Enichment-analysis-fgsea
unawaz1996
2023-03-16
Last updated: 2023-03-22
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f17fc63 | unawaz1996 | 2023-03-22 | Adding fgsea results |
Fast gene set enrichment analysis
Datasets
Hallmark Gene sets
Fast Gene Set Enrichment Analysis implements a fast version of the gsea algorithm. As a result, more permutations are made to get more fine grained p-values.
fgsea results can be intrepreted as following:
ES: Enrichment score which is calculated based on the rankNES: Normalized Enrichment Score
Using the MSigDB genesets as above, we will conduct a fgsea analysis on the sets of interest including KEGG, Wikipathways and Reactome.
UPF3B vs Knockdown results
UpSet plot indicating distribution of DE genes within all significant gene sets. Gene sets were restricted to those with an FDR < 0.05 and at least 5 DE genes
Gene sets enrichment in multiple comparisons
Hallmark sets
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|======================================================================| 100%UPF3B vs Controls
UPF3A vs Controls
## Double KD vs Controls
UPF3A OE vs Controls
## UPF3A OE, UPF3B KD
Gene sets enriched in all comparisons
R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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] grid stats4 tools stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] pander_0.6.5 msigdbr_7.5.1
[3] cowplot_1.1.1 ngsReports_2.0.3
[5] patchwork_1.1.2 VennDiagram_1.7.3
[7] futile.logger_1.4.3 UpSetR_1.4.0
[9] fgsea_1.24.0 GOplot_1.0.2
[11] RColorBrewer_1.1-3 gridExtra_2.3
[13] ggdendro_0.1.23 AnnotationHub_3.6.0
[15] BiocFileCache_2.6.1 dbplyr_2.3.1
[17] openxlsx_4.2.5.2 ggiraph_0.8.6
[19] wasabi_1.0.1 sleuth_0.30.1
[21] DT_0.27 VennDetail_1.14.0
[23] msigdb_1.6.0 GSEABase_1.60.0
[25] graph_1.76.0 annotate_1.76.0
[27] XML_3.99-0.13 pheatmap_1.0.12
[29] ggvenn_0.1.9 MetBrewer_0.2.0
[31] ggpubr_0.6.0 venn_1.11
[33] viridis_0.6.2 viridisLite_0.4.1
[35] tximeta_1.16.1 tximport_1.26.1
[37] goseq_1.50.0 geneLenDataBase_1.34.0
[39] BiasedUrn_2.0.9 org.Mm.eg.db_3.16.0
[41] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.22.0
[43] AnnotationFilter_1.22.0 GenomicFeatures_1.50.4
[45] AnnotationDbi_1.60.0 biomaRt_2.54.0
[47] edgeR_3.40.2 limma_3.54.1
[49] DESeq2_1.38.3 SummarizedExperiment_1.28.0
[51] Biobase_2.58.0 MatrixGenerics_1.10.0
[53] matrixStats_0.63.0 GenomicRanges_1.50.2
[55] GenomeInfoDb_1.34.9 IRanges_2.32.0
[57] S4Vectors_0.36.1 BiocGenerics_0.44.0
[59] corrplot_0.92 lubridate_1.9.2
[61] forcats_1.0.0 purrr_1.0.1
[63] readr_2.1.4 tidyverse_2.0.0
[65] stringr_1.5.0 tidyr_1.3.0
[67] scales_1.2.1 data.table_1.14.8
[69] readxl_1.4.2 tibble_3.1.8
[71] magrittr_2.0.3 reshape2_1.4.4
[73] ggplot2_3.4.1 dplyr_1.1.0.9000
[75] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 rtracklayer_1.58.0
[3] bit64_4.0.5 knitr_1.42
[5] DelayedArray_0.24.0 KEGGREST_1.38.0
[7] RCurl_1.98-1.10 generics_0.1.3
[9] callr_3.7.3 lambda.r_1.2.4
[11] RSQLite_2.3.0 bit_4.0.5
[13] tzdb_0.3.0 xml2_1.3.3
[15] httpuv_1.6.9 xfun_0.37
[17] hms_1.1.2 jquerylib_0.1.4
[19] babelgene_22.9 evaluate_0.20
[21] promises_1.2.0.1 fansi_1.0.4
[23] restfulr_0.0.15 progress_1.2.2
[25] DBI_1.1.3 geneplotter_1.76.0
[27] htmlwidgets_1.6.1 ellipsis_0.3.2
[29] crosstalk_1.2.0 backports_1.4.1
[31] vctrs_0.5.2.9000 abind_1.4-5
[33] cachem_1.0.7 withr_2.5.0
[35] GenomicAlignments_1.34.0 prettyunits_1.1.1
[37] lazyeval_0.2.2 crayon_1.5.2
[39] labeling_0.4.2 pkgconfig_2.0.3
[41] nlme_3.1-162 ProtGenerics_1.30.0
[43] rlang_1.0.6.9000 lifecycle_1.0.3
[45] filelock_1.0.2 cellranger_1.1.0
[47] rprojroot_2.0.3 Matrix_1.5-3
[49] carData_3.0-5 Rhdf5lib_1.20.0
[51] zoo_1.8-11 whisker_0.4.1
[53] processx_3.8.0 png_0.1-8
[55] rjson_0.2.21 bitops_1.0-7
[57] getPass_0.2-2 rhdf5filters_1.10.0
[59] Biostrings_2.66.0 blob_1.2.3
[61] rstatix_0.7.2 ggsignif_0.6.4
[63] memoise_2.0.1 plyr_1.8.8
[65] zlibbioc_1.44.0 compiler_4.2.2
[67] BiocIO_1.8.0 Rsamtools_2.14.0
[69] cli_3.6.0 XVector_0.38.0
[71] ps_1.7.2 formatR_1.14
[73] MASS_7.3-58.2 mgcv_1.8-41
[75] tidyselect_1.2.0 stringi_1.7.12
[77] highr_0.10 yaml_2.3.7
[79] locfit_1.5-9.7 sass_0.4.5
[81] fastmatch_1.1-3 timechange_0.2.0
[83] parallel_4.2.2 rstudioapi_0.14
[85] uuid_1.1-0 git2r_0.31.0
[87] farver_2.1.1 digest_0.6.31
[89] BiocManager_1.30.20 shiny_1.7.4
[91] Rcpp_1.0.10 car_3.1-1
[93] broom_1.0.3 BiocVersion_3.16.0
[95] later_1.3.0 httr_1.4.5
[97] colorspace_2.1-0 fs_1.6.1
[99] splines_4.2.2 statmod_1.5.0
[101] plotly_4.10.1 systemfonts_1.0.4
[103] xtable_1.8-4 jsonlite_1.8.4
[105] futile.options_1.0.1 R6_2.5.1
[107] pillar_1.8.1 htmltools_0.5.4
[109] mime_0.12 glue_1.6.2
[111] fastmap_1.1.1 BiocParallel_1.32.5
[113] interactiveDisplayBase_1.36.0 codetools_0.2-19
[115] utf8_1.2.3 lattice_0.20-45
[117] bslib_0.4.2 curl_5.0.0
[119] zip_2.2.2 GO.db_3.16.0
[121] admisc_0.30 rmarkdown_2.20
[123] munsell_0.5.0 rhdf5_2.42.0
[125] GenomeInfoDbData_1.2.9 gtable_0.3.1