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3 years ago GSEA和GSVA，再也不用去下载gmt文件咯 by 生信星球
前言
1.R包和示例数据
rm(list = ls())
library(GSVA)
library(GSEABase)
library(msigdbr)
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(limma)
#devtools::install_github("xjsun1221/tinyarray")
library(tinyarray)
load("exp_Group_deg.Rdata")
ls()
## [1] "deg" "exp" "Group"
exp[1:4,1:4]
## GSM1366348 GSM1366349 GSM1366350 GSM1366351
## RFC2 8.932805 8.679543 8.625015 8.637085
## HSPA6 9.383421 8.605809 9.462774 9.898573
## PAX8 7.916751 8.500635 8.258467 8.553656
## GUCA1A 5.085221 2.414033 1.718570 4.311794
dim(exp)
## [1] 20161 22
Group
## [1] RA RA RA RA RA RA
## [7] RA RA RA RA RA RA
## [13] RA control control control control control
## [19] control control control control
## Levels: control RA
head(deg)
## logFC AveExpr t P.Value
## HADHA -1.633518 11.204696 -16.14895 8.628803e-14
## LRRFIP1 -1.713884 11.965832 -15.06767 3.572073e-13
## OTUD4 -2.274611 7.651265 -13.43343 3.618635e-12
## FKBP2 -1.935657 8.628444 -13.04331 6.501205e-12
## ILRUN -1.893808 8.896532 -12.38173 1.812689e-11
## IGF2R -1.691681 10.302132 -12.00890 3.290947e-11
## adj.P.Val B change
## HADHA 2.358899e-09 20.92048 down
## LRRFIP1 4.882577e-09 19.68466 down
## OTUD4 3.297481e-08 17.61994 down
## FKBP2 3.554534e-08 17.08858 down
## ILRUN 6.607251e-08 16.15073 down
## IGF2R 1.124578e-07 15.60094 down
2.端详msigdbr这个包
1.囊括了11个物种
msigdbr_species()
## # A tibble: 11 x 2
## species_name species_common_name
## <chr> <chr>
## 1 Bos taurus cattle
## 2 Caenorhabditis elegans roundworm
## 3 Canis lupus familiaris dog
## 4 Danio rerio zebrafish
## 5 Drosophila melanogaster fruit fly
## 6 Gallus gallus chicken
## 7 Homo sapiens human
## 8 Mus musculus house mouse
## 9 Rattus norvegicus Norway rat
## 10 Saccharomyces cerevisiae baker's or brewer's yeast
## 11 Sus scrofa pig
2.看人类有哪些基因集
human <- msigdbr(species = "Homo sapiens")
human[1:4,1:4]
## # A tibble: 4 x 4
## gs_cat gs_subcat gs_name entrez_gene
## <chr> <chr> <chr> <int>
## 1 C3 MIR:MIR_Legacy AAACCAC_MIR140 10257
## 2 C3 MIR:MIR_Legacy AAACCAC_MIR140 23172
## 3 C3 MIR:MIR_Legacy AAACCAC_MIR140 81
## 4 C3 MIR:MIR_Legacy AAACCAC_MIR140 90
table(human[,1])
##
## C1 C2 C3 C4 C5 C6
## 40056 520069 734948 91173 1223276 30540
## C7 C8 H
## 945462 54466 7321
H: hallmark gene sets C1: positional gene sets C2: curated gene sets C3: motif gene sets C4: computational gene sets C5: GO gene sets C6: oncogenic signatures C7: immunologic signatures
3.KEGG和GO
table(human$gs_subcat)
##
## CGN CGP
## 1077845 42544 374969
## CM CP CP:BIOCARTA
## 48629 4520 4813
## CP:KEGG CP:PID CP:REACTOME
## 12797 8050 88301
## CP:WIKIPATHWAYS GO:BP GO:CC
## 26619 663648 95709
## GO:MF HPO MIR:MIR_Legacy
## 104978 358941 34163
## MIR:MIRDB TFT:GTRD TFT:TFT_Legacy
## 372126 173572 155087
KEGG_df = msigdbr(species = "Homo sapiens",category = "C2",subcategory = "CP:KEGG") %>%
dplyr::select(gs_exact_source,gene_symbol)
head(KEGG_df)
## # A tibble: 6 x 2
## gs_exact_source gene_symbol
## <chr> <chr>
## 1 hsa02010 ABCA1
## 2 hsa02010 ABCA10
## 3 hsa02010 ABCA12
## 4 hsa02010 ABCA13
## 5 hsa02010 ABCA2
## 6 hsa02010 ABCA3
# 基因数量
length(unique(KEGG_df$gene_symbol))
## [1] 5245
# 通路数量
length(unique(KEGG_df$gs_exact_source))
## [1] 186
GO_df = msigdbr(species = "Homo sapiens",category = "C5") %>%
dplyr::select(gene_symbol,gs_exact_source,gs_subcat)
dim(GO_df)
## [1] 1223276 3
GO_df = GO_df[GO_df$gs_subcat!="HPO",]
table(GO_df$gs_subcat)
##
## GO:BP GO:CC GO:MF
## 663648 95709 104978
GO_df = GO_df[,c(2,1)]
head(GO_df)
## # A tibble: 6 x 2
## gs_exact_source gene_symbol
## <chr> <chr>
## 1 GO:0004645 GDPGP1
## 2 GO:0004645 MTAP
## 3 GO:0004645 PYGB
## 4 GO:0004645 PYGL
## 5 GO:0004645 PYGM
## 6 GO:0004645 TYMP
# 基因数量
length(unique(GO_df$gene_symbol))
## [1] 19276
# terms数量
length(unique(GO_df$gs_exact_source))
## [1] 10271
3.做GSEA
ge = deg$logFC
names(ge) = rownames(deg)
ge = sort(ge,decreasing = T)
head(ge)
## OLR1 COL1A1 OLFM4 H3C8 CRISP3 ZFP36L2
## 3.835314 3.790745 3.427338 3.180043 2.956932 2.909744
head(GO_df)
## # A tibble: 6 x 2
## gs_exact_source gene_symbol
## <chr> <chr>
## 1 GO:0004645 GDPGP1
## 2 GO:0004645 MTAP
## 3 GO:0004645 PYGB
## 4 GO:0004645 PYGL
## 5 GO:0004645 PYGM
## 6 GO:0004645 TYMP
em <- GSEA(ge, TERM2GENE = GO_df)
#画个图来看看
gseaplot2(em, geneSetID = 1, title = em$Description[1])
4.做GSVA
exp[1:4,1:4]
## GSM1366348 GSM1366349 GSM1366350 GSM1366351
## RFC2 8.932805 8.679543 8.625015 8.637085
## HSPA6 9.383421 8.605809 9.462774 9.898573
## PAX8 7.916751 8.500635 8.258467 8.553656
## GUCA1A 5.085221 2.414033 1.718570 4.311794
dim(exp)
## [1] 20161 22
kegg_list = split(KEGG_df$gene_symbol,KEGG_df$gs_exact_source)
lapply(kegg_list[1:3], head)
## $hsa00010
## [1] "ACSS1" "ACSS2" "ADH1A" "ADH1B" "ADH1C" "ADH4"
##
## $hsa00020
## [1] "ACLY" "ACO1" "ACO2" "CS" "DLAT" "DLD"
##
## $hsa00030
## [1] "ALDOA" "ALDOB" "ALDOC" "DERA" "FBP1" "FBP2"
KEGG_ES <- gsva(expr=exp,
gset.idx.list=kegg_list,
parallel.sz=32) #自己电脑parallel.sz写5就好，线程数
## Setting parallel calculations through a MulticoreParam back-end
## with workers=32 and tasks=100.
## Estimating GSVA scores for 186 gene sets.
## Estimating ECDFs with Gaussian kernels
## Estimating ECDFs in parallel
##
|=============================================| 100%
KEGG_ES[1:4,1:4]
## GSM1366348 GSM1366349 GSM1366350 GSM1366351
## hsa00010 0.09619189 -0.2030580 -0.2575823 0.13014453
## hsa00020 0.41117392 -0.4726020 -0.4338775 -0.26959554
## hsa00030 0.03504000 -0.4179045 -0.4280300 -0.09500726
## hsa00040 0.16960156 -0.1391068 -0.2706679 -0.02557599
go_list = split(GO_df$gene_symbol,GO_df$gs_exact_source)
lapply(go_list[1:3], head)
## $`GO:0000002`
## [1] "AKT3" "DNA2" "FLCN" "LIG3" "LONP1" "MEF2A"
##
## $`GO:0000003`
## [1] "AAAS" "ABAT" "ABCC2" "ABHD2" "ACE" "ACOD1"
##
## $`GO:0000012`
## [1] "AP002495.1" "APLF" "APTX" "ERCC6"
## [5] "ERCC8" "LIG4"
GO_ES <- gsva(expr=exp,
gset.idx.list=go_list,
parallel.sz=32) #自己电脑parallel.sz写5就好，线程数
## Setting parallel calculations through a MulticoreParam back-end
## with workers=32 and tasks=100.
## Estimating GSVA scores for 10266 gene sets.
## Estimating ECDFs with Gaussian kernels
## Estimating ECDFs in parallel
##
|=============================================| 100%
KEGG_ES[1:4,1:4]
## GSM1366348 GSM1366349 GSM1366350 GSM1366351
## hsa00010 0.09619189 -0.2030580 -0.2575823 0.13014453
## hsa00020 0.41117392 -0.4726020 -0.4338775 -0.26959554
## hsa00030 0.03504000 -0.4179045 -0.4280300 -0.09500726
## hsa00040 0.16960156 -0.1391068 -0.2706679 -0.02557599
By default, kcdf="Gaussian" which is suitable when input expression values are continuous, such as microarray fluorescent units in logarithmic scale, RNA-seq log-CPMs, log-RPKMs or log-TPMs. When input expression values are integer counts, such as those derived from RNA-seq experiments, then this argument should be set to kcdf="Poisson".
5.GSVA 差异分析
design = model.matrix(~Group)
fit = lmFit(GO_ES, design)
fit = eBayes(fit)
DEG = topTable(fit, coef = 2, number = Inf)
head(DEG)
## logFC AveExpr t
## GO:0070180 -1.0388245 -0.01574550 -10.988613
## GO:0000104 -0.9768231 0.01008804 -10.300079
## GO:0045273 -0.9768231 0.01008804 -10.300079
## GO:0010626 -0.8295852 0.01190610 -9.550725
## GO:0043426 0.8652926 -0.02684029 9.088616
## GO:0010499 -0.8009479 0.02692075 -9.065723
## P.Value adj.P.Val B
## GO:0070180 8.519107e-11 8.745715e-07 14.61388
## GO:0000104 3.066710e-10 1.049428e-06 13.42665
## GO:0045273 3.066710e-10 1.049428e-06 13.42665
## GO:0010626 1.318927e-09 3.385026e-06 12.06086
## GO:0043426 3.358846e-09 6.023685e-06 11.17890
## GO:0010499 3.520564e-09 6.023685e-06 11.13440
draw_heatmap(GO_ES[head(rownames(DEG),200),],Group)
draw_volcano(DEG,pkg = 4,logFC_cutoff = 0.5)
draw_pca(GO_ES,Group)
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