Hi all, I have 250 samples from healthy and disease states. I want to integrate gene expression data into metabolic model and do flux balance analysis. Can I use FPKM directly for this work or should I normalize FPKM? For example in some publications I see that some researchers used quantile normalization of FPKM.
Any help is welcome
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1 answer
FPKM is not a perfect normalization method. I'd suggest you extract normalized counts from DESeq2.
FPKM data is not suitable for cross-sample comparisons. DESeq2 can indeed perform adjustment for gene length when you import via tximport.
I finally found the way to do this. Here is the R code for whoever needs it.
cts <- as.matrix(read.csv("Count_data.txt", row.names = 1, header= TRUE, sep="\t"))
coldata <- read.csv("coldata.txt", row.names = 1, sep="\t")
dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ condition)
txdb <- makeTxDbFromGFF("gencode.v19.annotation.gtf_withproteinids", format="gtf", circ_seqs=character())
ebg <- exonsBy(txdb, by="gene")
rowRanges(dds) = GRangesList(ebg)
FPKM = fpkm(dds)
Sorry, I was flying across the ocean. Glad that you got the solution. Where are the gene lengths being stored in your code, though? - presumably from gencode.v19.annotation.gtf_withproteinids
You asked the question here: https://support.bioconductor.org/p/116021/#116067
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You probably need some additional transformation to make it suitable for modeling - the distribution of FPKM (even after inter-library normalization) is not standard!
Yep - and some methods also require scaling. Also remember some ML methods have large problems with highly correlated features
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Who? Data on FPKM scale is already normalised, but not for cross-sample differences.
For example see this paper : https://www.pnas.org/content/115/50/E11874.short At the supplementary data, FPKM quantile normalization has been explained.
I'm not familiar with metabolic modeling, but it's not because it has been published before that it is correct.
Indeed, you may want to check on CrossValidated StackExchange about the feasibility of performing quantile normalisation on FPKM data. It does not feel right to me. A better transformation would be to Z-scores, via zFPKM package in R.
Maybe check quantro, a framework to test if your dataset fulfills the assumptions of quantile normlization.