Hi all,

in relation to a mail from January this yearin the r-help community, I followed Simon's advice to do my analyses in DESeq2 instead of DESeq.

I am working on an RNASeq from c. elegans. I have mapped the data with the ensembl genome build WBcel215. I have ran tophat2 to map and featureCounts to counts the reads (both with the defaults parameters).

I have two conditions, control and a knock-out with each three replica. Now I am trying to find differentially regulated genes between the two conditions using DESeq2.

This is the script I am using to read my raw count table into DESeq2:

```
featureCountTable <- read.table("featureCountTable_RawCounts.txt", sep="\t", quote=F)
colData <- data.frame(row.names=names(featureCountTable), condition = c(rep("wt",3), rep("cpb3", 3)))
cds <- DESeqDataSetFromMatrix (
    countData =     featureCountTable,
    colData   =     colData,  
    design    = ~    condition
    )
fit = DESeq(cds)
res = results(fit)
```

But I am getting the same problem with DESeq2 as I have got with DESeq. When I ran the DESeq command I get a warning:
```
Warning messages:
1: In log(ifelse(y == 0, 1, y/mu)) : NaNs produced
2: step size truncated due to divergence
```

So again I have tried to change the fitType.

    fit = DESeq(cds, fitType="local")

Which than came back without any warnings.

Apparently this time both fitTypes are almost similar (at least to my inexperienced eyes.)

I add  both dispersion Plots. The red line goes through the point-cloud in both cases (as Simon defined a good fit in the last communication, I wish it would have bin so easy :-) .

In the local fit type there a more outliers and the right end of the slope is going up again. I am not sure whether or not this is a good thing or not.

So, my question is - which of the two options is better?

I understand, that in general the parametric (default) option is better, but here it gives me a warning, so that something in the fit calculations is not good.

How can I understand theses plots?

Thanks for the help,

Assa

default/parametric fit   <img alt="DESeq2_parametric" src="http://s23.postimg.org/uk4pitj47/DESesq2_parametric.png" style="height:180px; width:179px" />

local fit     <img alt="DESeq2 local fit" src="http://s23.postimg.org/pvopnmtxj/DESesq2_local.png" style="height:180px; width:176px" />

P.S.

I also tried to post it on the bioC help site, but got no responses, so I try it here.
This warning message can be ignored. It is coming from a call to R's glm() function in capturing the (dispersion ~ mean) trend. And the trend is fit iteratively until convergence, so though glm() complained at some step, it produced a final fit without error. If the parametric trend does not converge, local fit is substituted.

What version of DESeq2 are you using? I thought I had worked on more comprehensible warning reporting in this function.

I would go with the parametric to avoid the curve at the right side, although it shouldn't matter much.