I came across this lovely thing: http://www.bios.unc.edu/research/gen..._eQTL/faq.html
"Outliers in expression data are usually harder to deal with. The accepted remedy by the GTEx consortium is the transformation of the measurements for each gene into normally distributed while preserving relative rankings. The target distribution may be the standard normal distribution or the normal distribution the mean and spread of the original measurements. Here is the code for such transformation:"
for( sl in 1:length(gene) ) {
mat = gene[[sl]];
mat = t(apply(mat, 1, rank, ties.method = "average"));
mat = qnorm(mat / (ncol(gene)+1));
gene[[sl]] = mat;
}
rm(sl, mat);
I used my normalized DESeq count data as input, then used the program to transform each gene to a normal distribution of expression. Comparing before and after transformation for a few genes, they certainly look normal.
The program claims to have been used successfully to identify eQTLs in RNAseq data. Whether or not my using this approach for eQTLs turns out to be biologically relevant, informative, or correct is yet to be determined. Has anyone tried other transformations of RNAseq for eQTL analysis?
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I would agree with kopi-o. The normalisation isn't based on the total counts, it's based on the distribution of counts across all genes. If you want a slightly more accurate comparison to size factors (but fairly easy to calculate), try splitting the count data into quantiles, or look at (say) the 75th percentile count instead of the total count.
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I have what is probably a naive question about DESeq normalization. The manual says that by dividing the count by the size factor, one makes samples comparable. Does this include comparable for, say, eQTL analysis? I randomly pulled some genes from counts(cds,normalized=TRUE) and plotted their distributions, and they are not all normalliy distributed. Some look normal-ish (bottom row), but I still don't know if I'd consider them normal; others are very clearly following other distributions (top row). If I want to identify eQTLs from my RNAseq data (as I also have genotype data on those individuals), then all the gene read counts need to be transformed to follow a normal distribution before I can test for eQTLs.
So my question is, is the command counts(cds,normalized=TRUE) designed to transform the raw reads counts into expression levels that follow a normal distribution? If so, why do some of my genes not look normally distributed? If not, how could I transform my countDataSet so that each gene follows a normal distribution? If my dataset consists of expression data from three time points, could that be messing up what would otherwise be a normally distributed gene? Figure of some distributions for genes from counts(cds,normalized=TRUE) are below. Thanks for any suggestions!
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Dear Stephen,
how does the 'pairs' (or LSD::heatpairs) plot look like, or the pairwise MA plots? You could also try the arrayQualityMetrics report. From this, problems such as suggested by kopi-o might become apparent.
Best wishes
Wolfgang.
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One thing that could cause this is extreme overrepresentation of one or a handful of genes, like hemoglobin in whole-blood samples for instance. Or maybe rubisco in plants. Partial/failed rRNA removal? I would look for something that seems to dominate the RNA pool.
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DESeq Normalization Question
I'm sure this issue has come up before, but I couldn't find an appropriate thread or answer either here or on the Bioconductor mailing list.
What feature of the data or the distribution of counts among my samples can cause the sizeFactors to vary much more than the raw counts / library sizes?
More detail: I'm using DESeq to analyze RNA-seq data mapped with STAR, counted with htseq-count. Comparing the "doubleTerm" samples to the "wt" samples, there are many genes that appear downregulated. While these samples were sequenced, on average, to a similar sequencing depth, the normalization factors are much smaller for WT, resulting in much larger normalized counts, resulting in more apparently downregulated genes in doubleTerm vs WT.
Code:> cds <- newCountDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory) > cds <- estimateSizeFactors(cds) > cds <- estimateDispersions(cds) > data.frame(sizefactors=sizeFactors(cds), rawcounts=colSums(counts(cds, normalized=FALSE))) sizefactors rawcounts S01_wt1 0.9016089 23466349 S02_wt2 0.7679168 22428603 S03_wt3 0.7952564 19841959 S04_wt4 0.7839629 18363384 S05_pten8w1 1.0301769 20859853 S06_pten8w2 0.9949514 16809588 S07_pten8w3 0.9425865 16731071 S08_pten22w1 1.0826846 18906329 S09_pten22w2 1.1640354 20164026 S10_pten22w3 1.0111748 17306468 S11_double8w1 0.7949001 17671986 S12_double8w2 1.4509978 23673557 S13_double8w3 1.1703853 22127841 S14_doubleterm2 1.0786455 19063010 S15_doubleterm4 1.1265935 19279814 S16_doubleterm6 1.3059472 22750403
Stephen
Code:> sessionInfo() R version 3.0.0 (2013-04-03) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq_1.12.0 lattice_0.20-15 locfit_1.5-9 Biobase_2.20.0 [5] BiocGenerics_0.6.0 edgeR_3.2.3 limma_3.16.2 BiocInstaller_1.10.1 loaded via a namespace (and not attached): [1] annotate_1.38.0 AnnotationDbi_1.22.3 DBI_0.2-6 DESeq2_1.0.9 [5] genefilter_1.42.0 geneplotter_1.38.0 GenomicRanges_1.12.2 grid_3.0.0 [9] IRanges_1.18.0 RColorBrewer_1.0-5 RSQLite_0.11.3 splines_3.0.0 [13] stats4_3.0.0 survival_2.37-4 tools_3.0.0 XML_3.95-0.2 [17] xtable_1.7-1
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