Question: Using DESeq w/ counts from different RNASeq experiments
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gravatar for chupalav
16 days ago by
chupalav0
chupalav0 wrote:

Hello there

I have count files from 3 different sequencing experiments on similar samples but with different illumina total loads (according to basespace) . Raw data was processed using cutadapt-bowtie-samtools. Now i want to run some DiffExpression experiments using Deseq2. It is known to normalize the data according to library size and composition - but would it be correct to feed it w data from different experiments? would it handle the normalization, or some additional one is required (normalize using cpm, etc) ? Thank you for your help.

Regards Chupalav

rna-seq deseq statistics de • 41 views
ADD COMMENTlink modified 15 days ago by Jennifer Hillman Jackson25k • written 16 days ago by chupalav0
0
gravatar for Jennifer Hillman Jackson
15 days ago by
United States
Jennifer Hillman Jackson25k wrote:

Hello,

Advice about combining experiments: https://support.bioconductor.org/p/91560/. There is much more about this topic at the Bioconductor forum in the context of what experiments represent: how to model the experiment based on content/samples, how to generate counts, when and at which step to normalize (or not), plus QA options.

Example Galaxy workflows that can be modified to suit your use-case: https://galaxyproject.github.io/training-material/topics/transcriptomics/. This one also exists but is not quite ready for publication, however, it can be used as a reference until then: https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/limma-voom_fastqs_to_counts/tutorial.html

Some of the tools available in Galaxy for DE analysis:

Get the counts:

  • featureCounts Measure gene expression in RNA-Seq experiments from SAM or BAM files.

Then use one or more of these. Help is on the tool forms, with more details in the Bioconductor FAQs/Forum:

  • limma Perform differential expression with limma-voom or limma-trend
  • edgeR Perform differential expression of count data
  • DESeq2 Determines differentially expressed features from count tables

Thanks! Jen, Galaxy team

ADD COMMENTlink modified 15 days ago • written 15 days ago by Jennifer Hillman Jackson25k
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