Hi,
i performed NGS for small RNA and ran DESeq on the data. there is one miRNA that is upregulated by 2-fold in the patients compared to healthy controls. However, when i look at the raw normalized data, it is almost the same in both groups.
can someone tell me how i can perform deseq2 manually using a formula so that I can chack whether this result is real?
thanks
Iddo
Hi Iddo,
one of the factors that affect the fold change calculated by DESeq2 is the design variable that you set when you perform the analysis. If you are willing to share the commands you used and the actual normalized values of the miRNA, I could give you a more precise answer. You should also check if the "raw normalized" data you are looking at are normalized with the same method of the normalization performed by DESeq2 during the test.
On the other hand I don't think that there is a set of formulas that you can apply "manually" to perform the analysis without the deseq2 functions. Maybe you could try other programs as edgeR to see if you get similar results and then exclude a technical problem.
Kind regards
Riccardo
Bioinformatics data analyst at www.sequentiabiotech.com
Hi Iddo,
one of the factors that affect the fold change calculated by DESeq2 is the design variable that you set when you perform the analysis. If you are willing to share the commands you used and the actual normalized values of the miRNA, I could give you a more precise answer. You should also check if the "raw normalized" data you are looking at are normalized with the same method of the normalization performed by DESeq2 during the test.
On the other hand I don't think that there is a set of formulas that you can apply "manually" to perform the analysis without the deseq2 functions. Maybe you could try other programs as edgeR to see if you get similar results and then exclude a technical problem.
Kind regards
Riccardo
Bioinformatics data analyst at www.sequentiabiotech.com
Hi Dr. Magen,
Perhaps your question merits an explanation of how the standard DESeq workflow generates both the statistical inference and differential gene expression. For starters, DESeq2 uses dispersion estimates via maximum-likelihood, which assumes that genes of similar average expression strength possess similar dispersion, as previously described (Article Moderated Estimation of Fold Change and Dispersion for RNA-S...
). Once this is completed, gene-wise dispersion estimates are then shrunken according to the empirical Bayes approach, providing normalized count data for genes proportional to both the dispersion and sample size (which it determines from. your "colData" matrix). Differential expression is determined from normalized read counts via log2(fold-change) typically using the Wald test followed by Bonferroni-adjusted P-value (i.e. Q-value) for each aligned and annotated gene.A few things come to mind regarding your issue. First, check to make sure that you are pulling the "normalized" counts:
normcount
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