Question

pathway for single cell RNA-seq

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6.2 years ago

kanwarjag ★ 1.2k

I have a single cell RNA-seq data form 10X genomics. I want to perform pathway analysis of top 50 genes of each cluster on the the basis of DE. These clusters represent cells with similar expression of genes. So in pathway analysis of these top 50 genes I want to identify what possible type of cells they may be. There are several types of pathway analysis- like IPA, Enrichr, David and so on. Any suggestion which tool will be the the best to at least suggest that clustered cells are of a particular cell type and has a specific pathways enriched in them. e.g if Stem cell pathway is enriched we may say that these are pluripotent cells. Any suggestion?

pathway • 8.7k views

ADD COMMENT • link updated 6.2 years ago by igor 13k • written 6.2 years ago by kanwarjag ★ 1.2k

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Did you get the fastq files? assuming that you do. From the single cell RNA-Seq Data to a pathway analysis is quite a path...way. A suggestion is to you look at https://pachterlab.github.io/kallisto/singlecell.html and http://satijalab.org/seurat/get_started.html

ADD REPLY • link 6.2 years ago by tiago211287 ★ 1.4k

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I have already analyzed the data and has top differentially expressed genes specific for each cluster. Instead of using known markers I want to use enrichment/ pathway analysis that what type of cells are in each cluster. Apology If my original question was not explained clearly.

ADD REPLY • link 6.2 years ago by kanwarjag ★ 1.2k

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Please use ADD COMMENT/ADD REPLY when responding to existing posts to keep threads logically organized.

ADD REPLY • link 6.2 years ago by GenoMax 141k

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Biologists around here seem to like IPA, but I haven't tried it myself.

ADD REPLY • link 6.2 years ago by Madelaine Gogol 5.3k

score 1 · Answer 1 · 2018-01-20

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6.2 years ago

tiago211287 ★ 1.4k

There are many ways of doing enrichment analysis. I usually like the TopGO package because it let me construct my background.

ADD COMMENT • link 6.2 years ago by tiago211287 ★ 1.4k

score 1 · Answer 2 · 2018-01-20

If you only take the top 50 genes, you might be missing a lot of information. Also, depending on what the other clusters are, the differentially expressed genes will change. You can take the average expression of all genes for each cluster and then use a cell type deconvolution tool. Check this previous discussion where there are a few great suggestions: Deconvolution Methods on RNA-Seq Data (Mixed cell types)