Reduce the number of nodes appear in the cnetplot of clusterprofiler
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YNKIM ▴ 10
@72de1776
Last seen 21 months ago
South Korea

Hello all,

I have a question regarding the cnetplot from the clusterprofiler package in R.

As shown in the first figure, the number of nodes appear in my cnetplot is too many that many data points (genes) are unlabeled. It seems that all genes in my gene list that belong to a certain category appear in the plot regardless of their FC values. Is there any way that I can reduce the number of nodes in the plot to such that only highly significant genes are displayed? (e.g., log2FC > abs(2.5)).
I want to change my figure to look like the one from the vignettes.

My genelist is a list of DEGs (n = 734) identified based on log2FC > abs(1.5), and p-value < 0.05.

I've tried to change several arguments in cnetplot, such as foldChange and cex_label_gene. Reducing cex_label_gene only results in the smaller size of the gene font, rather than reducing the actual number of the nodes.

Thank you in advance for your help!:)

my cnetplot

cnetplot from vignettes

my code

DEG = read.csv('DEG.csv', header = T)
geneList = DEG[,2]
names(geneList) = as.character(DEG[,1])
geneList = sort(geneList, decreasing = TRUE)

gse <- gseGO(gene = geneList, 
             ont ="BP", 
             keyType = "SYMBOL", 
             nPerm = 10000, 
             minGSSize = 3, 
             maxGSSize = 1000, 
             pvalueCutoff = 0.05, 
             verbose = TRUE, 
             OrgDb = "org.Hs.eg.db", 
             pAdjustMethod = "none")

cnetplot(gse, 
         foldChange = geneList,
         circular = TRUE, 
         colorEdge = TRUE,
         showCategory = category,
         cex_label_gene = 1)

sessionInfo( )
cnetplot R clusterProfiler • 6.0k views
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It appears you would like to check for GO-BP categories that are enriched in you list of DEG (734 genes). You should therefore rather use the enrichGO function, and not the gseGO function! The latter performs a gene set enrichment analysis (GSEA), and for that the whole dataset is required in which genes are ranked by a metric (e.g. signed p-value or log2(fold change)). See e.g. here for more info on the differences between the 2 methods (chapter 5.2 and 5.3).

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Guido Hooiveld ★ 4.1k
@guido-hooiveld-2020
Last seen 16 hours ago
Wageningen University, Wageningen, the …

Please note that it is not correct that all genes that belong to a certain category are plotted. I agree that it is not explicitly mentioned at the help page of the cnetplot function, but if you check chapter 15.3 of the clusterProfiler book you will read "GSEA result is also supported with only core enriched genes displayed." Since you performed a GSEA analysis, only the core enriched genes are plotted. Core enriched genes = leading edge genes in GSEA terminology.

Below some code to confirm that only the core enriched genes are plotted, and not all genes belonging to a category:

> library(clusterProfiler)
> 
> ## for reproducibility, use the provided example dataset
> data(geneList, package = "DOSE")
> 
> ## perform GSEA using GO-BP categories
> ## note that you used **very relaxed criteria** for min and max size!
> gse <- gseGO(gene = geneList, 
+              ont ="BP", 
+              eps = 0,
+              minGSSize = 3, 
+              maxGSSize = 1000, 
+              pvalueCutoff = 0.05, 
+              verbose = TRUE, 
+              OrgDb = "org.Hs.eg.db", 
+              pAdjustMethod = "none")
preparing geneSet collections...
GSEA analysis...
leading edge analysis...
done...
> 
> ## check
> gse
#
# Gene Set Enrichment Analysis
#
#...@organism    Homo sapiens 
#...@setType     BP 
#...@keytype     ENTREZID 
#...@geneList    Named num [1:12495] 4.57 4.51 4.42 4.14 3.88 ...
 - attr(*, "names")= chr [1:12495] "4312" "8318" "10874" "55143" ...
#...nPerm        
#...pvalues adjusted by 'none' with cutoff <0.05 
#...1910 enriched terms found
'data.frame':   1910 obs. of  11 variables:
 $ ID             : chr  "GO:0051276" "GO:0000278" "GO:1903047" "GO:0007059" ...
 $ Description    : chr  "chromosome organization" "mitotic cell cycle" "mitotic cell cycle process" "chromosome segregation" ...
 $ setSize        : int  455 744 617 278 188 165 233 357 942 391 ...
 $ enrichmentScore: num  0.524 0.443 0.467 0.57 0.646 ...
 $ NES            : num  2.56 2.25 2.34 2.65 2.87 ...
 $ pvalue         : num  9.72e-31 1.36e-28 2.78e-28 2.09e-25 2.61e-25 ...
 $ p.adjust       : num  9.72e-31 1.36e-28 2.78e-28 2.09e-25 2.61e-25 ...
 $ qvalue         : num  8.79e-27 6.14e-25 8.37e-25 4.71e-22 4.72e-22 ...
 $ rank           : num  1374 1264 1257 1374 532 ...
 $ leading_edge   : chr  "tags=24%, list=11%, signal=22%" "tags=21%, list=10%, signal=20%" "tags=22%, list=10%, signal=21%" "tags=26%, list=11%, signal=24%" ...
 $ core_enrichment: chr  "8318/55143/991/9493/1062/10403/7153/23397/9787/11065/55355/51203/10460/4751/55839/983/54821/4085/9837/81930/816"| __truncated__ "8318/55143/991/2305/9493/1062/4605/9833/9133/10403/23397/79733/6241/55165/9787/11065/220134/55872/51203/22974/1"| __truncated__ "8318/55143/991/2305/9493/1062/4605/9833/9133/10403/23397/6241/55165/9787/11065/55872/51203/22974/10460/4751/273"| __truncated__ "55143/991/9493/1062/10403/7153/23397/9787/11065/55355/220134/51203/10460/4751/55839/4085/81930/81620/332/3832/7"| __truncated__ ...
#...Citation
 T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.
 clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.
 The Innovation. 2021, 2(3):100141 

> 
> ## what are the 2 most significantly enriched gene sets?
> as.data.frame(gse)[1:2,]
                   ID             Description setSize enrichmentScore      NES
GO:0051276 GO:0051276 chromosome organization     455       0.5235628 2.557910
GO:0000278 GO:0000278      mitotic cell cycle     744       0.4429047 2.248928
                 pvalue     p.adjust       qvalue rank
GO:0051276 9.720836e-31 9.720836e-31 8.789683e-27 1374
GO:0000278 1.357929e-28 1.357929e-28 6.139269e-25 1264
                             leading_edge
GO:0051276 tags=24%, list=11%, signal=22%
GO:0000278 tags=21%, list=10%, signal=20%
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              core_enrichment
GO:0051276                                                                                                                                                                                                                                               8318/55143/991/9493/1062/10403/7153/23397/9787/11065/55355/51203/10460/4751/55839/983/54821/4085/9837/81930/81620/332/3832/2146/7272/64151/9212/51659/9319/9055/3833/146909/891/24137/4174/9232/4171/9928/11004/990/5347/29127/26255/701/9156/11130/57405/10615/3159/79075/2491/8438/9700/5888/898/3149/11339/3070/9134/4175/4173/2237/22948/5984/9918/1058/84296/699/4609/1063/5111/64785/9401/26271/55055/641/1763/54892/8357/3024/4176/3148/79980/3006/4436/5982/9735/908/23310/8607/3008/10051/10576/3009/4172/9631/83990/5885/2072/84722/51115/7283/5983/4678/5588/54908/10592/51377/4683/54069
GO:0000278 8318/55143/991/2305/9493/1062/4605/9833/9133/10403/23397/79733/6241/55165/9787/11065/220134/55872/51203/22974/10460/4751/27338/890/983/4085/9837/5080/81930/81620/332/3832/2146/7272/64151/9212/1111/9319/9055/3833/146909/10112/51514/6790/891/24137/9232/4171/1033/9928/1164/11004/993/4603/57348/990/5347/29127/26255/701/51512/11130/1978/57405/10615/1894/79075/9700/5888/898/56992/4998/4288/10733/339479/1163/9134/4175/4173/29899/10926/54962/6502/440/994/6347/9918/29980/1058/699/4609/6491/1063/5111/64785/26271/55055/51053/641/1869/1029/1763/3925/54892/55159/8317/7277/5902/2296/79980/9585/4436/9735/5641/586/5721/10950/23310/1871/1031/2253/79915/11169/55726/8877/80086/9088/995/10051/1104/84790/1019/284403/637/4172/79866/5885/80124/11200/11040/10263/9032/203068/7027/2290/940/1761/23175/84722/6873/51115/7283/8883/10381
> 
> ## extract the (number of) core enriched genes of each GO category,
> ## as well as the total number of genes that make up a GO category.
> ## note that these numbers are not identical!
> 
> library(stringr)
> core.genes <- str_split(as.data.frame(gse)[,"core_enrichment"] , "/")
> nmbr.core.genes <- lengths(core.genes)
> 
> head( cbind( as.data.frame(gse)[ ,c("Description", "setSize")] , nmbr.core.genes) )
                                    Description setSize nmbr.core.genes
GO:0051276              chromosome organization     455             110
GO:0000278                   mitotic cell cycle     744             154
GO:1903047           mitotic cell cycle process     617             135
GO:0007059               chromosome segregation     278              73
GO:0000819         sister chromatid segregation     188              46
GO:0000070 mitotic sister chromatid segregation     165              44
> 






> ## Visualize the default, unfiltered results in a cnetplot
> 
> ## first convert gene ID to Symbol
> gse.orig <- setReadable(gse, 'org.Hs.eg.db', 'ENTREZID')
> 
> ## use new way of specifying visualization options
> color.params = list(foldChange = geneList, edge = TRUE)
> cex.params = list(category_label = 0.6, gene_label = 0.4)
> 
> cnetplot(gse.orig,
+          showCategory = c("mitotic sister chromatid segregation",
+                           "sister chromatid segregation"),
+          circular = TRUE,
+          color.params =  color.params,
+          cex.params = cex.params)
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
>

enter image description here
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Part 2 of my reply; had to be split in 2 posts because of character limitation.

As said, the cnetplot visualization is thus as expected. Yet, if you still would like to reduce the number of genes in the plot, this is possible by applying a small hack. This hack consists of replacing for each GO category the full set of core enrichment genes in the core_enrichment column of the gseaResult object by a subset of genes of that GO category that you would like to visualize. These could for example be only genes that are significantly regulated as determined by a statistical test. However, please note that by doing so you are changing a well known, standard way of visualizing GSEA results! Whether this is OK to do I leave to you, and if you do so you need to be clear about that.

> ## prepare a list of relevant (significant) genes.
> ## for now I select the genes that have a ranking metric > 2.5
> 
> my.selected.genes <- names( geneList[abs(geneList) > 2.5] )   
> 
> ## for each GO category, only keep the core enriched genes that are in that category *and*
> ## have been selected.
> 
> filtered.core.genes <- sapply(
+                           lapply(core.genes, function(x) x[x %in% my.selected.genes]),
+                        paste, collapse="/")
> 
> ## Perform 'hack' by replacing core_enrichment in the gseaResult object.
> gse@result$core_enrichment <- filtered.core.genes
> 
> ## again convert gene ID to Symbol
> gse.filtered <- setReadable(gse, 'org.Hs.eg.db', 'ENTREZID')
> 
> ## make cnetplot of filtered results
> cnetplot(gse.filtered,
+          showCategory = c("mitotic sister chromatid segregation",
+                           "sister chromatid segregation"),
+          circular = TRUE,
+          color.params =  color.params,
+          cex.params = cex.params)
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
> 
>

enter image description here

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