Enichment-analysis-fgsea
unawaz1996
2023-03-16
Last updated: 2023-03-22
Checks: 7 0
Knit directory: NMD-analysis/
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| Rmd | f17fc63 | unawaz1996 | 2023-03-22 | Adding fgsea results |
Fast gene set enrichment analysis
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
Datasets
Hallmark Gene sets
C2 genesets
Using the MSigDB genesets as above, we will conduct a fgsea analysis on the sets of interest including KEGG, Wikipathways and Reactome. In our analysis, we will be using 2,465 gene sets in total.
- UPF3B KD vs Controls
- KEGG pathways
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- Reactome
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
- Wikipathways
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- UPF3A KD
- KEGG pathways
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
- Reactome
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
- Wikipathways
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
- Double KDs
- KEGG pathways
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- Reactome
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 8 DE genes
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- Wikipathways
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- UPF3A OE
- KEGG pathways
- Reactome
- Wikipathways
- UPF3A OE UPF3B KD
- KEGG pathways
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- Reactome
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
- Wikipathways
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
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
Summary of results from all comparisons
Hallmark sets
Gene sets enriched in all comparisons
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
| Version | Author | Date |
|---|---|---|
| 1f4ee28 | unawaz1996 | 2023-03-22 |
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] ggrepel_0.9.3 pander_0.6.5
[3] msigdbr_7.5.1 cowplot_1.1.1
[5] ngsReports_2.0.3 patchwork_1.1.2
[7] VennDiagram_1.7.3 futile.logger_1.4.3
[9] UpSetR_1.4.0 fgsea_1.24.0
[11] GOplot_1.0.2 RColorBrewer_1.1-3
[13] gridExtra_2.3 ggdendro_0.1.23
[15] AnnotationHub_3.6.0 BiocFileCache_2.6.1
[17] dbplyr_2.3.1 openxlsx_4.2.5.2
[19] ggiraph_0.8.6 wasabi_1.0.1
[21] sleuth_0.30.1 DT_0.27
[23] VennDetail_1.14.0 msigdb_1.6.0
[25] GSEABase_1.60.0 graph_1.76.0
[27] annotate_1.76.0 XML_3.99-0.13
[29] pheatmap_1.0.12 ggvenn_0.1.9
[31] MetBrewer_0.2.0 ggpubr_0.6.0
[33] venn_1.11 viridis_0.6.2
[35] viridisLite_0.4.1 tximeta_1.16.1
[37] tximport_1.26.1 goseq_1.50.0
[39] geneLenDataBase_1.34.0 BiasedUrn_2.0.9
[41] org.Mm.eg.db_3.16.0 EnsDb.Mmusculus.v79_2.99.0
[43] ensembldb_2.22.0 AnnotationFilter_1.22.0
[45] GenomicFeatures_1.50.4 AnnotationDbi_1.60.0
[47] biomaRt_2.54.0 edgeR_3.40.2
[49] limma_3.54.1 DESeq2_1.38.3
[51] SummarizedExperiment_1.28.0 Biobase_2.58.0
[53] MatrixGenerics_1.10.0 matrixStats_0.63.0
[55] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[57] IRanges_2.32.0 S4Vectors_0.36.1
[59] BiocGenerics_0.44.0 corrplot_0.92
[61] lubridate_1.9.2 forcats_1.0.0
[63] purrr_1.0.1 readr_2.1.4
[65] tidyverse_2.0.0 stringr_1.5.0
[67] tidyr_1.3.0 scales_1.2.1
[69] data.table_1.14.8 readxl_1.4.2
[71] tibble_3.1.8 magrittr_2.0.3
[73] reshape2_1.4.4 ggplot2_3.4.1
[75] dplyr_1.1.0.9000 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