ColData and ERROR: rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest
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LorManj • 0
@8cb9912e
Last seen 4 weeks ago
Portugal

Hi

I wonder to know if It is necessary that the row.names of the colData matrix match with the col.names of the countData matrix. If the names in first column from colData (colData [,1]) match with the col.names of the countData matrix but the row.names don't, will there be errors in the analysis?

On the other hand, after using the DESeq function, I got the following warning: "5 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest". How could I identify those rows?

I have tried the solutions from DESeq2 Error: rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest and maxit parameter cannot be changed in DESeq function but they didn't work.

Thank you so much

    dds <- DESeqDataSetFromMatrix(countData = countData,
                                          colData = colData,
                                          design = ~ Type+Sample+Type:Sample
    dds <- estimateSizeFactors(dds)
    nc <- counts(dds, normalized=TRUE)
    filter <- rowSums(nc >= 10) >= 2
    dds <- dds[filter,]
    dds <- estimateSizeFactors(dds)
    dds <- estimateDispersions(dds)
    dds <- nbinomWaldTest(dds, maxit=10000)

#or
dds <- DESeqDataSetFromMatrix(countData = countData,
                              colData = colData,
                              design = ~ Type+Sample+Type:Sample
dds <- DESeq(dds)
ddsClean <- dds[which(mcols(dds)$betaConv),]
results(ddsClean, list (c("Sample_Tp_vs_Ti)
DESeq2 • 158 views
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Entering edit mode
@mikelove
Last seen 44 minutes ago
United States

Yes, definitely make sure the row names of colData match the col names of countData.

This is the same as saying you make sure that the labels of the samples are not swapped. Otherwise the analysis will be entirely wrong.

The rows are these:

which(mcols(object)$betaConv)

Often they have very low counts and can be filtered out at the beginning of the analysis with:

keep <- rowSums(counts(dds) >= 10) >= X
dds <- dds[keep,]
# then the rest of your code...
dds <- DESeq(dds, ...)

Here you can put X as the sample size of one of your groups.

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