[BioC] smoothing function in bumphunter in minfi
Ruzicka, William B.,M.D.
wruzicka at mclean.harvard.edu
Wed Jun 11 16:50:42 CEST 2014
Hi Kasper -
Thanks for your help.
I've upgraded to R-3.1.0 but bumphunter is still giving the same "unused aregument" error:
> sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] doParallel_1.0.8 IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
[3] IlluminaHumanMethylation450kmanifest_0.4.0 minfi_1.10.1
[5] bumphunter_1.4.2 locfit_1.5-9.1
[7] iterators_1.0.7 foreach_1.4.2
[9] Biostrings_2.32.0 XVector_0.4.0
[11] GenomicRanges_1.16.3 GenomeInfoDb_1.0.2
[13] IRanges_1.22.8 lattice_0.20-29
[15] Biobase_2.24.0 BiocGenerics_0.10.0
loaded via a namespace (and not attached):
[1] annotate_1.42.0 AnnotationDbi_1.26.0 base64_1.1 beanplot_1.1
[5] codetools_0.2-8 compiler_3.1.0 DBI_0.2-7 digest_0.6.4
[9] doRNG_1.6 genefilter_1.46.1 grid_3.1.0 illuminaio_0.6.0
[13] limma_3.20.4 MASS_7.3-33 matrixStats_0.10.0 mclust_4.3
[17] multtest_2.20.0 nlme_3.1-117 nor1mix_1.1-4 pkgmaker_0.22
[21] plyr_1.8.1 preprocessCore_1.26.1 R.methodsS3_1.6.1 RColorBrewer_1.0-5
[25] Rcpp_0.11.2 registry_0.2 reshape_0.8.5 rngtools_1.2.4
[29] RSQLite_0.11.4 siggenes_1.38.0 splines_3.1.0 stats4_3.1.0
[33] stringr_0.6.2 survival_2.37-7 tools_3.1.0 XML_3.98-1.1
[37] xtable_1.7-3 zlibbioc_1.10.0
> traceback()
10: stop(simpleError(msg, call = expr))
9: e$fun(obj, substitute(ex), parent.frame(), e$data)
8: list(args = iter(IndexesChunks)(.doRNG.stream = list(c(407L,
-631507724L, 2080869221L, 1065599202L, 1311698683L, -323805696L,
-1040906751L), c(407L, 1945611946L, -608207003L, 1842430237L,
-518903442L, 1824620966L, -1548636643L), c(407L, 237898474L,
1062912735L, -1334786133L, 1059436525L, 793963592L, 1493198629L
), c(407L, 1527452477L, -1461135650L, 1252537885L, 1975038170L,
-892552789L, -512077693L))), argnames = c("idx", ".doRNG.stream"
), evalenv = <environment>, specified = character(0), combineInfo = list(
fun = function (a, ...)
c(a, list(...)), in.order = TRUE, has.init = TRUE, init = list(),
final = NULL, multi.combine = TRUE, max.combine = 100), errorHandling = "stop",
packages = c("bumphunter", "doRNG"), export = NULL, noexport = NULL,
options = list(), verbose = FALSE) %dopar% {
{
rngtools::RNGseed(.doRNG.stream)
}
{
sm <- smoothFunction(y = y[idx, ], x = x[idx], cluster = cluster[idx],
weights = weights[idx, ], verbose = verbose, ...)
c(sm, list(idx = idx))
}
}
7: do.call("%dopar%", list(obj, ex), envir = parent.frame())
6: foreach(idx = iter(IndexesChunks), .packages = "bumphunter") %dorng%
{
sm <- smoothFunction(y = y[idx, ], x = x[idx], cluster = cluster[idx],
weights = weights[idx, ], verbose = verbose, ...)
c(sm, list(idx = idx))
}
5: smoother(y = rawBeta, x = pos, cluster = cluster, weights = weights,
smoothFunction = smoothFunction, verbose = subverbose, ...)
4: bumphunterEngine(getMethSignal(object, type), design = design,
chr = as.factor(seqnames(object)), pos = start(object), cluster = cluster,
coef = coef, cutoff = cutoff, cutoffQ = cutoffQ, maxGap = maxGap,
smooth = smooth, smoothFunction = smoothFunction, useWeights = useWeights,
B = B, verbose = verbose, ...)
3: .local(object, ...)
2: bumphunter(GRQ, design = designMatrix, pickCutoff = TRUE, B = 100,
maxGap = 500, smooth = TRUE, smoothFunction = locfitByCluster)
1: bumphunter(GRQ, design = designMatrix, pickCutoff = TRUE, B = 100,
maxGap = 500, smooth = TRUE, smoothFunction = locfitByCluster)
R version 3.1.0 (2014-04-10) -- "Spring Dance"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
[Workspace loaded from ~/.RData]
> require(minfi)
Loading required package: minfi
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics?
The following objects are masked from ‘package:parallel?
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
The following object is masked from ‘package:stats?
xtabs
The following objects are masked from ‘package:base?
anyDuplicated, append, as.data.frame, as.vector, cbind, colnames, do.call, duplicated, eval,
evalq, Filter, Find, get, intersect, is.unsorted, lapply, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int, rownames,
sapply, setdiff, sort, table, tapply, union, unique, unlist
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
see 'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: lattice
Loading required package: GenomicRanges
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biostrings
Loading required package: XVector
Loading required package: bumphunter
Loading required package: foreach
foreach: simple, scalable parallel programming from Revolution Analytics
Use Revolution R for scalability, fault tolerance and more.
http://www.revolutionanalytics.com
Loading required package: iterators
Loading required package: locfit
locfit 1.5-9.1 2013-03-22
> setwd("G:/Illumina/Brad/Minfi")
> baseDir <- "G:/Illumina/Brad/Minfi/Scan Output"
> targets <- read.450k.sheet(baseDir)
[read.450k.sheet] Found the following CSV files:
[1] "G:/Illumina/Brad/Minfi/Scan Output/SampleSheetSZ32.csv"
> RGSet <- read.450k.exp(base = baseDir, targets = targets)
> pd <- pData(RGSet)
> pd[,1:4]
Sample_Name Sample_Well Sample_Plate Sample_Group
8942300007_R02C01 CON1_CA32 B01 Plate1 CON_CA32
8942300010_R06C01 CON2_CA32 B01 Plate1 CON_CA32
8942297078_R06C01 CON3_CA32 F02 Plate1 CON_CA32
8942300010_R01C01 CON4_CA32 E02 Plate1 CON_CA32
8942300010_R05C01 CON5_CA32 A01 Plate1 CON_CA32
8942297143_R06C02 CON6_CA32 H02 Plate1 CON_CA32
8942297143_R06C01 CON7_CA32 B02 Plate1 CON_CA32
8942297143_R01C02 CON8_CA32 C02 Plate1 CON_CA32
8942300007_R01C01 SZ1_CA32 A01 Plate1 SZ_CA32
8942300007_R03C01 SZ2_CA32 C01 Plate1 SZ_CA32
8942297078_R03C02 SZ3_CA32 A01 Plate1 SZ_CA32
8942300010_R02C01 SZ4_CA32 F02 Plate1 SZ_CA32
8942300010_R02C02 SZ5_CA32 D01 Plate1 SZ_CA32
8942297078_R01C01 SZ6_CA32 A02 Plate1 SZ_CA32
8942297143_R04C01 SZ7_CA32 H01 Plate1 SZ_CA32
8942297078_R06C02 SZ8_CA32 D01 Plate1 SZ_CA32
>
> gRatioSet.quantile <- preprocessQuantile(RGSet, fixOutliers = TRUE, removeBadSamples = TRUE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = FALSE, sex = NULL)
[preprocessQuantile] Mapping to genome.
Loading required package: IlluminaHumanMethylation450kmanifest
Loading required package: IlluminaHumanMethylation450kanno.ilmn12.hg19
[preprocessQuantile] Fixing outliers.
[preprocessQuantile] Quantile normalizing.
>
> GADBroad <- read.csv("GAD1LessBroad.csv")
> GRSGADBroad <- as.vector(GADBroad)
> Test <- GADBroad[1:1856,1]
> MSetGADBroad <- as.vector(GADBroad[,1])
> GRQ <- gRatioSet.quantile[Test]
> diagnosis <- pData(gRatioSet.quantile)$diagnosis
> designMatrix <- model.matrix(~ diagnosis)
> library(doParallel)
> registerDoParallel(cores = 4)
> dmrs <- bumphunter(GRQ, design = designMatrix, pickCutoff = TRUE, B=100, maxGap=500, smooth=TRUE, smoothFunction=locfitByCluster)
[bumphunterEngine] Parallelizing using 4 workers/cores (backend: doParallelSNOW, version: 1.0.8).
[bumphunterEngine] Computing coefficients.
[bumphunterEngine] Smoothing coefficients.
Error in { : task 1 failed - "unused argument (cutoffQ = 0.99)"
- Brad
________________________________
From: kasperdanielhansen at gmail.com [kasperdanielhansen at gmail.com] on behalf of Kasper Daniel Hansen [khansen at jhsph.edu]
Sent: Tuesday, June 10, 2014 9:48 PM
To: Brad Ruzicka [guest]
Cc: bioconductor at r-project.org; Ruzicka, William B.,M.D.
Subject: Re: [BioC] smoothing function in bumphunter in minfi
I have been prompted on this off-list. Turns out I was reading the OP a bit too fast ... he is on R 3.0.3 which we no longer support (nor can I make changes to the relevant Bioconductor packages). So the solution is to upgrade to R-3.1.
Hopefully that will fix the problem, otherwise get in touch. Whatever I have done would not have affected R 3.0.3.
Best,
Kasper
On Wed, May 14, 2014 at 11:31 AM, Kasper Daniel Hansen <khansen at jhsph.edu<mailto:khansen at jhsph.edu>> wrote:
This is a weirdly introduced error which we have fixed in the devel branch. I was supposed to back port it into release, but I forgot.
Kasper
On Wed, May 14, 2014 at 11:16 AM, Brad Ruzicka [guest] <guest at bioconductor.org<mailto:guest at bioconductor.org>> wrote:
Hi there-
I've been using bumphunter within minfi to analyze my HM450 dataset with good results, but I'm unable to use the smoothing function within bumphunter. When "smooth = TRUE" it gives the error:
"Error in { : task 1 failed - "unused argument (cutoffQ = 0.99)"", even though I have not included cutoffQ in the script.
The script below works fine with smooth = FALSE, but when set to true gives the above error:
setwd("G:/Illumina/Brad/Minfi")
baseDir <- "G:/Illumina/Brad/Minfi/Scan Output"
targets <- read.450k.sheet(baseDir)
RGSet <- read.450k.exp(base = baseDir, targets = targets)
pd <- pData(RGSet)
pd[,1:4]
gRatioSet.quantile <- preprocessQuantile(RGSet, fixOutliers = TRUE, removeBadSamples = TRUE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = FALSE, sex = NULL)
Age <- pData(gRatioSet.quantile)$age
PMI <- pData(gRatioSet.quantile)$PMI
diagnosis <- pData(gRatioSet.quantile)$diagnosis
gender <- pData(gRatioSet.quantile)$gender
designMatrix <- model.matrix(~ diagnosis + Age + PMI + gender)
library(doParallel)
registerDoParallel(cores = 4)
dmrs <- bumphunter(gRatioSet.quantile, design = designMatrix, maxGap=500, pickCutoff = TRUE, smooth = TRUE, smoothFunction=locfitByCluster, B=1000)
write.csv(dmrs$table, file = "GAD1BroadDMRsSZ3Test6.csv")
Output of script:
[read.450k.sheet] Found the following CSV files:
[1] "G:/Illumina/Brad/Minfi/Scan Output/SampleSheetSZ32.csv"
[preprocessQuantile] Mapping to genome.
[preprocessQuantile] Fixing outliers.
[preprocessQuantile] Quantile normalizing.
[bumphunterEngine] Parallelizing using 4 workers/cores (backend: doParallelSNOW, version: 1.0.8).
[bumphunterEngine] Computing coefficients.
[bumphunterEngine] Smoothing coefficients.
Error in { : task 1 failed - "unused argument (cutoffQ = 0.99)"
Any ideas where my error is?
Thanks,
Brad
-- output of sessionInfo():
> sessionInfo()
R version 3.0.3 (2014-03-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] doParallel_1.0.8
[2] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
[3] IlluminaHumanMethylation450kmanifest_0.4.0
[4] doRNG_1.6
[5] rngtools_1.2.4
[6] pkgmaker_0.20
[7] registry_0.2
[8] minfi_1.8.9
[9] bumphunter_1.2.0
[10] locfit_1.5-9.1
[11] iterators_1.0.7
[12] foreach_1.4.2
[13] Biostrings_2.30.1
[14] GenomicRanges_1.14.4
[15] XVector_0.2.0
[16] IRanges_1.20.7
[17] reshape_0.8.5
[18] lattice_0.20-29
[19] Biobase_2.22.0
[20] BiocGenerics_0.8.0
loaded via a namespace (and not attached):
[1] annotate_1.40.1 AnnotationDbi_1.24.0 base64_1.1
[4] beanplot_1.1 codetools_0.2-8 compiler_3.0.3
[7] DBI_0.2-7 digest_0.6.4 genefilter_1.44.0
[10] grid_3.0.3 illuminaio_0.4.0 itertools_0.1-3
[13] limma_3.18.13 MASS_7.3-33 matrixStats_0.8.14
[16] mclust_4.3 multtest_2.18.0 nlme_3.1-117
[19] nor1mix_1.1-4 plyr_1.8.1 preprocessCore_1.24.0
[22] R.methodsS3_1.6.1 RColorBrewer_1.0-5 Rcpp_0.11.1
[25] RSQLite_0.11.4 siggenes_1.36.0 splines_3.0.3
[28] stats4_3.0.3 stringr_0.6.2 survival_2.37-7
[31] tools_3.0.3 XML_3.98-1.1 xtable_1.7-3
--
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