Cannot find function treatDGE() in package edgeR?
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ejliaw • 0
@ejliaw-7382
Last seen 6.3 years ago
United States

Hello,

Based on other threads on this forum, I was planning to use edgeR:::treatDGE() to select differential open chromatin peaks while taking into account fold change.

I installed edgeR using biocLite and library(edgeR), and have been able to use many common functions from edgeR (i.e. DGEList, estimateGLM*Disp, glmFit and glmLRT). However, R cannot find treatDGE. The lengthy results of ls(package:edgeR) and ls(getNamespace("edgeR"), all.names=T) both did not include "treatDGE".

Did I install edgeR incorrectly, was the function deprecated, or is there another explanation?

Thanks, Eric

> sessionInfo()

R version 3.0.2 (2013-09-25)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
[1] C

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] BiocInstaller_1.10.4 edgeR_3.4.2          limma_3.16.7        
 [4] ctc_1.34.0           amap_0.8-14          cba_0.2-14          
 [7] proxy_0.4-14         gplots_2.11.3        MASS_7.3-29         
[10] KernSmooth_2.23-10   caTools_1.14         gdata_2.13.2        
[13] gtools_3.2.1        

loaded via a namespace (and not attached):
[1] bitops_1.0-6 tools_3.0.2 
edger installation • 1.8k views
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@ryan-c-thompson-5618
Last seen 8 months ago
Scripps Research, La Jolla, CA

You are running a quite old version of R (from 2013-09-25, note your sessionInfo output), which means you are getting the correspondingly old version of Bioconductor. You should update to the latest version to get new features.

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Thanks Ryan.

I checked the NEWS file and treatDGE() was added in edgeR 3.8.0, which answers my original question. So I need to update R, then update Bioconductor with biocLite("BiocUpgrade"), then reinstall edgeR (and other packages)?

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@james-w-macdonald-5106
Last seen 4 hours ago
United States

In addition to Ryan's comment, treatDGE() is almost surely not what you want. That function is intended for RNA-Seq analysis of gene expression.

If I assume you are doing something like DNAse H1 digestion of your DNA to find regions of open chromatin, then (to me, at least) an analogous method is ChIP-Seq, where you are pulling down unknown regions of the genome, and then are A.) trying to figure out which regions you have pulled down, and B.) trying to see if you are pulling down different regions in different samples (or different amounts of a particular region in different samples). The only difference to me is that you are 'pulling down' regions of DNA with DNAse H1, rather than IP.

If that assumption holds, then edgeR is not the tool for you. Instead, I would look at ChIP-Seq type tools: http://bioconductor.org/packages/release/BiocViews.html#___ChIPSeq

If you like the software coming out of Gordon Smyth's lab, then you might take a look at csaw.

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Thanks for the suggestions. I'll definitely look into other packages. I was looking for differential peaks and planning use edgeR or equivalent as a robust alternative to simply intersecting lists of peak calls from different cells.

From the documentation, I though edgeR's model could handle counts of reads mapping to arbitrary genomic regions (whether exons or peaks), and I also saw that the DiffBind package for ChIP-seq uses edgeR and DEseq. Would you happen to have a quick example of how edgeR's assumptions don't hold for DNAse-seq?

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The csaw package uses edgeR internally, but it does so in a way that avoids false positives resulting from the fact that you are performing two steps of analysis on the same data: calling peaks and then testing those peaks for differential binding. Read the csaw paper for more information.

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edgeR requires a count matrix as input, where each row contains the counts across all samples for the feature of interest. This is not trivial if you don't know what your features of interest are, and you want to find them from the data. Instead, you need to use de novo procedures to discover and test features for significant differences. This is what the csaw package does, using a window-based approach to avoid the need to specify any genomic regions.
 

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