Problem extracting differentially expressed genes using diffxpy
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Entering edit mode
3.0 years ago
zenbio ▴ 10

Hi,

I am new at performing RNAseq single cell analysis, so perhaps my question is naive. But here it is: I used the diffxpy package to find differentially expressed genes between two conditions in a given cluster of cells using wald test. To extract the genes with significant log fold change, I apply the following condition on pandas results dataframe:

condition_m = (test_megak.summary()["qval"] <= 0.05) & (abs(test_megak.summary()["log2fc"]) >= 1) 
gene_indices_m = test_megak.summary().index[condition_m]
gene_indices_list_m = gene_indices_m.tolist()

I end up with a list of around 3000 indices out of the 23000 gene tested. But when I run the volcano plot of diffxpy on the test results, it only shows a few point meeting my criterias for differentially expressed (i.e corrected pval <= 0.05 and abs(log2fc) >=1):

test_megak.plot_volcano(corrected_pval=True,log10_p_threshold=-20,log2_fc_threshold=5, min_fc=2, alpha=0.05, size=20)

enter image description here

I actually know that there should be only a handful of genes differentially expressed, certainly not 3000. So the volcano plot corresponds more to what I am expecting.

Can anybody tell me what I am doing wrong? How can I extract the genes names corresponding to the colored points I see on the volcano plot using the diffxpy api?

diffxpy genes differentially expressed • 1.6k views
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Entering edit mode
3.0 years ago
Marco Pannone ▴ 810

I would suggest you run DE analysis using DESeq2, which is more of a gold standard in RNA-seq analysis. You can find documentation here with proper examples: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

All you need to know about DESeq2 is written there, also showing you how to make plots and extract DEGs accordingly to your desired thresholds.

Good luck!

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DESeq2 is definitely not the gold standard for scRNA-seq.

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It's totally fine to perform DE analysis using DESeq2 also with scRNA-seq data, just keeping in mind to change some specific parameters, as also stated in the official documentation.

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