Question: clusterprofiler GSEA function no term enriched under specific pvalueCutoff...
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gravatar for tianbian0507
3 months ago by
tianbian05070 wrote:

I am relatively new to clusterProfiler and enrichment analysis, I tried to follow the steps from here https://yulab-smu.github.io/clusterProfiler-book/chapter3.html#msigdb-analysis to do GO, MSigDb analysis and wikiPathway on our own dataset. The gsea returned "no term enriched under specific pvalueCutoff..." for both of them. I am not sure if I did something wrong or if this is the real result which there are no enriched terms.

geneList <- global$avg_logFC
names(geneList) <- global$`rownames(Lymphoid.markers)`
geneList <- sort(geneList, decreasing = TRUE)

up.genes <- global[global$avg_logFC > 0, 1] 
dn.genes <- global[global$avg_logFC < 0, 1] 

up.genes <- bitr(up.genes$`rownames(Lymphoid.markers)`, fromType = "SYMBOL", toType = c("ENTREZID"), OrgDb = org.Hs.eg.db)
dn.genes <- bitr(dn.genes$`rownames(Lymphoid.markers)`, fromType = "SYMBOL", toType = c("ENTREZID"), OrgDb = org.Hs.eg.db)

msigdb <- msigdbr(species = "Homo sapiens")
#head(msigdb, 2) %>% as.data.frame
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_id, entrez_gene) 
m_t2n <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_id, gs_name) 

geneList2 <- geneList2[!is.na(names(geneList2))]
emsig <- enricher(up.genes[[2]], TERM2GENE=m_t2g)
emsig2 <- GSEA(geneList2, TERM2GENE = m_t2g, TERM2NAME = m_t2n)
head(summary(emsig2))
[1] ID              Description     setSize         enrichmentScore
[5] NES             pvalue          p.adjust        qvalues        
<0 rows> (or 0-length row.names)

I used the differentially expressed gene list and separated the list into two lists based on their avg_FC values. The list is the result of Seurat.

> head(global)
# A tibble: 6 x 6
  `rownames(Lymphoid.markers)`    p_val avg_logFC pct.1 pct.2 p_val_adj
  <chr>                           <dbl>     <dbl> <dbl> <dbl>     <dbl>
1 RPS26                        3.04e-89     0.941 0.979 0.912  4.59e-85
2 APOD                         6.56e-70    -1.32  0.189 0.665  9.91e-66
3 TXNIP                        5.39e-57    -0.678 0.606 0.862  8.15e-53
4 HLA-C                        8.96e-50     0.599 0.989 0.971  1.35e-45
5 UBC                          9.03e-49     0.580 0.977 0.938  1.36e-44
6 RGCC                         6.56e-48     0.875 0.767 0.509  9.91e-44

Any advice will be appreciated!

clusterprofiler • 350 views
ADD COMMENTlink modified 3 months ago • written 3 months ago by tianbian05070

Can you do head(up.genes[[2]]) and head(m_t2g)?

ADD REPLYlink written 3 months ago by igor8.8k
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