Identify the biological pathways (gene sets from Reactome) that each metagene (factor) might belongs to.
Usage
runGSEA(
object,
genesets = NULL,
useW = TRUE,
useV = NULL,
customGenesets = NULL,
gene_sets = genesets,
mat_w = useW,
mat_v = useV,
custom_gene_sets = customGenesets
)Arguments
- object
A liger object with valid factorization result.
- genesets
Character vector of the Reactome gene sets names to be tested. Default
NULLuses all the gene sets from the Reactome.- useW
Logical, whether to use the shared factor loadings (\(W\)). Default
TRUE.- useV
A character vector of the names, a numeric or logical vector of the index of the datasets where the \(V\) matrices will be included for analysis. Default
NULLuses all datasets.- customGenesets
A named list of character vectors of entrez gene ids. Default
NULLuses all the gene symbols from the input matrix.- gene_sets, mat_w, mat_v, custom_gene_sets
Deprecated. See Usage section for replacement.
Examples
# \donttest{
if (requireNamespace("org.Hs.eg.db", quietly = TRUE) &&
requireNamespace("reactome.db", quietly = TRUE) &&
requireNamespace("fgsea", quietly = TRUE) &&
requireNamespace("AnnotationDbi", quietly = TRUE)) {
runGSEA(pbmcPlot)
}
#>
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (40.43% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (38.3% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (40.43% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (29.79% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (42.55% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (34.04% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (29.79% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (42.55% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (46.81% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (38.3% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (44.68% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (31.91% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (51.06% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (38.3% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (53.19% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (34.04% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (34.04% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (40.43% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (40.43% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> Warning: You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument in the fgsea function call.
#> Warning: There are ties in the preranked stats (31.91% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning: All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".
#> $Factor_1
#> pathway pval padj ES NES
#> 1 Viral Infection Pathways 0.001391118 0.005564473 0.6841373 1.848427
#> 2 Infectious disease 0.006055455 0.012110910 0.6324946 1.732690
#> 3 Disease 0.035265547 0.047020730 0.5555992 1.539175
#> 4 Immune System 0.402103474 0.402103474 0.3869393 1.060003
#> nMoreExtreme size leadingEdge
#> 1 12 16 6232, 61....
#> 2 56 18 6232, 61....
#> 3 333 21 6232, 61....
#> 4 3784 18 5473, 91....
#>
#> $Factor_2
#> pathway pval padj ES NES
#> 1 Infectious disease 0.0004224757 0.0008449514 0.7516368 2.048992
#> 2 Viral Infection Pathways 0.0002133561 0.0008449514 0.7945198 2.127748
#> 3 Disease 0.0014753926 0.0019671901 0.6878732 1.890748
#> 4 Immune System 0.4189944134 0.4189944134 -0.2442762 -1.005201
#> nMoreExtreme size leadingEdge
#> 1 3 18 7852, 62....
#> 2 1 16 7852, 62....
#> 3 13 21 7852, 62....
#> 4 224 18 929, 362....
#>
#> $Factor_3
#> pathway pval padj ES NES nMoreExtreme
#> 1 Disease 0.1021156 0.2902135 0.5033034 1.363403 974
#> 2 Infectious disease 0.1451067 0.2902135 0.4849868 1.296973 1372
#> 3 Viral Infection Pathways 0.2918218 0.3890957 0.4347337 1.148921 2743
#> 4 Immune System 0.4154513 0.4154513 0.3933589 1.051938 3930
#> size leadingEdge
#> 1 21 3553, 92....
#> 2 18 3553, 22....
#> 3 16 6202, 90....
#> 4 18 3553, 92....
#>
#> $Factor_4
#> pathway pval padj ES NES nMoreExtreme
#> 1 Infectious disease 0.1959253 0.3918505 0.4457936 1.2418414 1855
#> 2 Viral Infection Pathways 0.1652452 0.3918505 0.4642226 1.2730006 1549
#> 3 Disease 0.5654097 0.7538796 0.3355193 0.9424895 5367
#> 4 Immune System 0.9053098 0.9053098 0.2307424 0.6427760 8575
#> size leadingEdge
#> 1 18 9636, 61....
#> 2 16 9636, 61....
#> 3 21 9636, 61....
#> 4 18 91543, 9....
#>
#> $Factor_5
#> pathway pval padj ES NES
#> 1 Infectious disease 0.06196468 0.1239294 0.5286897 1.4474224
#> 2 Viral Infection Pathways 0.05807003 0.1239294 0.5352879 1.4438988
#> 3 Disease 0.26604925 0.3547323 0.4263150 1.1795495
#> 4 Immune System 0.90440943 0.9044094 0.2309144 0.6321869
#> nMoreExtreme size leadingEdge
#> 1 585 18 3553, 96....
#> 2 543 16 9636, 62....
#> 3 2527 21 3553, 96....
#> 4 8552 18 972, 355....
#>
#> $Factor_6
#> pathway pval padj ES NES
#> 1 Immune System 0.02084869 0.08339476 0.5831827 1.576855
#> 2 Infectious disease 0.27787722 0.37050297 0.4286276 1.158957
#> 3 Viral Infection Pathways 0.24860011 0.37050297 0.4431857 1.186587
#> 4 Disease 0.40515615 0.40515615 0.3858369 1.055685
#> nMoreExtreme size leadingEdge
#> 1 197 18 2214, 51....
#> 2 2638 18 2214, 96....
#> 3 2352 16 9636, 61....
#> 4 3865 21 2214, 96....
#>
#> $Factor_7
#> pathway pval padj ES NES
#> 1 Infectious disease 0.001468275 0.005873099 0.6880527 1.8578428
#> 2 Disease 0.012445095 0.016593460 0.6088180 1.6578106
#> 3 Viral Infection Pathways 0.008969083 0.016593460 0.6283282 1.6766993
#> 4 Immune System 0.965285789 0.965285789 0.2017394 0.5447259
#> nMoreExtreme size leadingEdge
#> 1 13 18 2214, 62....
#> 2 118 21 2214, 62....
#> 3 84 16 6228, 90....
#> 4 9203 18 10578, 2....
#>
#> $Factor_8
#> pathway pval padj ES NES
#> 1 Infectious disease 0.0001065757 0.0002146614 0.7910399 2.175216
#> 2 Viral Infection Pathways 0.0001073307 0.0002146614 0.8328494 2.265038
#> 3 Disease 0.0004227883 0.0005637177 0.7311131 2.035824
#> 4 Immune System 0.8661408931 0.8661408931 0.2444750 0.672262
#> nMoreExtreme size leadingEdge
#> 1 0 18 6133, 61....
#> 2 0 16 6133, 61....
#> 3 3 21 6133, 61....
#> 4 8126 18 10578, 6....
#>
#> $Factor_9
#> pathway pval padj ES NES
#> 1 Immune System 0.01214187 0.04856747 0.6100554 1.659854
#> 2 Disease 0.37211274 0.51569892 0.3951049 1.085591
#> 3 Viral Infection Pathways 0.38677419 0.51569892 0.4006452 1.078623
#> 4 Infectious disease 0.51869209 0.51869209 0.3590636 0.976949
#> nMoreExtreme size leadingEdge
#> 1 113 18 9636, 91....
#> 2 3511 21 9636, 62....
#> 3 3596 16 9636, 61....
#> 4 4869 18 9636, 61....
#>
#> $Factor_10
#> pathway pval padj ES NES
#> 1 Viral Infection Pathways 0.003203075 0.01281230 0.6689367 1.819988
#> 2 Infectious disease 0.009693225 0.01938645 0.6113634 1.680414
#> 3 Disease 0.060532432 0.08070991 0.5244344 1.459576
#> 4 Immune System 0.288492707 0.28849271 -0.2735749 -1.155878
#> nMoreExtreme size leadingEdge
#> 1 29 16 7852, 62....
#> 2 90 18 7852, 62....
#> 3 572 21 7852, 62....
#> 4 177 18 929, 355....
#>
#> $Factor_11
#> pathway pval padj ES NES nMoreExtreme
#> 1 Infectious disease 0.1345868 0.2691736 0.4849411 1.318384 1266
#> 2 Viral Infection Pathways 0.1274247 0.2691736 0.4949309 1.326601 1188
#> 3 Disease 0.4201841 0.5602454 0.3823529 1.052155 3971
#> 4 Immune System 0.9417888 0.9417888 0.2086250 0.567178 8865
#> size leadingEdge
#> 1 18 2214, 61....
#> 2 16 6133, 61....
#> 3 21 2214, 61....
#> 4 18 2207, 62....
#>
#> $Factor_12
#> pathway pval padj ES NES
#> 1 Disease 0.02467465 0.09869859 0.5886130 1.573397
#> 2 Infectious disease 0.09727301 0.15975875 0.5168881 1.364840
#> 3 Viral Infection Pathways 0.11981906 0.15975875 0.5088522 1.330409
#> 4 Immune System 0.32305924 0.32305924 0.4253640 1.123171
#> nMoreExtreme size leadingEdge
#> 1 236 21 6279, 62....
#> 2 930 18 3553, 61....
#> 3 1138 16 6135, 61....
#> 4 3091 18 6279, 62....
#>
#> $Factor_13
#> pathway pval padj ES NES
#> 1 Infectious disease 0.0005426525 0.001085305 0.7125681 1.9947014
#> 2 Viral Infection Pathways 0.0003276540 0.001085305 0.7171567 1.9818528
#> 3 Disease 0.0062205062 0.008294008 0.6230769 1.7643122
#> 4 Immune System 0.9898605830 0.989860583 -0.1143908 -0.4973585
#> nMoreExtreme size leadingEdge
#> 1 4 18 4869, 90....
#> 2 2 16 4869, 90....
#> 3 57 21 4869, 90....
#> 4 780 18 929, 513....
#>
#> $Factor_14
#> pathway pval padj ES NES
#> 1 Disease 0.0001050641 0.0001415879 0.8417415 2.308089
#> 2 Infectious disease 0.0001055632 0.0001415879 0.8800578 2.382872
#> 3 Viral Infection Pathways 0.0001061909 0.0001415879 0.9076006 2.432131
#> 4 Immune System 0.2687969925 0.2687969925 -0.2882457 -1.176610
#> nMoreExtreme size leadingEdge
#> 1 0 21 6164, 61....
#> 2 0 18 6164, 61....
#> 3 0 16 6164, 61....
#> 4 142 18 929, 362....
#>
#> $Factor_15
#> pathway pval padj ES NES
#> 1 Viral Infection Pathways 0.0002189381 0.0008757526 0.7639766 2.1129491
#> 2 Infectious disease 0.0006509004 0.0013018008 0.7089421 1.9856468
#> 3 Disease 0.0050690250 0.0067587000 0.6304771 1.7843747
#> 4 Immune System 0.8229550879 0.8229550879 0.2552083 0.7148025
#> nMoreExtreme size leadingEdge
#> 1 1 16 4869, 62....
#> 2 5 18 4869, 62....
#> 3 46 21 4869, 62....
#> 4 7585 18 5473, 4869
#>
#> $Factor_16
#> pathway pval padj ES NES
#> 1 Viral Infection Pathways 0.0009634943 0.003853977 0.7242736 1.988848
#> 2 Infectious disease 0.0020129251 0.004025850 0.6729600 1.872986
#> 3 Disease 0.0122647494 0.016352999 0.5949352 1.670171
#> 4 Immune System 0.3605683837 0.360568384 -0.2577690 -1.064250
#> nMoreExtreme size leadingEdge
#> 1 8 16 7852, 62....
#> 2 18 18 7852, 62....
#> 3 115 21 7852, 62....
#> 4 202 18 929, 513....
#>
#> $Factor_17
#> pathway pval padj ES NES
#> 1 Infectious disease 0.0004203889 0.001681555 0.7231558 1.9422854
#> 2 Viral Infection Pathways 0.0011650074 0.002330015 0.7091217 1.8841903
#> 3 Disease 0.0035553697 0.004740493 0.6524684 1.7706436
#> 4 Immune System 0.5881147541 0.588114754 -0.2288559 -0.9160614
#> nMoreExtreme size leadingEdge
#> 1 3 18 6164, 62....
#> 2 10 16 6164, 62....
#> 3 33 21 6164, 62....
#> 4 286 18 929, 513....
#>
#> $Factor_18
#> pathway pval padj ES NES nMoreExtreme
#> 1 Immune System 0.08980426 0.3078081 0.5036946 1.400687 834
#> 2 Infectious disease 0.15390407 0.3078081 0.4669226 1.298430 1430
#> 3 Viral Infection Pathways 0.33178929 0.4423857 0.4085270 1.124577 3079
#> 4 Disease 0.45788521 0.4578852 0.3603539 1.017387 4299
#> size leadingEdge
#> 1 18 3627, 26....
#> 2 18 2214, 96....
#> 3 16 9636, 62....
#> 4 21 2214, 96....
#>
#> $Factor_19
#> pathway pval padj ES NES
#> 1 Viral Infection Pathways 0.0004265302 0.001706121 0.7083495 1.9105478
#> 2 Infectious disease 0.0021128248 0.004225650 0.6656027 1.8229746
#> 3 Disease 0.0118784821 0.015837976 0.6023632 1.6642634
#> 4 Immune System 0.8160785971 0.816078597 0.2716142 0.7439058
#> nMoreExtreme size leadingEdge
#> 1 3 16 6232, 78....
#> 2 19 18 6232, 78....
#> 3 112 21 6232, 78....
#> 4 7724 18 6402, 26....
#>
#> $Factor_20
#> pathway pval padj ES NES
#> 1 Infectious disease 0.03930039 0.07860078 0.5537159 1.4995176
#> 2 Viral Infection Pathways 0.03593005 0.07860078 0.5629286 1.5040992
#> 3 Disease 0.17402243 0.23202991 0.4622005 1.2645045
#> 4 Immune System 0.60056896 0.60056896 0.3386207 0.9170184
#> nMoreExtreme size leadingEdge
#> 1 372 18 7852, 62....
#> 2 338 16 7852, 62....
#> 3 1659 21 7852, 62....
#> 4 5699 18 10578, 5....
#>
# }