I am trying to do contrasts between 3 levels in a factor, and I am running the DESeq command in DESeq using the recommended betaPrior=FALSE argument. However, this command has been running for hours and I think there must be a problem. Its been "fitting model and testing" for three hours and counting...
My data set has 2 factors Treatment (treated vs non) and Type (control, high low).
Here is my code:
> dds <- DESeqDataSetFromMatrix(countData = counts,
+ colData = pData,
+ design = ~ Treatment + Type)
Usage note: the following factors have 3 or more levels:
Type
For DESeq2 versions < 1.3, if you plan on extracting results for
these factors, we recommend using betaPrior=FALSE as an argument
when calling DESeq().
As currently implemented in version 1.2, the log2 fold changes can
vary if the base level is changed, when extracting results for a
factor with 3 or more levels. A solution will be implemented in
version 1.3 which allows for the use of a beta prior and symmetric
log2 fold change estimates regardless of the selection of base level.
> colData(dds)$Type <- factor(colData(dds)$Type,
+ levels=c("control","low", "high"))
> dds <- DESeq(dds, betaPrior = FALSE)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
My data set has 2 factors Treatment (treated vs non) and Type (control, high low).
Here is my code:
> dds <- DESeqDataSetFromMatrix(countData = counts,
+ colData = pData,
+ design = ~ Treatment + Type)
Usage note: the following factors have 3 or more levels:
Type
For DESeq2 versions < 1.3, if you plan on extracting results for
these factors, we recommend using betaPrior=FALSE as an argument
when calling DESeq().
As currently implemented in version 1.2, the log2 fold changes can
vary if the base level is changed, when extracting results for a
factor with 3 or more levels. A solution will be implemented in
version 1.3 which allows for the use of a beta prior and symmetric
log2 fold change estimates regardless of the selection of base level.
> colData(dds)$Type <- factor(colData(dds)$Type,
+ levels=c("control","low", "high"))
> dds <- DESeq(dds, betaPrior = FALSE)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
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