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Rmd eef0287 Dave Tang 2025-01-24 Using fgsea with edgeR results

Installation

First install fgsea.

if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

if (!require("fgsea", quietly = TRUE))
  BiocManager::install("fgsea")

library(fgsea)

edgeR results

An example differential gene expression results table.

edger_res <- readr::read_csv("https://raw.githubusercontent.com/davetang/muse/refs/heads/main/data/13970886_edger_res.csv", show_col_types = FALSE)
head(edger_res)
# A tibble: 6 × 6
  ensembl_gene_id  logFC logCPM      F  PValue adjusted_pvalue
  <chr>            <dbl>  <dbl>  <dbl>   <dbl>           <dbl>
1 ENSG00000000003  2.73   4.83   4.28  0.0684           0.109 
2 ENSG00000000005 -7.00   0.541 17.6   0.00216          0.0138
3 ENSG00000000419  0.120  5.34   0.114 0.743            0.776 
4 ENSG00000000457 -0.708  5.31   3.35  0.0993           0.145 
5 ENSG00000000460 -0.897  3.95   2.66  0.136            0.186 
6 ENSG00000000938  1.54   5.60   1.86  0.205            0.258

Add ranking metrics.

edger_res |>
  dplyr::mutate(rank_metric = logFC * -log10(PValue)) |>
  dplyr::mutate(rank_metric2 = sign(logFC) * -log10(PValue)) -> edger_res

Compare rankings.

library(ggplot2)

ggplot(edger_res, aes(rank_metric, rank_metric2)) +
  geom_point() +
  theme_minimal()

Version Author Date
9f42e61 Dave Tang 2025-04-16

Correlation of rank metrics.

cor(edger_res$rank_metric, edger_res$rank_metric2, method = "spearman")
[1] 0.9586936

Convert to Entrez Gene IDs

Use {org.Hs.eg.db}.

if (!require("org.Hs.eg.db", quietly = TRUE))
  BiocManager::install("org.Hs.eg.db")

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, saveRDS, setdiff,
    table, tapply, union, unique, unsplit, which.max, which.min
Welcome to Bioconductor

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

Attaching package: 'S4Vectors'
The following object is masked from 'package:utils':

    findMatches
The following objects are masked from 'package:base':

    expand.grid, I, unname
suppressPackageStartupMessages(library(org.Hs.eg.db))

Convert to Entrez Gene IDs.

AnnotationDbi::select(
  org.Hs.eg.db,
  keys = edger_res$ensembl_gene_id,
  columns=c("ENSEMBL","ENTREZID"),
  keytype="ENSEMBL"
) -> ensembl_to_entrez
'select()' returned 1:many mapping between keys and columns
ensembl_to_entrez <- dplyr::rename(ensembl_to_entrez, "ensembl_gene_id" = ENSEMBL)

head(ensembl_to_entrez)
  ensembl_gene_id ENTREZID
1 ENSG00000000003     7105
2 ENSG00000000005    64102
3 ENSG00000000419     8813
4 ENSG00000000457    57147
5 ENSG00000000460    55732
6 ENSG00000000938     2268

Number of NAs.

table(is.na(ensembl_to_entrez$ENTREZID))

FALSE  TRUE 
28722 10968

Hallmark gene sets

Use {msigdb}.

if (!require("msigdb", quietly = TRUE))
  BiocManager::install("msigdb")

if (!require("ExperimentHub", quietly = TRUE))
  BiocManager::install("ExperimentHub")

Attaching package: 'AnnotationHub'
The following object is masked from 'package:Biobase':

    cache
if (!require("GSEABase", quietly = TRUE))
  BiocManager::install("GSEABase")

Attaching package: 'graph'
The following object is masked from 'package:XML':

    addNode
suppressPackageStartupMessages(library(msigdb))
suppressPackageStartupMessages(library(ExperimentHub))
suppressPackageStartupMessages(library(GSEABase))

Query an ExperimentHub object.

eh <- ExperimentHub(ask = FALSE)
AnnotationHub::query(x = eh, pattern = 'msigdb')
ExperimentHub with 49 records
# snapshotDate(): 2024-10-24
# $dataprovider: Broad Institute, Emory University, EBI
# $species: Homo sapiens, Mus musculus
# $rdataclass: GSEABase::GeneSetCollection, list, data.frame
# additional mcols(): taxonomyid, genome, description,
#   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#   rdatapath, sourceurl, sourcetype 
# retrieve records with, e.g., 'object[["EH5421"]]' 

           title                   
  EH5421 | msigdb.v7.2.hs.SYM      
  EH5422 | msigdb.v7.2.hs.EZID     
  EH5423 | msigdb.v7.2.mm.SYM      
  EH5424 | msigdb.v7.2.mm.EZID     
  EH6727 | MSigDB C8 MANNO MIDBRAIN
  ...      ...                     
  EH8296 | msigdb.v7.5.1.hs.SYM    
  EH8297 | msigdb.v7.5.1.mm.EZID   
  EH8298 | msigdb.v7.5.1.mm.idf    
  EH8299 | msigdb.v7.5.1.mm.SYM    
  EH8300 | imex_hsmm_0722

Latest version.

AnnotationHub::query(x = eh, pattern = 'msigdb.*hs.EZID') |>
  tail(1) -> msigdb_hs_latest
msigdb_hs_latest
ExperimentHub with 1 record
# snapshotDate(): 2024-10-24
# names(): EH8294
# package(): msigdb
# $dataprovider: Broad Institute
# $species: Homo sapiens
# $rdataclass: GSEABase::GeneSetCollection
# $rdatadateadded: 2023-07-03
# $title: msigdb.v7.5.1.hs.EZID
# $description: Gene expression signatures (Homo sapiens) from the Molecular...
# $taxonomyid: 9606
# $genome: NA
# $sourcetype: XML
# $sourceurl: https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.5...
# $sourcesize: NA
# $tags: c("Homo_sapiens_Data", "Mus_musculus_Data") 
# retrieve record with 'object[["EH8294"]]'

Download.

msigdb_hs_ezid <- eh[[names(msigdb_hs_latest)]]
see ?msigdb and browseVignettes('msigdb') for documentation
loading from cache

Collections.

table(sapply(lapply(msigdb_hs_ezid, collectionType), bcCategory))

   c1    c2    c3    c4    c5    c6    c7    c8     h 
  299  6180  3726   858 28005   189  5219   700    50

Create gene lists from the Hallmark collection.

wanted <- sapply(lapply(msigdb_hs_ezid, collectionType), bcCategory) == "h"
hallmark_gs <- msigdb_hs_ezid[wanted]

hallmark_gs_list <- lapply(hallmark_gs, geneIds)
class(hallmark_gs_list)
[1] "list"
names(hallmark_gs_list) <- names(hallmark_gs)
head(hallmark_gs_list)
$HALLMARK_TNFA_SIGNALING_VIA_NFKB
  [1] "3726"   "2920"   "467"    "4792"   "7128"   "5743"   "2919"   "8870"  
  [9] "9308"   "6364"   "2921"   "23764"  "4791"   "7127"   "1839"   "1316"  
 [17] "330"    "5329"   "7538"   "3383"   "3725"   "1960"   "3553"   "597"   
 [25] "23645"  "80149"  "6648"   "4929"   "3552"   "5971"   "7185"   "7832"  
 [33] "1843"   "1326"   "2114"   "2152"   "6385"   "1958"   "3569"   "7124"  
 [41] "23135"  "4790"   "3976"   "5806"   "8061"   "3164"   "182"    "6351"  
 [49] "2643"   "6347"   "1827"   "1844"   "10938"  "9592"   "5966"   "8837"  
 [57] "8767"   "4794"   "8013"   "22822"  "51278"  "8744"   "2669"   "1647"  
 [65] "3627"   "10769"  "8553"   "1959"   "9021"   "11182"  "5734"   "1847"  
 [73] "5055"   "4783"   "5054"   "10221"  "25976"  "5970"   "329"    "6372"  
 [81] "9516"   "7130"   "960"    "3624"   "5328"   "4609"   "3604"   "6446"  
 [89] "10318"  "10135"  "2355"   "10957"  "3398"   "969"    "3575"   "1942"  
 [97] "7262"   "5209"   "6352"   "79693"  "3460"   "8878"   "10950"  "4616"  
[105] "8942"   "50486"  "694"    "4170"   "7422"   "5606"   "1026"   "3491"  
[113] "10010"  "3433"   "3606"   "7280"   "3659"   "2353"   "4973"   "388"   
[121] "374"    "4814"   "65986"  "8613"   "9314"   "6373"   "6303"   "1435"  
[129] "1880"   "56937"  "5791"   "7097"   "57007"  "7071"   "4082"   "3914"  
[137] "1051"   "9322"   "2150"   "687"    "3949"   "7050"   "127544" "55332" 
[145] "2683"   "11080"  "1437"   "5142"   "8303"   "5341"   "6776"   "23258" 
[153] "595"    "23586"  "8877"   "941"    "25816"  "57018"  "2526"   "9034"  
[161] "80176"  "8848"   "9334"   "150094" "23529"  "4780"   "2354"   "5187"  
[169] "10725"  "490"    "3593"   "3572"   "9120"   "19"     "3280"   "604"   
[177] "8660"   "6515"   "1052"   "51561"  "4088"   "6890"   "9242"   "64135" 
[185] "3601"   "79155"  "602"    "24145"  "24147"  "1906"   "10209"  "650"   
[193] "1846"   "10611"  "23308"  "9945"   "10365"  "3371"   "5271"   "4084"  

$HALLMARK_HYPOXIA
  [1] "5230"   "5163"   "2632"   "5211"   "226"    "2026"   "5236"   "10397" 
  [9] "3099"   "230"    "2821"   "4601"   "6513"   "5033"   "133"    "8974"  
 [17] "2023"   "5214"   "205"    "26355"  "5209"   "7422"   "665"    "7167"  
 [25] "30001"  "55818"  "901"    "3939"   "2997"   "2597"   "8553"   "51129" 
 [33] "3725"   "5054"   "4015"   "2645"   "8497"   "23764"  "54541"  "6515"  
 [41] "3486"   "4783"   "2353"   "3516"   "3098"   "10370"  "3669"   "2584"  
 [49] "26118"  "5837"   "6781"   "23036"  "694"    "123"    "1466"   "7436"  
 [57] "23210"  "2131"   "2152"   "5165"   "55139"  "7360"   "229"    "8614"  
 [65] "54206"  "2027"   "10957"  "3162"   "5228"   "26330"  "9435"   "55076" 
 [73] "63827"  "467"    "857"    "272"    "2719"   "3340"   "8660"   "8819"  
 [81] "2548"   "6385"   "8987"   "8870"   "5313"   "3484"   "5329"   "112464"
 [89] "8839"   "9215"   "25819"  "6275"   "58528"  "7538"   "1956"   "1907"  
 [97] "3423"   "1026"   "6095"   "1843"   "4282"   "5507"   "10570"  "11015" 
[105] "1837"   "136"    "9957"   "284119" "2908"   "1316"   "2239"   "3491"  
[113] "7128"   "771"    "3073"   "633"    "23645"  "55276"  "5292"   "25824" 
[121] "55577"  "1027"   "680"    "8277"   "4493"   "538"    "4502"   "9672"  
[129] "25976"  "5317"   "302"    "5224"   "1649"   "5578"   "2542"   "7852"  
[137] "1944"   "1356"   "8609"   "1490"   "9469"   "7163"   "56925"  "124872"
[145] "10891"  "596"    "2651"   "3036"   "54800"  "949"    "6576"   "6383"  
[153] "839"    "7428"   "2309"   "5155"   "126792" "6518"   "8406"   "1942"  
[161] "2745"   "57007"  "5066"   "7045"   "1634"   "6478"   "51316"  "2203"  
[169] "8459"   "5260"   "4627"   "1028"   "9380"   "5105"   "3623"   "3309"  
[177] "8509"   "23327"  "7162"   "7511"   "3569"   "6533"   "4214"   "3948"  
[185] "9590"   "26136"  "3798"   "3906"   "1289"   "2817"   "3069"   "10994" 
[193] "1463"   "7052"   "2113"   "3219"   "8991"   "2355"   "6820"   "7043"  

$HALLMARK_CHOLESTEROL_HOMEOSTASIS
 [1] "2224"   "1595"   "3422"   "2222"   "1717"   "6713"   "3157"   "50814" 
 [9] "4047"   "4597"   "3949"   "7108"   "230"    "10682"  "6319"   "10654" 
[17] "4598"   "4023"   "6309"   "9415"   "3156"   "51478"  "312"    "6721"  
[25] "5833"   "55902"  "467"    "127"    "23474"  "1891"   "875"    "2990"  
[33] "2194"   "3958"   "22809"  "308"    "94241"  "1119"   "2946"   "39"    
[41] "552"    "5359"   "1191"   "54206"  "57761"  "58191"  "51330"  "71"    
[49] "182"    "5641"   "26270"  "493869" "10957"  "118429" "114569" "928"   
[57] "5468"   "2731"   "6811"   "134429" "1499"   "27346"  "116496" "5165"  
[65] "5329"   "7869"   "2770"   "20"     "6311"   "4783"   "214"    "2171"  
[73] "6282"   "132864"

$HALLMARK_MITOTIC_SPINDLE
  [1] "9181"   "23332"  "3832"   "9493"   "57679"  "382"    "4650"   "4627"  
  [9] "10426"  "9793"   "29127"  "57580"  "50650"  "4926"   "6711"   "11004" 
 [17] "3799"   "7272"   "324"    "11190"  "5048"   "10435"  "9371"   "55704" 
 [25] "56992"  "332"    "116840" "4763"   "7248"   "996"    "11064"  "114791"
 [33] "24137"  "22919"  "55785"  "675"    "5347"   "5921"   "4751"   "8936"  
 [41] "7153"   "7204"   "9826"   "10300"  "9055"   "54443"  "55755"  "9126"  
 [49] "10844"  "9700"   "55201"  "201176" "9732"   "29901"  "3619"   "394"   
 [57] "2934"   "10276"  "10128"  "23637"  "2317"   "64411"  "121512" "29"    
 [65] "55835"  "4690"   "1063"   "9585"   "10163"  "4628"   "1062"   "9266"  
 [73] "4281"   "3831"   "57787"  "127829" "9702"   "8409"   "393"    "23580" 
 [81] "163786" "9113"   "4983"   "8976"   "4296"   "6654"   "25"     "7074"  
 [89] "23095"  "6453"   "134549" "8440"   "9787"   "613"    "10048"  "2037"  
 [97] "10801"  "11104"  "51174"  "22974"  "3797"   "357"    "85378"  "6709"  
[105] "23022"  "23647"  "9735"   "84376"  "25777"  "58526"  "1739"   "2316"  
[113] "79658"  "8476"   "23365"  "4082"   "51199"  "5108"   "10928"  "7430"  
[121] "85464"  "983"    "22930"  "10160"  "11346"  "54509"  "1894"   "2035"  
[129] "51735"  "3835"   "84333"  "6780"   "396"    "6790"   "26271"  "51203" 
[137] "5829"   "9564"   "23607"  "11214"  "10013"  "22994"  "3996"   "23192" 
[145] "5116"   "7840"   "11133"  "667"    "22920"  "151987" "9411"   "9462"  
[153] "9133"   "80119"  "5922"   "4739"   "8243"   "81"     "5311"   "7461"  
[161] "998"    "10403"  "9874"   "9344"   "6904"   "832"    "1794"   "2017"  
[169] "10051"  "10565"  "7277"   "4001"   "10006"  "6093"   "55125"  "699"   
[177] "50628"  "64857"  "253260" "10018"  "1778"   "6624"   "8874"   "140735"
[185] "4643"   "274"    "4853"   "5981"   "10611"  "89941"  "8470"   "11135" 
[193] "7414"   "6249"   "23012"  "7531"   "9771"   "55722"  "1453"  

$HALLMARK_WNT_BETA_CATENIN_SIGNALING
 [1] "4609"  "1499"  "3714"  "4851"  "28514" "8313"  "5664"  "8321"  "4855" 
[10] "51176" "8312"  "85407" "81029" "8454"  "182"   "9794"  "2648"  "2770" 
[19] "7475"  "5727"  "9612"  "27121" "3066"  "22943" "6932"  "7471"  "8650" 
[28] "6868"  "1856"  "5467"  "23385" "10014" "894"   "10023" "1454"  "3516" 
[37] "8325"  "7157"  "6502"  "23493" "23462" "79885"

$HALLMARK_TGF_BETA_SIGNALING
 [1] "7046"  "4092"  "7040"  "64750" "57154" "659"   "6498"  "6497"  "90"   
[10] "56937" "9612"  "5054"  "3726"  "4086"  "4091"  "23645" "7050"  "5045" 
[19] "4088"  "2280"  "6885"  "657"   "1499"  "28996" "7071"  "650"   "2022" 
[28] "324"   "5494"  "331"   "999"   "3397"  "7044"  "1028"  "51592" "11031"
[37] "7082"  "6574"  "1025"  "3399"  "9241"  "51742" "3460"  "3398"  "5499" 
[46] "6711"  "25937" "8412"  "7057"  "2339"  "3065"  "7323"  "4053"  "387"

fgsea analysis

Rank metrics distribution.

plot(hist(edger_res$rank_metric, breaks = 50))

Version Author Date
49a8145 Dave Tang 2025-01-24 
plot(hist(edger_res$rank_metric2, breaks = 50))

Version Author Date
9f42e61 Dave Tang 2025-04-16

Create ranks vector.

edger_res |>
  dplyr::inner_join(y = ensembl_to_entrez, by = "ensembl_gene_id", relationship = "many-to-many") |>
  dplyr::filter(!is.na(ENTREZID)) |>
  dplyr::group_by(ENTREZID) |>
  dplyr::mutate(ambiguous = ifelse(dplyr::n()>1, TRUE, FALSE)) |>
  dplyr::filter(!ambiguous) -> res
  
my_ranks <- res$rank_metric
my_names <- as.character(res$ENTREZID)
names(my_ranks) <- my_names

my_ranks2 <- res$rank_metric2
names(my_ranks2) <- my_names
plot(hist(my_ranks, breaks = 50))

Version Author Date
49a8145 Dave Tang 2025-01-24 
plot(hist(my_ranks2, breaks = 50))

Version Author Date
9f42e61 Dave Tang 2025-04-16

The fgsea() function runs the pre-ranked gene set enrichment analysis.

set.seed(1984)
fgseaRes <- fgsea(
  pathways = hallmark_gs_list, 
  stats = my_ranks,
  minSize=15,
  maxSize=500,
  nPermSimple = 100000
)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.44% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : There were 37 pathways for which P-values were not calculated
properly due to unbalanced (positive and negative) gene-level statistic values.
For such pathways pval, padj, NES, log2err are set to NA. You can try to
increase the value of the argument nPermSimple (for example set it nPermSimple
= 1000000)
set.seed(1984)
fgseaRes2 <- fgsea(
  pathways = hallmark_gs_list, 
  stats = my_ranks2,
  minSize=15,
  maxSize=500,
  nPermSimple = 100000
)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.44% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : There were 24 pathways for which P-values were not calculated
properly due to unbalanced (positive and negative) gene-level statistic values.
For such pathways pval, padj, NES, log2err are set to NA. You can try to
increase the value of the argument nPermSimple (for example set it nPermSimple
= 1000000)

Top 10 enriched pathways.

head(fgseaRes[order(pval), ], 10)
                           pathway         pval         padj      log2err
                            <char>        <num>        <num>        <num>
 1:       HALLMARK_NOTCH_SIGNALING 3.596565e-05 0.0004675534 0.5573322388
 2:          HALLMARK_ANGIOGENESIS 9.649323e-04 0.0062720600 0.4772708154
 3:       HALLMARK_SPERMATOGENESIS 3.342167e-01 1.0000000000 0.0064391585
 4:   HALLMARK_PANCREAS_BETA_CELLS 4.715489e-01 1.0000000000 0.0048298692
 5:     HALLMARK_KRAS_SIGNALING_DN 8.689013e-01 1.0000000000 0.0017721017
 6:        HALLMARK_APICAL_SURFACE 9.996100e-01 1.0000000000 0.0000935165
 7:    HALLMARK_HEDGEHOG_SIGNALING 9.997800e-01 1.0000000000 0.0000901134
 8:  HALLMARK_BILE_ACID_METABOLISM 1.000000e+00 1.0000000000 0.0000000000
 9:           HALLMARK_COAGULATION 1.000000e+00 1.0000000000 0.0000000000
10: HALLMARK_FATTY_ACID_METABOLISM 1.000000e+00 1.0000000000 0.0000000000
            ES        NES  size  leadingEdge
         <num>      <num> <int>       <list>
 1:  0.6388199  2.3898036    31 2648, 11....
 2:  0.4445579  1.7356069    35 6696, 70....
 3: -0.5487978 -1.0258898   135 56903, 8....
 4: -0.5614137 -1.0146693    40 5080, 47....
 5: -0.5031756 -0.9463433   194 56154, 2....
 6: -0.2987335 -0.5417834    44 116085, ....
 7: -0.2533869 -0.4560201    36 5340, 78....
 8: -0.3366708 -0.6269499   112 1581, 32....
 9: -0.2742501 -0.5128142   137 4153, 32....
10: -0.2290126 -0.4293097   158 2572, 76....
head(fgseaRes2[order(pval), ], 10)
                               pathway         pval         padj   log2err
                                <char>        <num>        <num>     <num>
 1: HALLMARK_UNFOLDED_PROTEIN_RESPONSE 2.519434e-32 6.550528e-31 1.4745625
 2:                HALLMARK_DNA_REPAIR 2.521495e-29 3.277944e-28 1.4099514
 3:         HALLMARK_PROTEIN_SECRETION 6.387479e-23 5.535815e-22 1.2378967
 4:                 HALLMARK_APOPTOSIS 1.611617e-22 1.047551e-21 1.2295041
 5: HALLMARK_INTERFERON_ALPHA_RESPONSE 9.927853e-22 5.162483e-21 1.2039752
 6:         HALLMARK_ANDROGEN_RESPONSE 5.673049e-17 2.458321e-16 1.0574636
 7:            HALLMARK_MYC_TARGETS_V2 7.470786e-17 2.774863e-16 1.0574636
 8:   HALLMARK_PI3K_AKT_MTOR_SIGNALING 1.538928e-15 5.001517e-15 1.0175448
 9:     HALLMARK_FATTY_ACID_METABOLISM 8.155750e-15 2.356105e-14 0.9865463
10:   HALLMARK_CHOLESTEROL_HOMEOSTASIS 6.985436e-13 1.816213e-12 0.9214260
           ES      NES  size  leadingEdge
        <num>    <num> <int>       <list>
 1: 0.6405570 4.335567   113 54541, 5....
 2: 0.5411742 3.844693   149 124583, ....
 3: 0.5901297 3.886446    95 9871, 10....
 4: 0.4474179 3.242312   160 914, 715....
 5: 0.5752868 3.810652    97 3627, 85....
 6: 0.5001549 3.331186    99 25803, 9....
 7: 0.6461827 3.875631    58 3336, 79....
 8: 0.4624624 3.106777   105 2810, 51....
 9: 0.3573006 2.577347   158 259307, ....
10: 0.4995212 3.124397    74 2990, 56....

Plot the most significantly enriched pathway.

plotEnrichment(
  hallmark_gs_list[[head(fgseaRes[order(pval), ], 1)$pathway]],
  my_ranks
) +
  ggplot2::labs(title=head(fgseaRes[order(pval), ], 1)$pathway)

Version Author Date
49a8145 Dave Tang 2025-01-24

Further reading

No consensus on the best way to rank.


sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

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

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] GSEABase_1.68.0      graph_1.84.1         annotate_1.84.0     
 [4] XML_3.99-0.18        ExperimentHub_2.14.0 AnnotationHub_3.14.0
 [7] BiocFileCache_2.14.0 dbplyr_2.5.0         msigdb_1.14.0       
[10] org.Hs.eg.db_3.20.0  AnnotationDbi_1.68.0 IRanges_2.40.1      
[13] S4Vectors_0.44.0     Biobase_2.66.0       BiocGenerics_0.52.0 
[16] ggplot2_3.5.1        fgsea_1.32.4         BiocManager_1.30.25 
[19] workflowr_1.7.1     

loaded via a namespace (and not attached):
 [1] DBI_1.2.3               rlang_1.1.4             magrittr_2.0.3         
 [4] git2r_0.35.0            compiler_4.4.1          RSQLite_2.3.9          
 [7] getPass_0.2-4           png_0.1-8               callr_3.7.6            
[10] vctrs_0.6.5             stringr_1.5.1           pkgconfig_2.0.3        
[13] crayon_1.5.3            fastmap_1.2.0           XVector_0.46.0         
[16] labeling_0.4.3          utf8_1.2.4              promises_1.3.2         
[19] rmarkdown_2.28          tzdb_0.4.0              UCSC.utils_1.2.0       
[22] ps_1.8.1                purrr_1.0.2             bit_4.5.0              
[25] xfun_0.48               zlibbioc_1.52.0         cachem_1.1.0           
[28] GenomeInfoDb_1.42.3     jsonlite_1.8.9          blob_1.2.4             
[31] highr_0.11              later_1.3.2             BiocParallel_1.40.0    
[34] parallel_4.4.1          R6_2.5.1                bslib_0.8.0            
[37] stringi_1.8.4           jquerylib_0.1.4         Rcpp_1.0.13            
[40] knitr_1.48              readr_2.1.5             httpuv_1.6.15          
[43] Matrix_1.7-0            tidyselect_1.2.1        rstudioapi_0.17.1      
[46] yaml_2.3.10             codetools_0.2-20        curl_6.2.1             
[49] processx_3.8.4          lattice_0.22-6          tibble_3.2.1           
[52] withr_3.0.2             KEGGREST_1.46.0         evaluate_1.0.1         
[55] Biostrings_2.74.1       pillar_1.10.1           filelock_1.0.3         
[58] whisker_0.4.1           generics_0.1.3          vroom_1.6.5            
[61] rprojroot_2.0.4         BiocVersion_3.20.0      hms_1.1.3              
[64] munsell_0.5.1           scales_1.3.0            xtable_1.8-4           
[67] glue_1.8.0              tools_4.4.1             data.table_1.16.2      
[70] fs_1.6.4                fastmatch_1.1-6         cowplot_1.1.3          
[73] grid_4.4.1              colorspace_2.1-1        GenomeInfoDbData_1.2.13
[76] cli_3.6.3               rappdirs_0.3.3          dplyr_1.1.4            
[79] gtable_0.3.6            sass_0.4.9              digest_0.6.37          
[82] farver_2.1.2            memoise_2.0.1           htmltools_0.5.8.1      
[85] lifecycle_1.0.4         httr_1.4.7              mime_0.12              
[88] bit64_4.5.2