Entering edit mode
Hi,
I have a time course RNASeq experiment and I'd like to detect DEU
between early and late stages. I am trying to use 'Age' as a
continuous
variable in my design, but I'm getting an error which I suspect is
related to this e.g:
> summary(sample.data.ipsc[,1:6])
Sample CellType Subtype Donor Age
Length:28 iPSC:28 iPSC :28 4555 :28 Min. :
2.000
Class :character HBR : 0 D4721 : 0 1st Qu.:
4.000
Mode :character L2 : 0 D4749 : 0 Median :
8.000
L3 : 0 D6002 : 0 Mean :
7.821
L4 : 0 D6005 : 0 3rd
Qu.:10.500
L5 : 0 D6008 : 0 Max.
:14.000
(Other): 0 (Other): 0
Passage
42:3
48:7
49:5
52:6
56:7
> dxd = DEXSeqDataSetFromHTSeq(countFiles,
sampleData=sample.data.ipsc,
design= ~ sample + exon + Age:exon,
flattenedfile=flattenedFile)
> BPPARAM = MulticoreParam(workers=24)
> dxd = estimateSizeFactors(dxd)
> dxd = estimateDispersions(dxd, BPPARAM=BPPARAM)
> dxd = testForDEU(dxd, BPPARAM=BPPARAM)
> dxd = estimateExonFoldChanges(dxd, fitExpToVar="Age",
BPPARAM=BPPARAM)
Error: 8346 errors; first error:
Error in FUN(1:3[[1L]], ...): Non-factor in model frame
For more information, use bplasterror(). To resume calculation, re-
call
the function and set the argument 'BPRESUME' to TRUE or wrap the
previous call in bpresume().
First traceback:
31: estimateExonFoldChanges(dxd, fitExpToVar = "Age", BPPARAM =
BPPARAM)
30: bplapply(testablegenes, geteffects, BPPARAM = BPPARAM)
29: bplapply(testablegenes, geteffects, BPPARAM = BPPARAM)
28: mclapply(X = X, FUN = FUN, ..., mc.set.seed = BPPARAM$setSeed,
mc.silent = !BPPARAM$verbose, mc.cores =
bpworkers(BPPARAM),
mc.cleanup = if (BPPARAM$cleanup) BPPARAM$cleanupSignal
else
FALSE)
27: lapply(seq_len(cores), inner.do)
26: lapply(seq_len(cores), inner.do)
25: FUN(1:24[[1L]], ...)
24: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
23: parallel:::sendMaster(...)
22: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
21: tryCatch(expr, error = function(e)
Can DEXSeq accept a model with a continuous variable? Does it make
sense
to do so? (I do the same thing with DESeq2 to detected DE and it works
fine). Is this error due to that? Note that all the other steps seem
to
run fine and I can get results (though I don't have many significant
hits - not sure if this is related or not). If not what is best
practice? Just split the data set into 'early' and 'late' samples and
run that as a factor?
Alex Gutteridge
> sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets
methods
[8] base
other attached packages:
[1] stringr_0.6.2
[2] DEXSeq_1.10.8
[3] BiocParallel_0.6.1
[4] reshape_0.8.5
[5] ggplot2_1.0.0
[6] matrixStats_0.10.0
[7] statmod_1.4.20
[8] pcaMethods_1.54.0
[9] Homo.sapiens_1.1.2
[10] TxDb.Hsapiens.UCSC.hg19.knownGene_2.14.0
[11] org.Hs.eg.db_2.14.0
[12] GO.db_2.14.0
[13] RSQLite_0.11.4
[14] DBI_0.2-7
[15] OrganismDbi_1.6.0
[16] GenomicFeatures_1.16.2
[17] AnnotationDbi_1.26.0
[18] Biobase_2.24.0
[19] limma_3.20.8
[20] DESeq2_1.4.5
[21] RcppArmadillo_0.4.320.0
[22] Rcpp_0.11.2
[23] GenomicRanges_1.16.3
[24] GenomeInfoDb_1.0.2
[25] IRanges_1.22.9
[26] BiocGenerics_0.10.0
[27] gplots_2.14.1
[28] RColorBrewer_1.0-5
loaded via a namespace (and not attached):
[1] annotate_1.42.1 BatchJobs_1.3 BBmisc_1.7
[4] biomaRt_2.20.0 Biostrings_2.32.1 bitops_1.0-6
[7] brew_1.0-6 BSgenome_1.32.0 caTools_1.17
[10] checkmate_1.2 codetools_0.2-8 colorspace_1.2-4
[13] digest_0.6.4 fail_1.2 foreach_1.4.2
[16] gdata_2.13.3 genefilter_1.46.1
geneplotter_1.42.0
[19] GenomicAlignments_1.0.3 graph_1.42.0 grid_3.1.0
[22] gtable_0.1.2 gtools_3.4.1 hwriter_1.3
[25] iterators_1.0.7 KernSmooth_2.23-12 labeling_0.2
[28] lattice_0.20-29 locfit_1.5-9.1 MASS_7.3-33
[31] munsell_0.4.2 plyr_1.8.1 proto_0.3-10
[34] RBGL_1.40.0 RCurl_1.95-4.1 reshape2_1.4
[37] R.methodsS3_1.6.1 Rsamtools_1.16.1
rtracklayer_1.24.2
[40] scales_0.2.4 sendmailR_1.1-2 splines_3.1.0
[43] stats4_3.1.0 survival_2.37-7 tools_3.1.0
[46] XML_3.98-1.1 xtable_1.7-3 XVector_0.4.0
[49] zlibbioc_1.10.0
