Question: DeSeq2 data transformation for visualization
1
gravatar for sunil.mangalam
3.9 years ago by
United States
sunil.mangalam10 wrote:

Hi,

 I am facing some difficulties with data transformations of my single cell RNA-Seq data analyzed using DESeq2. This data set is different from typical RNA-Seq experiments.. For example, there is a subset of genes which will be present in one group and totally absent in the other, unlike typical data sets where down regulated genes will still be expressed at lower level. The variation between replicates is also high, and so, we have at least ten replicates for each condition. Even with all these limitations, I am able to get a meaningful result from this analysis.

But the rlog transformation is not optimal for my analysis. I get a warning that more than 10% of the genes have outliers and it suggests doing vst.The vst works for without any warning, but I am still worried if this is optimal or if there is a way to do a better transformation for making heat maps and PCA.

 Also, is there a way to extract the normalized values without any transformation? DESeq used to output this, but this function is not in the DESeq2 vignette.

Thanks for your help!

data transformation deseq2 • 2.0k views
ADD COMMENTlink modified 11 days ago by ATpoint21k • written 3.9 years ago by sunil.mangalam10

you can get the normalized counts with the function: counts(dds, normalized=T)
all the other values computed by DESeq2 can be accessed with: mcols(dds)

ADD REPLYlink written 3.9 years ago by Martombo2.5k

When you run, the genes present in on place that aren't in the another set will get NA in the dataset. This could generate warnings later. Remove the rows that contain NA  within padj with:

result1 <- result1[ !is.na(Cresultado$padj  ), ]

ADD REPLYlink written 3.9 years ago by tiago2112871.1k
0
gravatar for ATpoint
11 days ago by
ATpoint21k
Germany
ATpoint21k wrote:

Year 2019: See the manual's recommendation on scRNA-seq data: http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#recommendations-for-single-cell-analysis

ADD COMMENTlink written 11 days ago by ATpoint21k
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