Hi,

I wish to perform a differential expression analysis comparing one tumour-type category ('Tumour.Type') against an average of all the other categories using DESeq2:

# Analysis Groups
> grps
[1] "Tumour.TypeLUAD" "Tumour.TypeCOAD" "Tumour.TypeUEC"  "Tumour.TypeBRCA"
[5] "Tumour.TypeKIRP" "Tumour.TypeKIRC" "Tumour.TypeLUSC" "Tumour.TypePAAD"

ddsMat <- DESeqDataSetFromMatrix(
  countData=Counts, colData=SampleSheet, design=~0 + Tumour.Type
)
dds <- DESeq(ddsMat)
> identical(sort(grps), resultsNames(dds))
[1] TRUE

For example, to compare 'LUAD' with the seven other groups I've used the contrast:

res <- results(
  dds, contrast = list(grps[1], grps[-1]), listValues = c(1,-1/(length(grps)-1))
 )

Having obtained the log2-foldChanges, I'd like to shrink them for geneset enrichment analyses. I've done this before using lfcShrink() with the "apeglm" method. However, "apeglm" requires the use of 'coef' and I've not been able to find a way of creating the name or finding the number of the coefficient for the above contrast.

Please could anyone help me out with this?

Many thanks, Richard Coulson.

sessionInfo() R version 3.5.1 (2018-07-02) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.4 LTS

Matrix products: default BLAS: /usr/lib/libblas/libblas.so.3.6.0 LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale: [1] LCCTYPE=enGB.UTF-8 LCNUMERIC=C
[3] LCTIME=enGB.UTF-8 LCCOLLATE=enGB.UTF-8
[5] LCMONETARY=enGB.UTF-8 LCMESSAGES=enGB.UTF-8
[7] LCPAPER=enGB.UTF-8 LCNAME=C
[9] LCADDRESS=C LCTELEPHONE=C
[11] LCMEASUREMENT=enGB.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] shinyngs0.0.1 bcbioRNASeq0.2.9
[3] DESeq21.20.0 bcbioBase0.4.1
[5] basejump0.7.2 SummarizedExperiment1.10.1 [7] DelayedArray0.6.6 BiocParallel1.14.2
[9] matrixStats0.54.0 Biobase2.40.0
[11] GenomicRanges1.34.0 GenomeInfoDb1.16.0
[13] IRanges2.16.0 S4Vectors0.20.1
[15] BiocGenerics_0.28.0

loaded via a namespace (and not attached): [1] R.utils2.8.0 tidyselect0.2.5
[3] RSQLite2.1.2 AnnotationDbi1.44.0
[5] htmlwidgets1.3 grid3.5.1
[7] munsell0.5.0 codetools0.2-15
[9] preprocessCore1.44.0 withr2.1.2
[11] colorspace1.3-2 knitr1.22
[13] rstudioapi0.8 assertive.base0.0-7
[15] rdrop20.8.1 lasso21.2-20
[17] tximport1.10.1 bbmle1.0.20
[19] GenomeInfoDbData1.1.0 mnormt1.5-5
[21] bit640.9-7 pheatmap1.0.10
[23] coda0.19-2 Matrix.utils0.9.7
[25] vctrs0.2.0 generics0.0.2
[27] xfun0.6 R62.3.0
[29] apeglm1.2.1 assertive.sets0.0-3
[31] locfit1.5-9.1 AnnotationFilter1.6.0
[33] bitops1.0-6 reshape0.8.7
[35] assertthat0.2.0 promises1.0.1
[37] scales1.0.0 nnet7.3-12
[39] gtable0.2.0 affy1.60.0
[41] ensembldb2.6.1 rlang0.4.1
[43] zeallot0.1.0 genefilter1.62.0
[45] GlobalOptions0.1.0 splines3.5.1
[47] rtracklayer1.42.0 lazyeval0.2.1
[49] acepack1.4.1 broom0.5.2
[51] checkmate1.8.5 BiocManager1.30.4
[53] yaml2.2.0 reshape21.4.3
[55] GenomicFeatures1.34.1 backports1.1.2
[57] httpuv1.4.5 Hmisc4.1-1
[59] tools3.5.1 tcltk3.5.1
[61] psych1.8.12 logging0.9-107
[63] ggplot23.0.0 affyio1.52.0
[65] assertive.strings0.0-3 RColorBrewer1.1-2
[67] ggdendro0.1-20 sessioninfo1.1.0
[69] Rcpp1.0.2 plyr1.8.4
[71] base64enc0.1-3 progress1.2.0
[73] zlibbioc1.26.0 purrr0.2.5
[75] RCurl1.95-4.11 prettyunits1.0.2
[77] rpart4.1-13 pbapply1.4-0
[79] GetoptLong0.1.7 cowplot0.9.3
[81] grr0.9.5 ggrepel0.8.0
[83] cluster2.0.7-1 magrittr1.5
[85] data.table1.11.8 assertive.data0.0-3
[87] circlize0.4.6 ProtGenerics1.14.0
[89] hms0.4.2 mime0.6
[91] xtable1.8-3 XML3.98-1.16
[93] emdbook1.3.10 gridExtra2.3
[95] shape1.4.4 compiler3.5.1
[97] biomaRt2.36.1 tibble2.1.3
[99] crayon1.3.4 R.oo1.22.0
[101] htmltools0.3.6 later0.7.5
[103] Formula1.2-3 tidyr0.8.1
[105] geneplotter1.58.0 DBI1.0.0
[107] assertive.files0.0-2 ComplexHeatmap1.20.0
[109] MASS7.3-51 assertive.numbers0.0-2
[111] Matrix1.2-14 readr1.3.1
[113] cli1.0.1 vsn3.50.0
[115] assertive.types0.0-3 R.methodsS31.7.1
[117] pkgconfig2.0.2 GenomicAlignments1.18.0
[119] numDeriv2016.8-1 foreign0.8-71
[121] plotly4.8.0 annotate1.58.0
[123] XVector0.20.0 stringr1.3.1
[125] digest0.6.18 ConsensusClusterPlus1.46.0
[127] assertive.code0.0-3 Biostrings2.48.0
[129] htmlTable1.12 edgeR3.22.5
[131] curl3.2 shiny1.1.0
[133] Rsamtools1.34.0 rjson0.2.20
[135] nlme3.1-137 jsonlite1.5
[137] DEGreport1.18.1 viridisLite0.3.0
[139] limma3.36.5 pillar1.4.2
[141] lattice0.20-35 Nozzle.R11.1-1
[143] httr1.3.1 survival2.42-6
[145] interactiveDisplayBase1.20.0 glue1.3.0
[147] bit1.1-14 assertive.properties0.0-4
[149] stringi1.2.4 blob1.2.0
[151] AnnotationHub2.14.5 latticeExtra0.6-28
[153] memoise1.1.0 dplyr0.8.3

We don't have a mechanism in lfcShrink for performing shrinkage with apeglm based on a contrast of the coefficients (while apeglm has a contrast argument, the handoff between the two software wasn't trivial so we just work on named coefficients).

I'd recommend to use ashr, which we found works well in our testing, and better than type="normal".