Hi,

we're working on a data set from D. melanogaster and examining a series of TP of different knock-outs. we have several RNA-Seq data sets with triplicates. I was analyzing both the differences in each pair of time-points using DESeq2 as well as the time-series analysis using Mfuzz.

I have tried to compare the expression values (or more precise profiles) created by Mfuzz and DESeq2 and got confusing results. Below is an example for one gene, its raw values as well as the calculated values from Mfuzz and normalised values from DESeq2

Plotting these values gives a completely different expression behavior of the same gene across all time-points (see plots attached).

I would like to know, if these two different expression profiles can be solely explained by the difference between the DESeq2 normalisation and the Mfuzz standardisation methods. If not, is there another explanation for this two plots?

thanks in advance 

Assa

DESeq2's normalized counts are pretty straightforward: these are counts / size factors, where the size factors account for differences in sequencing depth across samples. The normalized counts are then scaled count values, with the lowest and highest sequencing depth samples scaled up- and down- respectively to be similar scale to the middle samples.

I don't know anything about the normalization of Mfuzz. You can email the authors of that method for more information if it is not explained in their documentation or paper.

(Remember, the normalized counts in DESeq2 are just provided for visualization, they aren't actually used in the DESeq2 model which uses all samples on the original counts scale and keeps track of size factors on the right-hand side of the equations as shown in the DESeq2 paper.)