Question

Data normalization

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nolwenn • 0

@c3b2b52f

Last seen 3.7 years ago

France

Hello,

I'm new in bioinformatic and I would like to normalize HTseqcount data to do a survival analysis. How can I choose the best normalization ?

Thank you for your help !!

DESeq2 Normalization • 2.5k views

ADD COMMENT • link 3.7 years ago nolwenn • 0

score 1 · Answer 1 · 2021-04-07

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Michael Love 43k

@mikelove

Last seen 1 day ago

United States

See the DESeq2 vignette on variance stabilizing transformations. This is one approach to dealing with sequencing depth and heteroskedasticity, so you can work with scaled, transformed data downstream. The data is approximately log2 scale after running vst.

ADD COMMENT • link 3.7 years ago Michael Love 43k

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I try this transformation :

DESeq_object <- DESeqDataSetFromMatrix(countData = count,
                                colData = coldata,
                                design= ~ gender)
vst_object <- varianceStabilizingTransformation(DESeq_object)
boxplot(assay(vst_object))

I have this boxplot : boxplot normalized data

All medians are not align ... How can I align all medians to have a good normalization ?

ADD REPLY • link 3.7 years ago nolwenn • 0

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VST is not quantile normalization. It adjusts for sequencing depth and transforms to log2 scale. It also does not perform sample QC for you.

ADD REPLY • link 3.7 years ago Michael Love 43k

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..and, for the survival analysis part, I would take the VST expression levels and then follow my tutorial from Step 2, here: Tutorial:Survival analysis with gene expression

ADD REPLY • link 3.7 years ago Kevin Blighe ★ 4.0k

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I would like to predict how many month a patient stay in life after the diagnostics. I need to test each gene independently ?

ADD REPLY • link 3.7 years ago nolwenn • 0

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Can I use the median ratio method normalization and VST method ? How can I do QC ? I'm sorry I'm new in bioinformatic ...

ADD REPLY • link 3.7 years ago nolwenn • 0

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This is out of scope for what I can provide on the support site. Consult a bioinformatician perhaps, or a general purpose site such as biostars (see Kevin's post above).

ADD REPLY • link 3.7 years ago Michael Love 43k

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Thank you for your help.

ADD REPLY • link 3.7 years ago nolwenn • 0

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I'm sorry, I have a new question with vst. I try to use vst and represente data with meanSdPlot like in the vignette and I don't have a real difference between normalized data and vst data. It's normal ?

DESeq_object <- estimateSizeFactors(DESeq_object)
counts_normalized <- counts(DESeq_object, normalized = TRUE)
meanSdPlot(log2(counts_normalized+1), ranks = FALSE)

graph

vst_object <- varianceStabilizingTransformation(DESeq_object, blind = TRUE)
meanSdPlot(assay(vst_object), ranks = FALSE)

graph

Thank you !

ADD REPLY • link 3.7 years ago nolwenn • 0

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As I said, VST is similar to log2 transformation, but has better stabilization of variance of small count genes.

ADD REPLY • link 3.7 years ago Michael Love 43k

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The user now posted on Biostars https://www.biostars.org/p/9464165/#9464165

ADD REPLY • link 3.7 years ago Kevin Blighe ★ 4.0k

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Thank you for your answer !

ADD REPLY • link 3.7 years ago nolwenn • 0