KEGG analysis in R

Question

[san96]

Hello everyone, I want to perform a KEGG pathway enrichment analysis in R and I would like to request help, I have a table of IDs and the associated KEGG term, as well as a table of differential expression of the genes on which I want to do the KEGG analysis, anyone could you guide me?

My KEGG list:

ID                                  KEGG 
Zm00001eb0961100    ko:K05359
Zm00001eb3216800    ko:K21596
Zm00001eb3236400    -
Zm00001eb0802600    ko:K10406
Zm00001eb0264900    ko:K13946

My DEGs list (deseq2):

    baseMean    log2FoldChange  lfcSE   stat    pvalue  padj
Zm00001eb000370 83.58504092 2.580437493 0.33040911  7.809825506 5.73E-15    1.79E-13
Zm00001eb000450 134.6974945 1.118574691 0.174756454 6.400763268 1.55E-10    2.35E-09
Zm00001eb000790 68.87059177 1.251076899 0.285848224 4.376717405 1.20E-05    7.31E-05
Zm00001eb000850 123.1027878 8.837192547 1.221856886 7.23259217  4.74E-13    1.09E-11

I really appreciated the support

[san96]

Thanks so much.

[letizia.ottaviani]

Hello,

I have a problem to do the analysis in R. I want to kindly ask you: How did you perform KEGG pathway enrichment analysis in R for Zea mays? I have the same table output from DESeq2 Thank you in advance for your help

Answer 1

[txema.heredia]

gProfiler does enrichment/over-representation analysis simultaneously on pathways from the KEGG database and many others

https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html

https://biit.cs.ut.ee/gprofiler/page/r

You can follow that with creating pathview plots as described at the end of the clusterprofiler tutorial Jean-Karim posted.

Answer 2

[Jean-Karim Heriche]

Have a look at the clusterprofiler Bioconductor package and its supporting guide book chapter on the topic.

[san96]

Hello, I already managed to perform the analysis. Now the question I have is how to convert the KEGG terms to the description of the metabolic pathway:

Could you help me or give me suggestions?

# Select the needed columns
kegg_final <- kegg_unique[, c(2,1)]
head(kegg_final, 5)

 KEGG.KO           query
    <char>          <char>
1:  K05359 Zm00001eb096110
2:  K21596 Zm00001eb321680
3:  K10406 Zm00001eb080260
4:  K13946 Zm00001eb026490
5:  K08286 Zm00001eb189060



# Differential
proteins <- read.table("proteinas.txt", sep = "\t")
head(proteins, 5)


# Define your list of gene/protein names of interest
protein_ids <- proteins$V1

# Enrichment analysis
enr_res <- enricher(protein_ids, TERM2GENE = kegg_final, pvalueCutoff = 0.05, pAdjustMethod = "BH", qvalueCutoff = 0.05, minGSSize = 10)
dotplot(enr_res, showCategory=15)
barplot(enr_res, showCategory=15)

[Jean-Karim Heriche]

Maybe the ggkegg package is what you need or if you just need to retrieve info directly from the database, use the KEGG REST API via the KEGGREST package. Note that using the API is only free for academic users and that requests are limited to 3 per seconds (check the web site).

[san96]

Thanks so much.