topGO for Gene Ontology and Gene Set enrichment
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fromhj304 ▴ 10
Last seen 7.0 years ago

Hi all, I'm trying to get Gene Ontology and Gene Set Enrichment using topGO. I'm pretty novice to bioinformatics using R, so I need your help!

So from RNA-Seq raw count data (31973 genes), I used DESeq2 to get differentially expressed genes, using results(), and from there I sorted by p-values. I didn't use adjusted p-values, since there are too many genes, when I adjusted p-values, all of them were above 0.9, which did not give differentially expressed genes. So, I sorted by p-values, and then about to look for genes that are less than 0.05, however, since I didn't use adjusted p-values, I reduced the p-value cutoff to 0.01. QUESTION: Is it acceptable to use reduced p-values (0.05 -> 0.01) instead of adjusted p-values?

QUESTION2: What is the best R tools package to use this .csv file (consists of Ensembl gene ID, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj) as an input and give me Gene Ontology analysis and Gene Set Enrichment as outputs?




topgo gene ontology gene set enrichment rna-seq bioinformatics • 2.2k views
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maybe you can follow this workflow:

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Last seen 4 days ago
European Union

The use of more strict p-values is up to the researcher, however, consider that if you take 0.01 as threshold, you are saying you accept 1 wrong for every 100 DEG.

If you correct for multiple testing you are taking into account that each test done is related to the others (because it is taken from the same sample), if you don't take that into account the results might be misleading.

However it is strange you didn't found any DEG, have you corrected batch effects, and taken into account the design of the experiment?

For Gene Ontology enrichment there are the packages GOstat, and topGO (mainly) for gene set enrichment, you can use limma, DOSE, ReactomePA, clusterProfiler, GSA, gsva, and many others


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