Hi, I am working with Single population Rna seq data,each sample has around 500 cells laser captured from antibody stained unfixed tissue. I have too many zeros in the data set and there are some cases where only one sample in a group have zero counts , i think which is affecting my results and analysis. Is doing IQR outliers and performing comparisions advised in this case, because i tried doing outliers and doing comparisions, which gave me pretty good results. How does Deseq2 deal with these type of data. Any suggestions on this is higly appreciated.
Thanks.
Hi, I am analyzing single population RNAseq data for differential expression analysis using DESeq2 and also tried Zinbwave integration which improved number of genes recovered and statistical significance. However i still see problems with outliers effects on comparisons like below Group A: 0 0 0 0 12.74787975 0 Group B: 0 0 0 0.885504516 0 0
Deseq2 reports above gene as DE with p = 0.01
Is there any effective way of doing outlier analysis and filter the genes.
Thanks.
Try the LRT, I think this is also what is recommended in the section I cited above.
The above results are using Zinbwave, LRT and sftype="poscount". Is there a way where we can do Outliers for a gene by IQR and perform comparisons. Is that really recommended in DE analysis.
Thanks.
maxCooks is the only outlier statistic we offer in DESeq2