Question of using DESeq for non-replicate samples

[camelbbs]

HI.

I've tried the package DESeq and want to apply in our datasets: two samples without replicates.

I first summarized the counts in a table:
brain liver
uc001aaa.3 0 0
uc001aac.4 0 0
uc001aae.4 0 0
...
...

By following code:

>over<-read.table("wokao.txt",header=T,row.names=1)
>conds<-c("brain","liver")
>cds<-newCountDataSet(over,conds)
>cds<-estimateSizeFactors(cds)
>cds<-estimateDispersions(cds,method="blind",sharingMode="fit-only")
Error in parametricDispersionFit(means, disps) :
Parametric dispersion fit failed. Try a local fit and/or a pooled estimation. (See '?estimateDispersions')

>cds2=cds[,c("brain","liver")]
>cds2=estimateSizeFactors(cds2)
>cds2=estimateDispersions(cds2,method="blind",sharingMode="fit-only")
Error in parametricDispersionFit(means, disps) :
Parametric dispersion fit failed. Try a local fit and/or a pooled estimation. (See '?estimateDispersions')

I have no idea why there is the error. May you give some guides.

[cascoamarillo]

Hi
Not sure what DESeq version are you using. I did that analysis once (no replicates) with DESeq 1.9.15 :

countsTable <- read.table(args[5], header=TRUE, stringsAsFactors=TRUE)
rownames( countsTable ) <- countsTable$gene
countsTable <- countsTable[ , -1 ]
conds <- c("WT", "MU" )
cds <- newCountDataSet( countsTable, conds )
cds <- estimateSizeFactors( cds )
sizeFactors( cds )
cds <- estimateDispersions( cds, method="blind", fitType="local", sharingMode="fit-only" )
res <- nbinomTest( cds, "WT", "MU" )

Where the input table is like:

gene	WT	MU	
Gene_00001	0	0
Gene_00002	20	8
Gene_00003	3	0

Hope it works!

[camelbbs]

Thanks, I have got it.

Hi
Not sure what DESeq version are you using. I did that analysis once (no replicates) with DESeq 1.9.15 :

countsTable <- read.table(args[5], header=TRUE, stringsAsFactors=TRUE)
rownames( countsTable ) <- countsTable$gene
countsTable <- countsTable[ , -1 ]
conds <- c("WT", "MU" )
cds <- newCountDataSet( countsTable, conds )
cds <- estimateSizeFactors( cds )
sizeFactors( cds )
cds <- estimateDispersions( cds, method="blind", fitType="local", sharingMode="fit-only" )
res <- nbinomTest( cds, "WT", "MU" )

Where the input table is like:

gene	WT	MU	
Gene_00001	0	0
Gene_00002	20	8
Gene_00003	3	0

Hope it works!