[BioC] Remove batch effects from RNA-seq data using edgeR and sva/ComBat

[Gordon K Smyth]

Hi David,

I think you're right that this is too good to be true.

First, cpm values are grossly non-normal.  If you wish to use a method 
designed for microarrays, you need to convert to the log-scale:

   data = cpm(dge, log=TRUE, prior.count=2)

See Section 2.10 of the edgeR User's Guide.

Second, as Evan has said in a separate email, your sample sizes are too 
small for ComBat to be reliable.  You have only 8 samples total, and only 
two samples in each batch.  ComBat isn't designed to work in this range.

So adjusting for batch in the glm model is the way to go.

Best wishes
Gordon

PS. We didn't get your attached file, presumably because it is over the 
size limit for the mailing list.

> Date: Tue, 4 Jun 2013 15:40:20 -0400
> From: "David O'Brien" <dobrie10 at jhmi.edu>
> To: bioconductor at r-project.org
> Subject: [BioC] Remove batch effects from RNA-seq data using edgeR and
> 	sva/ComBat
>
> I'm trying to analyze an RNA-seq experiment where the PCA plot shows better
> clustering by the day the experiment was done rather than treatment type.
> Using edgeR to determine differentially expressed genes resulted in less
> than 5 genes with an FDR under 5%. Creating a GLM model to remove batch
> effects for day of experiment as stated in the edgeR manual resulted in 42
> genes with an FDR less than 5%. An improvement, but still not good. So I
> tried using ComBat and the result was 986 genes with an FDR under 5%.
> Looking at the GO enrichment, the differentially expressed genes seem to
> make sense, but since ComBat was developed for microarrays, I'm concerned
> that there may be some caveats with this approach that I'm missing. Looking
> at the top genes below, the log2 fold change is really low and generally
> this just seems too good to be true. So my question is: Are there any
> reasons why using ComBat with RNA-seq data is not legit? And if so, can you
> see any problems with the approach below?
>
>          mean_control mean_treatment      logFC         pval         padj
> Gene5727     51.224797      45.371919 -0.1750427 3.474361e-08 0.0003224554
> Gene3059      8.998311       5.740828 -0.6483954 1.056473e-07 0.0003268376
> Gene11899    35.044302      39.027842  0.1553238 7.398559e-08 0.0003268376
> Gene11724     2.556712       3.684178  0.5270535 1.959058e-07 0.0003636404
> Gene12218    30.852989      23.702209 -0.3803888 1.908726e-07 0.0003636404
>
> Gene4952     26.122068      30.466346  0.2219474 3.346424e-07 0.0005176360
>
>
> My code is below. I've attached a file, dge.Rdata, that contains the
> counts info that is output from readDGE, so you can have the initial
> counts info.
>
>
> require(edgeR)
> require(sva)
> source('code/annotate_edgeR.R')
> files = data.frame(files=c('counts.control0', 'counts.control1',
> 'counts.control2', 'counts.control3', 'counts.treatment0',
> 'counts.treatment1', 'counts.treatment2', 'counts.treatment3'),
>                   group=c('control', 'control', 'control', 'control',
> 'treatment', 'treatment', 'treatment', 'treatment'),
>                   day=rep(0:3,2)
> )
> labels <- paste0(files$group, files$day)
> dge <- readDGE(files=files, path='data/HTSeq/', labels=labels)
> rownames(dge$counts) <- paste0('Gene', 1:nrow(dge$counts)) #Change gene
> names to anonymize data
> ################################
> # save(dge, file='objs/dge.Rdata')
> #     SEE ATTACHED FILE     #
> ###############################
>
> ##  filter out the no_feature etc. rows
> dge <- dge[1:(nrow(dge)-5), ]
> ##  This mitochondrial rRNA gene takes up a massive portion of my libraries
> dge <- dge[!rownames(dge)%in%'Gene13515', ]
> ##  filter out lowly expressed genes
> keep <- rowSums(cpm(dge) > 1) >= 3 ## gene has at least 3 columns where cpm
> is > 1
> dge <- dge[keep, ]
> ##  Recompute library sizes
> dge$samples$lib.size <- colSums(dge$counts)
> ##  Normalize for lib size
> dge <- calcNormFactors(dge)
>
> ## ComBat
> mod <- model.matrix(~as.factor(group), data=dge$sample)
> mod0 <- model.matrix(~1, data=dge$sample)
> batch <- dge$sample$day
>
> combat <- ComBat(dat=cpm(dge), batch=batch, mod=mod)
> pval_combat = f.pvalue(combat, mod, mod0)
> padj_combat = p.adjust(pval_combat, method="BH")
> mean_control <- rowMeans(combat[, 1:4])
> mean_treatment <- rowMeans(combat[, 5:8])
> logFC <- log2(mean_treatment/mean_control)
>
> res <- data.frame(mean_control, mean_treatment, logFC, pval=pval_combat,
> padj=padj_combat)
> res <- res[order(res$padj), ]
>
> R version 3.0.1 (2013-05-16)
> Platform: x86_64-pc-linux-gnu (64-bit)
>
> locale:
> [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
> LC_TIME=en_US.UTF-8
> [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8
> LC_MESSAGES=en_US.UTF-8
> [7] LC_PAPER=C                 LC_NAME=C
> LC_ADDRESS=C
> [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8
> LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] biomaRt_2.16.0 sva_3.6.0      mgcv_1.7-23    corpcor_1.6.6
> edgeR_3.2.3    limma_3.16.4
>
> loaded via a namespace (and not attached):
> [1] grid_3.0.1      lattice_0.20-15 Matrix_1.0-12   nlme_3.1-109
> RCurl_1.95-4.1  tools_3.0.1
> [7] XML_3.96-1.1
>
> ------------------------------
>

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