any packages.

##ERROR MESSAGE
🔎 No available models. Downloading...
Traceback (most recent call last):
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-celltypist.py", line 19, in
predictions = celltypist.annotate(data, model = model, majority_voting = True)
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/annotate.py", line 77, in annotate
lr_classifier = model if isinstance(model, Model) else Model.load(model)
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 90, in load
if model in get_all_models():
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 359, in get_all_models
download_if_required()
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 372, in download_if_required
download_models()
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 432, in download_models
models_json = get_models_index(force_update)
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 394, in get_models_index
return json.load(f)
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/init.py", line 293, in load
return loads(fp.read(),
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/init.py", line 346, in loads
return _default_decoder.decode(s)
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
[Sun Nov 5 21:57:49 2023]

Error in rule celltypist_celltype:
jobid: 11
input: analyses_integrated/h5ad/percent_mtauto/resolutionauto/integrated.h5ad
output: analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mtauto/resolutionauto/integrated/seurat_clusters, analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mtauto/resolutionauto/integrated/seurat_clusters/predicted_labels.csv, results_integrated/integrated/percent_mtauto/resolutionauto/celltypist/Immune_All_Low.pkl/plot_celltypist_dotplot-seurat_clusters.pdf, results_integrated/integrated/percent_mtauto/resolutionauto/celltypist/Immune_All_Low.pkl/table_cluster_annotation_table-seurat_clusters.xlsx
shell:
/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-celltypist.py analyses_integrated/h5ad/percent_mtauto/resolutionauto/integrated.h5ad results_integrated/integrated/percent_mtauto/resolutionauto/celltypist/Immune_All_Low.pkl/plot_celltypist_dotplot-seurat_clusters.pdf analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mtauto/resolutionauto/integrated/seurat_clusters results_integrated/integrated/percent_mtauto/resolutionauto/celltypist/Immune_All_Low.pkl/table_cluster_annotation_table-seurat_clusters.xlsx Immune_All_Low.pkl seurat_clusters
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Removing output files of failed job celltypist_celltype since they might be corrupted:
analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mtauto/resolutionauto/integrated/seurat_clusters
Trying to restart job 11.
Select jobs to execute...

My conda info/list and R sessionInfo() is below:
##CONDA INFO
(cellsnake) acboynes@rdl-b5qh9t:~$ conda info

 active environment : cellsnake
active env location : /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake
        shell level : 2
   user config file : /home/student.unimelb.edu.au/acboynes/.condarc

populated config files : /home/student.unimelb.edu.au/acboynes/miniforge3/.condarc
/home/student.unimelb.edu.au/acboynes/.condarc
conda version : 23.9.0
conda-build version : not installed
python version : 3.10.13.final.0
virtual packages : __archspec=1=zen2
__glibc=2.31=0
__linux=5.4.0=0
__unix=0=0
base environment : /home/student.unimelb.edu.au/acboynes/miniforge3 (writable)
conda av data dir : /home/student.unimelb.edu.au/acboynes/miniforge3/etc/conda
conda av metadata url : None
channel URLs : https://conda.anaconda.org/conda-forge/linux-64
https://conda.anaconda.org/conda-forge/noarch
package cache : /home/student.unimelb.edu.au/acboynes/miniforge3/pkgs
/home/student.unimelb.edu.au/acboynes/.conda/pkgs
envs directories : /home/student.unimelb.edu.au/acboynes/miniforge3/envs
/home/student.unimelb.edu.au/acboynes/.conda/envs
platform : linux-64
user-agent : conda/23.9.0 requests/2.31.0 CPython/3.10.13 Linux/5.4.0-122-generic ubuntu/20.04.4 glibc/2.31 solver/libmamba conda-libmamba-solver/23.11.0 libmambapy/1.5.3
UID:GID : 1512668138:1512668138
netrc file : None
offline mode : False

##CONDA LIST
(cellsnake) acboynes@rdl-b5qh9t:~$ conda list

packages in environment at /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake:

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_r-mutex 1.0.1 anacondar_1 conda-forge
aioeasywebdav 2.4.0 py39hf3d152e_1001 conda-forge
aiohttp 3.8.6 py39hd1e30aa_1 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
amply 0.1.6 pyhd8ed1ab_0 conda-forge
anndata 0.10.2 pyhd8ed1ab_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
argcomplete 3.1.4 pyhd8ed1ab_0 conda-forge
arpack 3.7.0 hdefa2d7_2 conda-forge
array-api-compat 1.4 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge
attmap 0.13.2 pyhd8ed1ab_0 conda-forge
attrs 23.1.0 pyh71513ae_1 conda-forge
backports 1.0 pyhd8ed1ab_3 conda-forge
backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge
bcrypt 4.0.1 py39h9fdd4d6_1 conda-forge
bedtools 2.31.0 hf5e1c6e_3 bioconda
binutils_impl_linux-64 2.40 hf600244_0 conda-forge
bioconductor-annotationdbi 1.60.0 r42hdfd78af_0 bioconda
bioconductor-annotationhub 3.6.0 r42hdfd78af_0 bioconda
bioconductor-batchelor 1.14.0 r42hf17093f_1 bioconda
bioconductor-beachmat 2.14.0 r42hf17093f_1 bioconda
bioconductor-biobase 2.58.0 r42ha9d7317_1 bioconda
bioconductor-biocfilecache 2.6.0 r42hdfd78af_0 bioconda
bioconductor-biocgenerics 0.44.0 r42hdfd78af_0 bioconda
bioconductor-biocneighbors 1.16.0 r42hf17093f_1 bioconda
bioconductor-biocparallel 1.32.5 r42hf17093f_1 bioconda
bioconductor-biocsingular 1.14.0 r42hf17093f_1 bioconda
bioconductor-biocversion 3.16.0 r42hdfd78af_0 bioconda
bioconductor-biostrings 2.66.0 r42ha9d7317_1 bioconda
bioconductor-celldex 1.8.0 r42hdfd78af_0 bioconda
bioconductor-clusterprofiler 4.6.0 r42hdfd78af_0 bioconda
bioconductor-complexheatmap 2.14.0 r42hdfd78af_0 bioconda
bioconductor-data-packages 20230718 hdfd78af_1 bioconda
bioconductor-delayedarray 0.24.0 r42ha9d7317_1 bioconda
bioconductor-delayedmatrixstats 1.20.0 r42hdfd78af_0 bioconda
bioconductor-dose 3.24.0 r42hdfd78af_0 bioconda
bioconductor-enhancedvolcano 1.16.0 r42hdfd78af_0 bioconda
bioconductor-enrichplot 1.18.0 r42hdfd78af_0 bioconda
bioconductor-experimenthub 2.6.0 r42hdfd78af_0 bioconda
bioconductor-fgsea 1.24.0 r42hf17093f_1 bioconda
bioconductor-genomeinfodb 1.34.9 r42hdfd78af_0 bioconda
bioconductor-genomeinfodbdata 1.2.9 r42hdfd78af_0 bioconda
bioconductor-genomicranges 1.50.0 r42ha9d7317_1 bioconda
bioconductor-ggtree 3.6.0 r42hdfd78af_0 bioconda
bioconductor-go.db 3.16.0 r42hdfd78af_0 bioconda
bioconductor-gosemsim 2.24.0 r42hf17093f_1 bioconda
bioconductor-graph 1.76.0 r42ha9d7317_1 bioconda
bioconductor-hdo.db 0.99.1 r42hdfd78af_0 bioconda
bioconductor-interactivedisplaybase 1.36.0 r42hdfd78af_0 bioconda
bioconductor-iranges 2.32.0 r42ha9d7317_1 bioconda
bioconductor-keggrest 1.38.0 r42hdfd78af_0 bioconda
bioconductor-limma 3.54.0 r42ha9d7317_1 bioconda
bioconductor-matrixgenerics 1.10.0 r42hdfd78af_0 bioconda
bioconductor-miqc 1.6.0 r42hdfd78af_0 bioconda
bioconductor-org.hs.eg.db 3.16.0 r42hdfd78af_0 bioconda
bioconductor-qvalue 2.30.0 r42hdfd78af_0 bioconda
bioconductor-residualmatrix 1.8.0 r42hdfd78af_0 bioconda
bioconductor-rhdf5lib 1.20.0 r42ha9d7317_2 bioconda
bioconductor-s4vectors 0.36.0 r42ha9d7317_1 bioconda
bioconductor-scaledmatrix 1.6.0 r42hdfd78af_0 bioconda
bioconductor-scater 1.26.0 r42hdfd78af_0 bioconda
bioconductor-scuttle 1.8.0 r42hf17093f_1 bioconda
bioconductor-singlecellexperiment 1.20.0 r42hdfd78af_0 bioconda
bioconductor-singler 2.0.0 r42hf17093f_1 bioconda
bioconductor-sparsematrixstats 1.10.0 r42hf17093f_1 bioconda
bioconductor-summarizedexperiment 1.28.0 r42hdfd78af_0 bioconda
bioconductor-topgo 2.50.0 r42hdfd78af_0 bioconda
bioconductor-treeio 1.22.0 r42hdfd78af_0 bioconda
bioconductor-xvector 0.38.0 r42ha9d7317_1 bioconda
bioconductor-zlibbioc 1.44.0 r42ha9d7317_2 bioconda
blast 2.14.1 pl5321h6f7f691_0 bioconda
blosc 1.21.5 h0f2a231_0 conda-forge
boost-cpp 1.78.0 h5adbc97_2 conda-forge
boto3 1.28.77 pyhd8ed1ab_0 conda-forge
botocore 1.31.77 pyhd8ed1ab_0 conda-forge
brotli 1.1.0 hd590300_1 conda-forge
brotli-bin 1.1.0 hd590300_1 conda-forge
brotli-python 1.1.0 py39h3d6467e_1 conda-forge
bwidget 1.9.14 ha770c72_1 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.21.0 hd590300_0 conda-forge
ca-certificates 2023.7.22 hbcca054_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 5.3.2 pyhd8ed1ab_0 conda-forge
cairo 1.16.0 ha61ee94_1014 conda-forge
cellsnake 0.2.0.11 pyh7cba7a3_0 bioconda
celltypist 1.3.0 pyhdfd78af_0 bioconda
certifi 2023.7.22 pyhd8ed1ab_0 conda-forge
cffi 1.16.0 py39h7a31438_0 conda-forge
cfitsio 4.2.0 hd9d235c_0 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
click 8.1.7 unix_pyh707e725_0 conda-forge
cmake 3.25.2 h077f3f9_0 conda-forge
coin-or-cbc 2.10.10 h9002f0b_0 conda-forge
coin-or-cgl 0.60.7 h516709c_0 conda-forge
coin-or-clp 1.17.8 h1ee7a9c_0 conda-forge
coin-or-osi 0.108.8 ha2443b9_0 conda-forge
coin-or-utils 2.11.9 hee58242_0 conda-forge
coincbc 2.10.10 0_metapackage conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
configargparse 1.7 pyhd8ed1ab_0 conda-forge
connection_pool 0.0.3 pyhd3deb0d_0 conda-forge
contourpy 1.1.1 py39h7633fee_1 conda-forge
cryptography 39.0.0 py39hd598818_0 conda-forge
curl 7.87.0 h6312ad2_0 conda-forge
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
cython 3.0.5 py39h3d6467e_0 conda-forge
datrie 0.8.2 py39hd1e30aa_7 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
docopt 0.6.2 py_1 conda-forge
docutils 0.20.1 py39hf3d152e_2 conda-forge
dpath 2.1.6 pyha770c72_0 conda-forge
dropbox 11.36.2 pyhd8ed1ab_0 conda-forge
eido 0.2.1 pyhd8ed1ab_0 conda-forge
entrez-direct 16.2 he881be0_1 bioconda
et_xmlfile 1.1.0 pyhd8ed1ab_0 conda-forge
exceptiongroup 1.1.3 pyhd8ed1ab_0 conda-forge
expat 2.5.0 hcb278e6_1 conda-forge
filechunkio 1.8 py_2 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.14.2 h14ed4e7_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.43.1 py39hd1e30aa_0 conda-forge
freetype 2.12.1 h267a509_2 conda-forge
freexl 1.0.6 h166bdaf_1 conda-forge
fribidi 1.0.10 h36c2ea0_0 conda-forge
frozenlist 1.4.0 py39hd1e30aa_1 conda-forge
ftputil 5.0.4 pyhd8ed1ab_0 conda-forge
fuzzywuzzy 0.18.0 pyhd8ed1ab_0 conda-forge
gcc_impl_linux-64 13.2.0 h338b0a0_2 conda-forge
geos 3.11.1 h27087fc_0 conda-forge
geotiff 1.7.1 ha76d385_4 conda-forge
gettext 0.21.1 h27087fc_0 conda-forge
gfortran_impl_linux-64 13.2.0 h76e1118_2 conda-forge
giflib 5.2.1 h0b41bf4_3 conda-forge
gitdb 4.0.11 pyhd8ed1ab_0 conda-forge
gitpython 3.1.40 pyhd8ed1ab_0 conda-forge
glpk 5.0 h445213a_0 conda-forge
gmp 6.2.1 h58526e2_0 conda-forge
google-api-core 2.12.0 pyhd8ed1ab_0 conda-forge
google-api-python-client 2.106.0 pyhd8ed1ab_0 conda-forge
google-auth 2.23.4 pyhca7485f_0 conda-forge
google-auth-httplib2 0.1.1 pyhd8ed1ab_0 conda-forge
google-cloud-core 2.3.3 pyhd8ed1ab_0 conda-forge
google-cloud-storage 2.13.0 pyhca7485f_0 conda-forge
google-crc32c 1.1.2 py39h328ec2c_5 conda-forge
google-resumable-media 2.6.0 pyhd8ed1ab_0 conda-forge
googleapis-common-protos 1.61.0 pyhd8ed1ab_0 conda-forge
graphite2 1.3.13 h58526e2_1001 conda-forge
grpcio 1.46.3 py39h0f497a6_0 conda-forge
gsl 2.7 he838d99_0 conda-forge
gxx_impl_linux-64 13.2.0 h338b0a0_2 conda-forge
h5py 3.7.0 nompi_py39h817c9c5_102 conda-forge
harfbuzz 6.0.0 h8e241bc_0 conda-forge
hdf4 4.2.15 h9772cbc_5 conda-forge
hdf5 1.12.2 nompi_h2386368_101 conda-forge
htslib 1.17 h6bc39ce_1 bioconda
httplib2 0.22.0 pyhd8ed1ab_0 conda-forge
humanfriendly 10.0 pyhd8ed1ab_6 conda-forge
icu 70.1 h27087fc_0 conda-forge
idna 3.4 pyhd8ed1ab_0 conda-forge
igraph 0.10.3 hb9ddf80_0 conda-forge
importlib-metadata 6.8.0 pyha770c72_0 conda-forge
importlib_metadata 6.8.0 hd8ed1ab_0 conda-forge
importlib_resources 6.1.0 pyhd8ed1ab_0 conda-forge
iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge
jinja2 3.1.2 pyhd8ed1ab_1 conda-forge
jmespath 1.0.1 pyhd8ed1ab_0 conda-forge
joblib 1.3.2 pyhd8ed1ab_0 conda-forge
jpeg 9e h0b41bf4_3 conda-forge
jq 1.5 0 bioconda
json-c 0.16 hc379101_0 conda-forge
jsonschema 4.19.2 pyhd8ed1ab_0 conda-forge
jsonschema-specifications 2023.7.1 pyhd8ed1ab_0 conda-forge
jupyter_core 5.5.0 py39hf3d152e_0 conda-forge
kaleido-core 0.2.1 h3644ca4_0 conda-forge
kealib 1.4.15 ha7026e8_1 conda-forge
kernel-headers_linux-64 2.6.32 he073ed8_16 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
kiwisolver 1.4.5 py39h7633fee_1 conda-forge
kraken2 2.1.3 pl5321hdcf5f25_0 bioconda
krb5 1.20.1 hf9c8cef_0 conda-forge
lcms2 2.14 h6ed2654_0 conda-forge
ld_impl_linux-64 2.40 h41732ed_0 conda-forge
leidenalg 0.9.1 py39h227be39_0 conda-forge
lerc 4.0.0 h27087fc_0 conda-forge
levenshtein 0.23.0 py39h3d6467e_0 conda-forge
libabseil 20230802.1 cxx17_h59595ed_0 conda-forge
libaec 1.1.2 h59595ed_1 conda-forge
libblas 3.9.0 16_linux64_openblas conda-forge
libbrotlicommon 1.1.0 hd590300_1 conda-forge
libbrotlidec 1.1.0 hd590300_1 conda-forge
libbrotlienc 1.1.0 hd590300_1 conda-forge
libcblas 3.9.0 16_linux64_openblas conda-forge
libcrc32c 1.1.2 h9c3ff4c_0 conda-forge
libcurl 7.87.0 h6312ad2_0 conda-forge
libdeflate 1.13 h166bdaf_0 conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 h516909a_1 conda-forge
libexpat 2.5.0 hcb278e6_1 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc 7.2.0 h69d50b8_2 conda-forge
libgcc-devel_linux-64 13.2.0 ha9c7c90_2 conda-forge
libgcc-ng 13.2.0 h807b86a_2 conda-forge
libgdal 3.6.0 ha189470_7 conda-forge
libgfortran-ng 13.2.0 h69a702a_2 conda-forge
libgfortran5 13.2.0 ha4646dd_2 conda-forge
libglib 2.78.0 hebfc3b9_0 conda-forge
libgomp 13.2.0 h807b86a_2 conda-forge
libhwloc 2.9.1 hd6dc26d_0 conda-forge
libiconv 1.17 h166bdaf_0 conda-forge
libidn2 2.3.4 h166bdaf_0 conda-forge
libkml 1.3.0 h01aab08_1016 conda-forge
liblapack 3.9.0 16_linux64_openblas conda-forge
liblapacke 3.9.0 16_linux64_openblas conda-forge
libllvm11 11.1.0 he0ac6c6_5 conda-forge
libnetcdf 4.8.1 nompi_h261ec11_106 conda-forge
libnghttp2 1.51.0 hdcd2b5c_0 conda-forge
libnsl 2.0.1 hd590300_0 conda-forge
libopenblas 0.3.21 pthreads_h78a6416_3 conda-forge
libpng 1.6.39 h753d276_0 conda-forge
libpq 14.5 h2baec63_5 conda-forge
libprotobuf 4.24.4 hf27288f_0 conda-forge
librttopo 1.1.0 ha49c73b_12 conda-forge
libsanitizer 13.2.0 h7e041cc_2 conda-forge
libsodium 1.0.18 h36c2ea0_1 conda-forge
libspatialite 5.0.1 h7c8129e_22 conda-forge
libsqlite 3.44.0 h2797004_0 conda-forge
libssh2 1.10.0 haa6b8db_3 conda-forge
libstdcxx-devel_linux-64 13.2.0 ha9c7c90_2 conda-forge
libstdcxx-ng 13.2.0 h7e041cc_2 conda-forge
libtiff 4.4.0 h0e0dad5_3 conda-forge
libudunits2 2.2.28 h40f5838_3 conda-forge
libunistring 0.9.10 h7f98852_0 conda-forge
libuuid 2.38.1 h0b41bf4_0 conda-forge
libuv 1.46.0 hd590300_0 conda-forge
libv8 8.9.83 h7465d70_2 conda-forge
libwebp-base 1.3.2 hd590300_0 conda-forge
libxcb 1.13 h7f98852_1004 conda-forge
libxml2 2.10.3 hca2bb57_4 conda-forge
libzip 1.9.2 hc869a4a_1 conda-forge
libzlib 1.2.13 hd590300_5 conda-forge
llvmlite 0.39.1 py39h7d9a04d_1 conda-forge
logmuse 0.2.6 pyh8c360ce_0 conda-forge
lz4-c 1.9.4 hcb278e6_0 conda-forge
make 4.3 hd18ef5c_1 conda-forge
markdown-it-py 3.0.0 pyhd8ed1ab_0 conda-forge
markupsafe 2.1.3 py39hd1e30aa_1 conda-forge
mathjax 2.7.7 ha770c72_3 conda-forge
matplotlib-base 3.6.3 py39he190548_0 conda-forge
mdurl 0.1.0 pyhd8ed1ab_0 conda-forge
metis 5.1.0 h59595ed_1007 conda-forge
mpfr 4.2.1 h9458935_0 conda-forge
multidict 6.0.4 py39hd1e30aa_1 conda-forge
munkres 1.0.7 py_1 bioconda
natsort 8.4.0 pyhd8ed1ab_0 conda-forge
nbformat 5.9.2 pyhd8ed1ab_0 conda-forge
ncbi-vdb 3.0.0 pl5321h87f3376_0 bioconda
ncurses 6.4 h59595ed_2 conda-forge
networkx 3.2 pyhd8ed1ab_0 conda-forge
nlopt 2.7.1 py39h863de18_3 conda-forge
nose 1.3.7 py_1006 conda-forge
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r-sass 0.4.7 r42ha503ecb_0 conda-forge
r-scales 1.2.1 r42hc72bb7e_2 conda-forge
r-scattermore 1.2 r42ha503ecb_1 conda-forge
r-scatterpie 0.2.1 r42hc72bb7e_1 conda-forge
r-sctransform 0.4.1 r42h08d816e_0 conda-forge
r-selectr 0.4_2 r42hc72bb7e_3 conda-forge
r-seurat 4.3.0 r42ha503ecb_1 conda-forge
r-seuratobject 5.0.0 r42ha503ecb_0 conda-forge
r-sf 1.0_7 r42h282678f_6 conda-forge
r-shadowtext 0.1.2 r42hc72bb7e_2 conda-forge
r-shape 1.4.6 r42ha770c72_2 conda-forge
r-shiny 1.7.5.1 r42h785f33e_0 conda-forge
r-sitmo 2.0.2 r42ha503ecb_2 conda-forge
r-slam 0.1_50 r42h1df0287_3 conda-forge
r-slider 0.3.1 r42ha503ecb_0 conda-forge
r-snow 0.4_4 r42hc72bb7e_2 conda-forge
r-sourcetools 0.1.7_1 r42ha503ecb_1 conda-forge
r-sp 2.1_1 r42h57805ef_0 conda-forge
r-spam 2.10_0 r42h9f9f741_0 conda-forge
r-sparsem 1.81 r42h61816a4_2 conda-forge
r-spatstat.data 3.0_3 r42hc72bb7e_0 conda-forge
r-spatstat.explore 3.2_5 r42h57805ef_0 conda-forge
r-spatstat.geom 3.2_7 r42h57805ef_0 conda-forge
r-spatstat.random 3.2_1 r42ha503ecb_0 conda-forge
r-spatstat.sparse 3.0_3 r42h57805ef_0 conda-forge
r-spatstat.utils 3.0_4 r42h57805ef_0 conda-forge
r-spdata 2.3.0 r42hc72bb7e_0 conda-forge
r-spdep 1.2_8 r42h57805ef_1 conda-forge
r-speedglm 0.3_5 r42hc72bb7e_1 conda-forge
r-spelling 2.2.1 r42hc72bb7e_1 conda-forge
r-squarem 2021.1 r42hc72bb7e_2 conda-forge
r-statmod 1.5.0 r42hd8f1df9_1 conda-forge
r-stringi 1.7.12 r42h1ae9187_0 conda-forge
r-stringr 1.5.0 r42h785f33e_1 conda-forge
r-survival 3.5_7 r42h57805ef_0 conda-forge
r-sys 3.4.2 r42h57805ef_1 conda-forge
r-systemfonts 1.0.5 r42haf97adc_0 conda-forge
r-tensor 1.5 r42hc72bb7e_1005 conda-forge
r-terra 1.5_21 r42h0f53b7d_8 conda-forge
r-testthat 3.2.0 r42ha503ecb_0 conda-forge
r-textshaping 0.3.6 r42hbb20487_4 conda-forge
r-tibble 3.2.1 r42h57805ef_2 conda-forge
r-tidygraph 1.2.3 r42ha503ecb_1 conda-forge
r-tidyr 1.3.0 r42ha503ecb_1 conda-forge
r-tidyselect 1.2.0 r42hc72bb7e_1 conda-forge
r-tidyseurat 0.7.4 r42hc72bb7e_0 conda-forge
r-tidytree 0.4.5 r42hc72bb7e_0 conda-forge
r-tidyverse 1.3.2 r42hc72bb7e_1 conda-forge
r-timechange 0.2.0 r42ha503ecb_1 conda-forge
r-timedate 4022.108 r42hc72bb7e_1 conda-forge
r-tinytex 0.48 r42hc72bb7e_1 conda-forge
r-ttservice 0.4.0 r42hc72bb7e_0 conda-forge
r-tweenr 2.0.2 r42ha503ecb_2 conda-forge
r-tzdb 0.4.0 r42ha503ecb_1 conda-forge
r-units 0.8_4 r42ha503ecb_0 conda-forge
r-utf8 1.2.4 r42h57805ef_0 conda-forge
r-uuid 1.1_1 r42h57805ef_0 conda-forge
r-uwot 0.1.16 r42ha503ecb_0 conda-forge
r-v8 4.3.0 r42h3877ea8_0 conda-forge
r-vctrs 0.6.4 r42ha503ecb_0 conda-forge
r-vipor 0.4.5 r42hc72bb7e_1005 conda-forge
r-viridis 0.6.4 r42hc72bb7e_0 conda-forge
r-viridislite 0.4.2 r42hc72bb7e_1 conda-forge
r-vroom 1.6.4 r42ha503ecb_0 conda-forge
r-waldo 0.5.2 r42hc72bb7e_0 conda-forge
r-warp 0.2.1 r42h57805ef_0 conda-forge
r-withr 2.5.2 r42hc72bb7e_0 conda-forge
r-wk 0.9.0 r42ha503ecb_0 conda-forge
r-xfun 0.41 r42ha503ecb_0 conda-forge
r-xml 3.99_0.14 r42hb43fdd4_0 conda-forge
r-xml2 1.3.3 r42h044e5c7_2 conda-forge
r-xtable 1.8_4 r42hc72bb7e_5 conda-forge
r-yaml 2.3.7 r42h57805ef_1 conda-forge
r-yulab.utils 0.1.0 r42hc72bb7e_0 conda-forge
r-zip 2.3.0 r42h57805ef_1 conda-forge
r-zoo 1.8_12 r42h57805ef_1 conda-forge
rapidfuzz 3.4.0 py39h3d6467e_0 conda-forge
readline 8.2 h8228510_1 conda-forge
referencing 0.30.2 pyhd8ed1ab_0 conda-forge
regex 2023.10.3 py39hd1e30aa_0 conda-forge
requests 2.31.0 pyhd8ed1ab_0 conda-forge
reretry 0.11.8 pyhd8ed1ab_0 conda-forge
rhash 1.4.3 hd590300_2 conda-forge
rich 13.6.0 pyhd8ed1ab_0 conda-forge
rpds-py 0.10.6 py39h9fdd4d6_0 conda-forge
rsa 4.9 pyhd8ed1ab_0 conda-forge
s3transfer 0.7.0 pyhd8ed1ab_0 conda-forge
samtools 1.18 hd87286a_0 bioconda
scanpy 1.9.5 pyhd8ed1ab_0 conda-forge
scikit-learn 1.2.2 py39hd189fd4_1 conda-forge
scipy 1.10.1 py39h7360e5f_0 conda-forge
seaborn 0.13.0 hd8ed1ab_0 conda-forge
seaborn-base 0.13.0 pyhd8ed1ab_0 conda-forge
sed 4.8 he412f7d_0 conda-forge
session-info 1.0.0 pyhd8ed1ab_0 conda-forge
setuptools 68.2.2 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
slacker 0.14.0 py_0 conda-forge
smart_open 6.4.0 pyhd8ed1ab_0 conda-forge
smmap 5.0.0 pyhd8ed1ab_0 conda-forge
snakemake 7.32.4 hdfd78af_1 bioconda
snakemake-minimal 7.32.4 pyhdfd78af_1 bioconda
snappy 1.1.10 h9fff704_0 conda-forge
sqlite 3.44.0 h2c6b66d_0 conda-forge
statsmodels 0.13.5 py39h2ae25f5_2 conda-forge
stdlib-list 0.8.0 pyhd8ed1ab_0 conda-forge
stone 3.3.1 pyhd8ed1ab_0 conda-forge
stopit 1.1.2 py_0 conda-forge
suitesparse 5.10.1 h9e50725_1 conda-forge
sysroot_linux-64 2.12 he073ed8_16 conda-forge
tabulate 0.9.0 pyhd8ed1ab_1 conda-forge
tbb 2021.9.0 hf52228f_0 conda-forge
tenacity 8.2.3 pyhd8ed1ab_0 conda-forge
texttable 1.7.0 pyhd8ed1ab_0 conda-forge
threadpoolctl 3.2.0 pyha21a80b_0 conda-forge
throttler 1.2.2 pyhd8ed1ab_0 conda-forge
tiledb 2.11.3 h1e4a385_1 conda-forge
tk 8.6.13 h2797004_0 conda-forge
tktable 2.10 h0c5db8f_5 conda-forge
toml 0.10.2 pyhd8ed1ab_0 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
tomlkit 0.12.2 pyha770c72_0 conda-forge
toposort 1.10 pyhd8ed1ab_0 conda-forge
tqdm 4.66.1 pyhd8ed1ab_0 conda-forge
traitlets 5.13.0 pyhd8ed1ab_0 conda-forge
typing-extensions 4.8.0 hd8ed1ab_0 conda-forge
typing_extensions 4.8.0 pyha770c72_0 conda-forge
tzcode 2023c h0b41bf4_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
ubiquerg 0.6.3 pyhd8ed1ab_0 conda-forge
udunits2 2.2.28 h40f5838_3 conda-forge
umap-learn 0.5.4 py39hf3d152e_0 conda-forge
unicodedata2 15.1.0 py39hd1e30aa_0 conda-forge
uriparser 0.9.7 hcb278e6_1 conda-forge
uritemplate 4.1.1 pyhd8ed1ab_0 conda-forge
urllib3 1.26.18 pyhd8ed1ab_0 conda-forge
veracitools 0.1.3 py_0 conda-forge
wcwidth 0.2.9 pyhd8ed1ab_0 conda-forge
wget 1.20.3 ha56f1ee_1 conda-forge
wheel 0.41.3 pyhd8ed1ab_0 conda-forge
wrapt 1.15.0 py39hd1e30aa_1 conda-forge
xerces-c 3.2.4 h55805fa_1 conda-forge
xmltodict 0.13.0 pyhd8ed1ab_0 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.0.10 h7f98852_0 conda-forge
xorg-libsm 1.2.3 hd9c2040_1000 conda-forge
xorg-libx11 1.8.4 h0b41bf4_0 conda-forge
xorg-libxau 1.0.11 hd590300_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxrender 0.9.10 h7f98852_1003 conda-forge
xorg-libxt 1.3.0 hd590300_0 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.9.2 py39hd1e30aa_1 conda-forge
yq 3.2.3 pyhd8ed1ab_0 conda-forge
yte 1.5.1 pyha770c72_2 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hd590300_5 conda-forge
zstd 1.5.5 hfc55251_0 conda-forge
(cellsnake) acboynes@rdl-b5qh9t:~$

##R SESSIONINFO
sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS/LAPACK: /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/libopenblasp-r0.3.21.so

locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

loaded via a namespace (and not attached):
[1] compiler_4.2.2

[sinanugur]

Do you have internet connection? I think celltypist requires the models to be download.

type this and then rerun.

celltypist --update-models

I think Seurat5 wont solve your resource problem. It requires quite a large computer (we usually run on 1TB ram and 126 CPUs, CPUs is not necessary but RAM is important) and it may takes days to finish differential expression analysis. You can integrate a smaller dataset in your current setup but it may still takes hours with cellsnake. I recommend to use a PCA dims 20 and integration methodology rcpa to test on 100K cells on your computer.

For example,

cellsnake integrate data --dims 20 --reduction rcpa

[ab4cp]

Hi @sinanugur - this was actually a security restriction on my server sorry

[ab4cp]

Hi @sinanugur I may have ran into another bug which I couldn't find too much troubleshooting information on.

I'm getting a new error message with the fetal-brain dataset doubletFinder_v3. Also, getting the same error message when running some of my own datasets.

~/Downloads/fetal-brain$ cellsnake standard data --jobs 5

Loading required package: viridisLite

Try help(fields) to get started.
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
Error in doubletFinder_v3(scrna, PCs = 1:10, pN = 0.25, pK = 0.09, nExp = nExp_poi, :
no slot of name "counts" for this object of class "Assay5"
In addition: Warning messages:
1: package ‘fields’ was built under R version 4.2.3
2: package ‘spam’ was built under R version 4.2.3
3: package ‘viridisLite’ was built under R version 4.2.3
4: package ‘KernSmooth’ was built under R version 4.2.3
Execution halted
[Fri Nov 10 15:55:45 2023]
Error in rule normalization_pca_rds:
jobid: 9
input: analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds
output: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds
shell:
/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-normalization-pca.R --rds analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds --doublet.filter --normalization.method LogNormalize --cpu 5 --scale.factor 10000 --reference BlueprintEncodeData --variable.selection.method vst --nfeature 2000 --resolution 0.8 --output.rds analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds --umap --tsne
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Trying to restart job 9.
Select jobs to execute...

[Fri Nov 10 15:55:45 2023]
rule normalization_pca_rds:
input: analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds
output: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds
jobid: 9
reason: Missing output files: analyses/processed/percent_mt10/resolution0.8/10X_17_028.rds; Input files updated by another job: analyses/raw/percent_mt10/resolution0.8/10X_17_028.rds
wildcards: sample=10X_17_028, percent_mt=10, resolution=0.8
threads: 5
resources: tmpdir=/tmp, mem_mb=15000, mem_mib=14306

[sinanugur]

Hi @ab4cp,
Yes, it seems some new updates causing this problem. I submitted a new build and will submit a patch but before that you can fix your environment.

Here is the solution:

conda install r-seuratobject=4.1.3 r-matrix=1.5_4.1 -c conda-forge

Just to be on thre safe side, you can also check if the Seurat version messed up:

conda install r-seurat=4.3.0 -c conda-forge

Cheers,

[ab4cp]

@sinanugur thanks for the reply. I just updated both of those as you mentioned. I ran the fetal-brain dataset again and got the following error about cannot find seurat object

Warning message:
package ‘data.table’ was built under R version 4.2.3
Error in CreateSeuratObject(counts = scrna.data, project = make.names(opt$sampleid), :
could not find function "CreateSeuratObject"
Execution halted
[Sat Nov 11 01:20:01 2023]
Error in rule create_initial_raw_rds_and_trimming:
jobid: 0
input: data/10X_17_029/outs/filtered_feature_bc_matrix
output: analyses/raw/percent_mt10/resolution0.8/10X_17_029.rds, results/10X_17_029/percent_mt10/resolution0.8/technicals/plot_before-qc-trimming.pdf, results/10X_17_029/percent_mt10/resolution0.8/technicals/plot_after-qc-trimming.pdf

RuleException:
CalledProcessError in file /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/rules/seurat.smk, line 46:
Command 'set -euo pipefail; /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-read-qc.R --data.dir data/10X_17_029/outs/filtered_feature_bc_matrix --output.rds analyses/raw/percent_mt10/resolution0.8/10X_17_029.rds --sampleid 10X_17_029 --percent.rp 0 --percent.mt 10 --min.features 200 --max.features inf --max.molecules inf --min.cells 3 --before.violin.plot results/10X_17_029/percent_mt10/resolution0.8/technicals/plot_before-qc-trimming.pdf --after.violin.plot results/10X_17_029/percent_mt10/resolution0.8/technicals/plot_after-qc-trimming.pdf' returned non-zero exit status 1.
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/rules/seurat.smk", line 46, in __rule_create_initial_raw_rds_and_trimming
File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/concurrent/futures/thread.py", line 58, in run
Shutting down, this might take some time.
Exiting because a job execution failed. Look above for error message
Exiting because a job execution failed. Look above for error message

[sinanugur]

OK, now the environment is missing Seurat, did you run?

conda install r-seurat=4.3.0 -c conda-forge

Make sure the other dependencies are not updated.

[ab4cp]

Yeh I ran the above. Looks like the correct version of seurat is in the environment

packages in environment at /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake:

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_r-mutex 1.0.1 anacondar_1 conda-forge
aioeasywebdav 2.4.0 py39hf3d152e_1001 conda-forge
aiohttp 3.8.6 py39hd1e30aa_1 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
amply 0.1.6 pyhd8ed1ab_0 conda-forge
anndata 0.10.2 pyhd8ed1ab_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
argcomplete 3.1.4 pyhd8ed1ab_0 conda-forge
arpack 3.7.0 hdefa2d7_2 conda-forge
array-api-compat 1.4 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge
attmap 0.13.2 pyhd8ed1ab_0 conda-forge
attrs 23.1.0 pyh71513ae_1 conda-forge
backports 1.0 pyhd8ed1ab_3 conda-forge
backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge
bcrypt 4.0.1 py39h9fdd4d6_1 conda-forge
bedtools 2.31.0 hf5e1c6e_3 bioconda
binutils_impl_linux-64 2.40 hf600244_0 conda-forge
bioconductor-annotationdbi 1.60.0 r42hdfd78af_0 bioconda
bioconductor-annotationhub 3.6.0 r42hdfd78af_0 bioconda
bioconductor-batchelor 1.14.0 r42hf17093f_1 bioconda
bioconductor-beachmat 2.14.0 r42hf17093f_1 bioconda
bioconductor-biobase 2.58.0 r42ha9d7317_1 bioconda
bioconductor-biocfilecache 2.6.0 r42hdfd78af_0 bioconda
bioconductor-biocgenerics 0.44.0 r42hdfd78af_0 bioconda
bioconductor-biocneighbors 1.16.0 r42hf17093f_1 bioconda
bioconductor-biocparallel 1.32.5 r42hf17093f_1 bioconda
bioconductor-biocsingular 1.14.0 r42hf17093f_1 bioconda
bioconductor-biocversion 3.16.0 r42hdfd78af_0 bioconda
bioconductor-biostrings 2.66.0 r42ha9d7317_1 bioconda
bioconductor-celldex 1.8.0 r42hdfd78af_0 bioconda
bioconductor-clusterprofiler 4.6.0 r42hdfd78af_0 bioconda
bioconductor-complexheatmap 2.14.0 r42hdfd78af_0 bioconda
bioconductor-data-packages 20230718 hdfd78af_1 bioconda
bioconductor-delayedarray 0.24.0 r42ha9d7317_1 bioconda
bioconductor-delayedmatrixstats 1.20.0 r42hdfd78af_0 bioconda
bioconductor-dose 3.24.0 r42hdfd78af_0 bioconda
bioconductor-enhancedvolcano 1.16.0 r42hdfd78af_0 bioconda
bioconductor-enrichplot 1.18.0 r42hdfd78af_0 bioconda
bioconductor-experimenthub 2.6.0 r42hdfd78af_0 bioconda
bioconductor-fgsea 1.24.0 r42hf17093f_1 bioconda
bioconductor-genomeinfodb 1.34.9 r42hdfd78af_0 bioconda
bioconductor-genomeinfodbdata 1.2.9 r42hdfd78af_0 bioconda
bioconductor-genomicranges 1.50.0 r42ha9d7317_1 bioconda
bioconductor-ggtree 3.6.0 r42hdfd78af_0 bioconda
bioconductor-go.db 3.16.0 r42hdfd78af_0 bioconda
bioconductor-gosemsim 2.24.0 r42hf17093f_1 bioconda
bioconductor-graph 1.76.0 r42ha9d7317_1 bioconda
bioconductor-hdo.db 0.99.1 r42hdfd78af_0 bioconda
bioconductor-interactivedisplaybase 1.36.0 r42hdfd78af_0 bioconda
bioconductor-iranges 2.32.0 r42ha9d7317_1 bioconda
bioconductor-keggrest 1.38.0 r42hdfd78af_0 bioconda
bioconductor-limma 3.54.0 r42ha9d7317_1 bioconda
bioconductor-matrixgenerics 1.10.0 r42hdfd78af_0 bioconda
bioconductor-miqc 1.6.0 r42hdfd78af_0 bioconda
bioconductor-org.hs.eg.db 3.16.0 r42hdfd78af_0 bioconda
bioconductor-qvalue 2.30.0 r42hdfd78af_0 bioconda
bioconductor-residualmatrix 1.8.0 r42hdfd78af_0 bioconda
bioconductor-rhdf5lib 1.20.0 r42ha9d7317_2 bioconda
bioconductor-s4vectors 0.36.0 r42ha9d7317_1 bioconda
bioconductor-scaledmatrix 1.6.0 r42hdfd78af_0 bioconda
bioconductor-scater 1.26.0 r42hdfd78af_0 bioconda
bioconductor-scuttle 1.8.0 r42hf17093f_1 bioconda
bioconductor-singlecellexperiment 1.20.0 r42hdfd78af_0 bioconda
bioconductor-singler 2.0.0 r42hf17093f_1 bioconda
bioconductor-sparsematrixstats 1.10.0 r42hf17093f_1 bioconda
bioconductor-summarizedexperiment 1.28.0 r42hdfd78af_0 bioconda
bioconductor-topgo 2.50.0 r42hdfd78af_0 bioconda
bioconductor-treeio 1.22.0 r42hdfd78af_0 bioconda
bioconductor-xvector 0.38.0 r42ha9d7317_1 bioconda
bioconductor-zlibbioc 1.44.0 r42ha9d7317_2 bioconda
blast 2.14.1 pl5321h6f7f691_0 bioconda
blosc 1.21.5 h0f2a231_0 conda-forge
boost-cpp 1.78.0 h5adbc97_2 conda-forge
boto3 1.28.77 pyhd8ed1ab_0 conda-forge
botocore 1.31.77 pyhd8ed1ab_0 conda-forge
brotli 1.1.0 hd590300_1 conda-forge
brotli-bin 1.1.0 hd590300_1 conda-forge
brotli-python 1.1.0 py39h3d6467e_1 conda-forge
bwidget 1.9.14 ha770c72_1 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
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