In this tutorial, we will perform gene module enrichment analysis using both the Seurat package and the GSVA package. The analysis involves calculating enrichment scores for a gene list using Seurat's AddModuleScore() function, as well as the ssGSEA and GSVA methods from the GSVA package.
- Gene List: A list of genes for which enrichment analysis will be performed.
- Module scores: Enrichment scores computed via Seurat, ssGSEA, and GSVA methods.
- Visualization: Violin plots for gene set enrichment scores across cell types.
Author: Cankun Wang
Citation:
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Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R (2023). “Dictionary learning for integrative, multimodal and scalable single-cell analysis.” Nature Biotechnology. doi:10.1038/s41587-023-01767-y, https://doi.org/10.1038/s41587-023-01767-y.
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Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013). https://doi.org/10.1186/1471-2105-14-7
> sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.6.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[6] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[11] clusterProfiler_4.12.6
loaded via a namespace (and not attached):
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[5] farver_2.1.2 rmarkdown_2.28 fs_1.6.4 zlibbioc_1.50.0
[9] vctrs_0.6.5 memoise_2.0.1 ggtree_3.12.0 htmltools_0.5.8.1
[13] S4Arrays_1.4.1 gridGraphics_0.5-1 SparseArray_1.4.8 parallelly_1.38.0
[17] htmlwidgets_1.6.4 plyr_1.8.9 httr2_1.0.3 cachem_1.1.0
[21] igraph_2.0.3 lifecycle_1.0.4 pkgconfig_2.0.3 gson_0.1.0
[25] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0 GenomeInfoDbData_1.2.12
[29] MatrixGenerics_1.16.0 future_1.34.0 aplot_0.2.3 digest_0.6.36
[33] enrichplot_1.24.4 colorspace_2.1-1 patchwork_1.2.0 AnnotationDbi_1.66.0
[37] S4Vectors_0.42.1 DESeq2_1.44.0 rprojroot_2.0.4 GenomicRanges_1.56.1
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[49] bit64_4.0.5 withr_3.0.1 BiocParallel_1.38.0 viridis_0.6.5
[53] DBI_1.2.3 qs_0.26.3 ggforce_0.4.2 R.utils_2.12.3
[57] MASS_7.3-60.2 rappdirs_0.3.3 DelayedArray_0.30.1 tools_4.4.1
[61] scatterpie_0.2.4 ape_5.8 R.oo_1.26.0 glue_1.7.0
[65] nlme_3.1-164 GOSemSim_2.30.2 shadowtext_0.1.4 grid_4.4.1
[69] reshape2_1.4.4 fgsea_1.30.0 generics_0.1.3 gtable_0.3.5
[73] tzdb_0.4.0 R.methodsS3_1.8.2 hms_1.1.3 data.table_1.15.4
[77] RApiSerialize_0.1.3 tidygraph_1.3.1 stringfish_0.16.0 utf8_1.2.4
[81] XVector_0.44.0 BiocGenerics_0.50.0 ggrepel_0.9.5 pillar_1.9.0
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[113] qvalue_2.36.0 BiocManager_1.30.23 ggplotify_0.1.2 cli_3.6.3
[117] RcppParallel_5.1.8 munsell_0.5.1 Rcpp_1.0.13 GenomeInfoDb_1.40.1
[121] globals_0.16.3 png_0.1-8 parallel_4.4.1 blob_1.2.4
[125] DOSE_3.30.5 listenv_0.9.1 tidytree_0.4.6 viridisLite_0.4.2
[129] scales_1.3.0 crayon_1.5.3 rlang_1.1.4 cowplot_1.1.3
[133] fastmatch_1.1-4 KEGGREST_1.44.1