Differential Expression Analysis for Single Population RNASeq in Deseq2
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@saicharanp18-20483
Last seen 21 months ago
United States

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.

deseq2 normalization • 775 views
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@mikelove
Last seen 1 hour ago
United States

If you feel like you need to model the additional zeros not compatible with the NB with high mean and low dispersion for this data, take a look at the scRNA section of the vignette where we provide code for integrating with zinbwave.

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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.

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Try the LRT, I think this is also what is recommended in the section I cited above.

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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.

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maxCooks is the only outlier statistic we offer in DESeq2

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