Hi Jon,
as WouterDeCoster says QN might be possible for RNA-seq but is not common. I suggest reading the manuals of e.g. edgeR and DESeq2 to learn about normalization. Aditionally check the videos linked below which nicely explain the normalization techniques that are part of the differential pipeline of these two tools. Beyond that DESeq2 offers two functions, vst and rlog that not only normalize counts with respect to library size and composition but also try to unlock the variance dependency from the mean. If these vocabulary are new to you search around in the web, there is plenty of forum and blog entries on normalization and RNA-seq available.
I suggest you use one of the mentioned packages for differential analysis (normalization will be taken care of internally) and vst for everything else (e.g. clustering/PCA). Note that both rlog and vst return log2 scaled counts, check the manuals and vignettes.
In order to check normalization efficiency I would also not use Z-scored heatmaps. They are rather uninformative on that matter. Instead use MA-plots (e.g. via the smoothScatter function in R to get areas colored by density or heatscatter from LSD) and then check if the bulk of the data centers somewhat along y=0.
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16 months ago by
ATpoint ♦ 44k
Hi, Google Drive is not a recommended host for images as it doesn't support embedding on biostars. Could I trouble you to please follow this guide and upload on imgbb?
I made the changes for you already. You have to use the image button and paste in the full link to the image including the suffix (.png or similar). In this case the link would be
https://i.ibb.co/d7QHxTW/Screen-Shot-2019-09-24-at-7-52-23-PM.pngThank you, I am trying to add another box plot image
So you have RNA-seq, and you use
normalizeBetweenArrays()? RNA-seq requires a different analysis than a microarray. Please follow a well-tested tutorial, like this one from bioconductor.You could use voom normalization from limma, and add the quantile normalization in there with argument
normalize.method = "quantile". However, start with real counts, derived from featureCounts instead of RSEM.