ssGSEA FDR?

[domi84]

Hello,
thanks for this package, seems pretty useful.
I am trying to use it on a scRNAseq dataset (Seurat), I get the results, I can see pathways, but I don't see any FDR/p-value columns.
I checked also the PDF/excel files generated, but nothing.
How can I access to those?

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

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

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

other attached packages:
 [1] ReactomeGSA_1.6.1    DoubletFinder_2.0.3  dplyr_1.0.7          dsb_1.0.2            Nebulosa_1.2.0       SeuratWrappers_0.3.0
 [7] clustree_0.4.4       ggraph_2.0.5         patchwork_1.1.1      SeuratObject_4.0.2   Seurat_4.0.3         harmony_0.1.0       
[13] Rcpp_1.0.10          ggplot2_3.3.5        unixtools_0.1-1     

loaded via a namespace (and not attached):
  [1] utf8_1.2.1                  reticulate_1.20             R.utils_2.10.1              ks_1.13.5                  
  [5] tidyselect_1.1.1            htmlwidgets_1.5.3           grid_4.1.0                  Rtsne_0.15                 
  [9] munsell_0.5.0               codetools_0.2-18            ica_1.0-2                   future_1.21.0              
 [13] miniUI_0.1.1.1              withr_2.5.0                 colorspace_2.0-2            Biobase_2.52.0             
 [17] knitr_1.33                  rstudioapi_0.13             stats4_4.1.0                SingleCellExperiment_1.14.1
 [21] ROCR_1.0-11                 tensor_1.5                  listenv_0.8.0               labeling_0.4.2             
 [25] MatrixGenerics_1.4.0        GenomeInfoDbData_1.2.6      polyclip_1.10-0             farver_2.1.0               
 [29] parallelly_1.26.1           vctrs_0.3.8                 generics_0.1.0              xfun_0.24                  
 [33] ulimit_0.0-3                R6_2.5.0                    doParallel_1.0.16           GenomeInfoDb_1.28.1        
 [37] clue_0.3-59                 graphlayouts_0.7.1          rsvd_1.0.5                  bitops_1.0-7               
 [41] spatstat.utils_3.0-1        DelayedArray_0.18.0         assertthat_0.2.1            promises_1.2.0.1           
 [45] scales_1.1.1                gtable_0.3.0                Cairo_1.5-12.2              globals_0.14.0             
 [49] goftest_1.2-2               tidygraph_1.2.0             rlang_1.0.6                 GlobalOptions_0.1.2        
 [53] splines_4.1.0               lazyeval_0.2.2              spatstat.geom_3.0-6         BiocManager_1.30.16        
 [57] yaml_2.2.1                  reshape2_1.4.4              abind_1.4-5                 httpuv_1.6.1               
 [61] tools_4.1.0                 ellipsis_0.3.2              gplots_3.1.1                spatstat.core_2.2-0        
 [65] RColorBrewer_1.1-2          BiocGenerics_0.38.0         ggridges_0.5.3              plyr_1.8.6                 
 [69] progress_1.2.2              zlibbioc_1.38.0             purrr_0.3.4                 RCurl_1.98-1.3             
 [73] prettyunits_1.1.1           rpart_4.1-15                deldir_1.0-6                pbapply_1.4-3              
 [77] GetoptLong_1.0.5            viridis_0.6.1               cowplot_1.1.1               S4Vectors_0.30.0           
 [81] zoo_1.8-9                   SummarizedExperiment_1.22.0 ggrepel_0.9.1               cluster_2.1.2              
 [85] magrittr_2.0.1              data.table_1.14.0           scattermore_0.7             circlize_0.4.13            
 [89] lmtest_0.9-38               RANN_2.6.1                  mvtnorm_1.1-3               fitdistrplus_1.1-5         
 [93] matrixStats_0.59.0          hms_1.1.0                   mime_0.11                   evaluate_0.14              
 [97] xtable_1.8-4                mclust_5.4.9                IRanges_2.26.0              gridExtra_2.3              
[101] shape_1.4.6                 compiler_4.1.0              tibble_3.1.2                KernSmooth_2.23-20         
[105] crayon_1.4.1                R.oo_1.24.0                 htmltools_0.5.4             mgcv_1.8-36                
[109] later_1.2.0                 tidyr_1.1.3                 DBI_1.1.1                   tweenr_1.0.2               
[113] ComplexHeatmap_2.8.0        MASS_7.3-54                 Matrix_1.3-4                cli_3.0.0                  
[117] R.methodsS3_1.8.1           parallel_4.1.0              igraph_1.2.6                GenomicRanges_1.44.0       
[121] pkgconfig_2.0.3             plotly_4.9.4.1              spatstat.sparse_3.0-0       foreach_1.5.1              
[125] XVector_0.32.0              stringr_1.4.0               digest_0.6.27               sctransform_0.3.2          
[129] RcppAnnoy_0.0.18            pracma_2.3.8                spatstat.data_3.0-0         rmarkdown_2.9              
[133] leiden_0.3.8                uwot_0.1.10                 curl_4.3.2                  shiny_1.6.0                
[137] gtools_3.9.2                rjson_0.2.20                lifecycle_1.0.0             nlme_3.1-152               
[141] jsonlite_1.7.2              viridisLite_0.4.0           limma_3.48.1                fansi_0.5.0                
[145] pillar_1.6.1                lattice_0.20-44             fastmap_1.1.0               httr_1.4.2                 
[149] survival_3.2-11             glue_1.4.2                  remotes_2.4.0               png_0.1-7                  
[153] iterators_1.0.13            ggforce_0.3.3               stringi_1.6.2               caTools_1.18.2             
[157] irlba_2.3.3                 future.apply_1.7.0   

[jgriss]

Those are not reported as they do not exist. This method only maps pathway expression values onto the clusters (ie. one pathway expression level per cluster). In order to get p- / FDR values you would perform a f.e. t-test on these values comparing the relevant groups of clusters with each other.

Does that make sense?

[domi84]

Yes, it makes sense,

In order to get p- / FDR values you would perform a f.e. t-test on these values comparing the relevant groups of clusters with each other.

I assume you don't have a function ready to use in the package, because I don't see it in the vignette https://bioconductor.org/packages/release/bioc/vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html

Also, because those are pathway-level expression values per cell cluster, do I need to split them per replicates (donors) in order to make a stat test?

[jgriss]

Hi,

We are working on a function - but as always, things don't progress as quickly as we'd like.

Yes, splitting based on donor is ideal for this setup.