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How to find which DE genes fall under statistically significant criteria using scanpy DEG analysis

I have performed differential expression analysis on my dataset using the Scanpy package and Wilcoxon-rank sum testing.To carry out the same, I used the following tutorial link :

https://nbisweden.github.io/workshop-scRNAseq/labs/compiled/scanpy/scanpy_05_dge.html

Now, my issue is I don't see a very big difference in expression values for the DE genes when I plot them, particularly comparing between control and conditions. How to make sure which DE genes fall under statistically significant criteria?

rna-seq expression cell single differential scanpy
• 1,277 views • link
written by shome • shome

I used the following code :

result = adata_sub.uns['rank_genes_groups']
groups = result['names'].dtype.names
pd.DataFrame(
    {group + '_' + key[:1]: result[key][group]
    for group in groups for key in ['names', 'pvals_adj','logfoldchanges']}

to get the adjusted p-values and logFC,which is as follows :

enter image description here

Generally p-value < 0.01 is considered statistically significant.But, in my case, all the genes in the dataset are having p-value < 0.01, which is making me confused about whether I have approached it appropriately.

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written by shome • shome

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