Hi,
I have data from single cell RNASeq experiments that I am doing differential expression analysis on using DeSeq2. This dataset is not typical-the variability between replicates is much higher, for example and there are libraries with skewed distribution where relatively fewer number of genes may contribute to a large proportion of reads. Things begin to get complicated when I do data normalization, I get size factors from 14- 0.03 although the total number of raw counts does not vary by more than 2.2 fold between individual libraries. Is there a way to fix the normalization? I guess at some point I have to exclude the outlier libraries, but first I wish to try and improve the normalization before throwing out potentially useful data... My gut feeling is that things will be better if I can fix the normalization because DeSeq has worked for other single cell experiments where the size factors were more along the expected lines....Have tried to use the FPKM normalization and method in cuffdiff but it is much worser probably because the libraries are very 3' biased...Thanks for your thoughts and inputs on this.
I have data from single cell RNASeq experiments that I am doing differential expression analysis on using DeSeq2. This dataset is not typical-the variability between replicates is much higher, for example and there are libraries with skewed distribution where relatively fewer number of genes may contribute to a large proportion of reads. Things begin to get complicated when I do data normalization, I get size factors from 14- 0.03 although the total number of raw counts does not vary by more than 2.2 fold between individual libraries. Is there a way to fix the normalization? I guess at some point I have to exclude the outlier libraries, but first I wish to try and improve the normalization before throwing out potentially useful data... My gut feeling is that things will be better if I can fix the normalization because DeSeq has worked for other single cell experiments where the size factors were more along the expected lines....Have tried to use the FPKM normalization and method in cuffdiff but it is much worser probably because the libraries are very 3' biased...Thanks for your thoughts and inputs on this.
Comment