Question: rna seq replicates not clustering
1
gravatar for guillaume.rbt
10 months ago by
guillaume.rbt530
France
guillaume.rbt530 wrote:

Hi all,

I'm currently doing a differential expression analysis on RNA-seq data. After counting the reads with salmon, and normalizing with deseq2, I've plotted the dispersion of my samples thanks to a PCA.

I have 3 replicates per conditions, and I was kind of waiting for them to cluster together, but I found out that there is not really a clustering of replicates, the samples being spread across the PCA.

I'm not really familiar with this kind of output, and I was wondering if not seing clustering of replicates could be normal (due to biological variation between replicates,even with the sam conditions), or if I should worried about something before going to DE analysis.

Thanks for your inputs,

Guillaume

ADD COMMENTlink written 10 months ago by guillaume.rbt530
1

Please give some details on the experimental setup: Cell line or primary samples, which organism, which treatment etc.

ADD REPLYlink written 10 months ago by ATpoint14k

I have 72 samples of Vitis vinifera leaves, with 4 changing treatments, and 3 biological replicates for each set of conditions. Mainly I wanted to know if a DE analysis can still be relevant with a low transcriptomic concordance between biological replicates.

ADD REPLYlink written 10 months ago by guillaume.rbt530

What have you actually input to the PCA functions, and which PCA functions have you used? Please show your exact code. Also, have you performed pre-filtering steps on the raw counts prior to normalisation?

ADD REPLYlink written 10 months ago by Kevin Blighe39k

My input is the "vst" normalized table of counts, filtered for genes with no reads. I used the function plotPCA from the DEseq2 package.

ADD REPLYlink written 10 months ago by guillaume.rbt530
2

Try this code: A: PCA plot from read count matrix from RNA-Seq

You'll get a different answer. Why? - because the DESeq2 plotPCA function filters a large proportion of your genes based on variance prior to performing the PCA transformation.

ADD REPLYlink written 10 months ago by Kevin Blighe39k
1

You could also simply increase the ntop option to Inf (plotPCA(foo, ntop=Inf) or something like that).

ADD REPLYlink modified 10 months ago • written 10 months ago by Devon Ryan88k

How to add images to a Biostars post

ADD REPLYlink written 10 months ago by WouterDeCoster37k
1
gravatar for Devon Ryan
10 months ago by
Devon Ryan88k
Freiburg, Germany
Devon Ryan88k wrote:

Don't read too much into PCA plots, they're telling you more about whether you have outlier samples than whether continuing with differential expression analysis is worth while.

ADD COMMENTlink written 10 months ago by Devon Ryan88k

Ok thank for the tip, I will continue to explore the data then.

ADD REPLYlink written 10 months ago by guillaume.rbt530
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