Is it really CONSIDERED a batch effect when I extracted information from FASTQ file, reading first lines of it, and got the run_number and flowcell_ID from those lines? Or I am unintentionally reading too much by extracting such information for my RNA-sequence dataset which actually is not a batch effect?
Hey,
It is not 'over-interpreting' - the information that you have extracted can indeed be used to identify potential batches. In RNA-seq, the sequencing run and flowcell are well known sources of technical variation / batch effects, and, for this reason, are sometimes explicitly included in the statistical model. The flowcell ID, in particular, can be important.
To check if these are actually driving a batch effect in your data, I would advise to generate a PCA bi-plot (or heatmap) of your normalised counts and colour the samples by flowcell / run. If you see a clear separation, then, yes, there is a batch effect. In that case, you can use this information as a covariate in your model, e.g., in DESeq2's design formula.
Kevin
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Thank you Kevin for your reply. Kindly, can you advise me after checking the PCAs of my dataset?
I would not consider the flow cell meaningfully and clearly impacting things based on your non-batch corrected plots. I'd leave it out of your model design personally. Note that checking additional PCs can also be helpful (PC3/4), but it can quickly become a ghost hunt. True batch effects are typically pretty obvious.
Thank you for the reply. What do you suggest me that should I keep all 3 replicates for each treatment or drop some of them? as from PCA it appears that atleast two replicates for most of my treatments cluster closer. My PCA confuses me.