Hi Kasper,

thanks for the hard work on the package. I am trying to read epic idats of different file sizes and I think you need to uncomment/delete line 58 in in read.meth.R to get the force=TRUE argument working?

Best,

Martin

Currently the sex argument in preprocessFunnorm is a numeric (with values in 1:2 according to code). Need to change code to use M/F as other functions do; also change man page.

Dear Kasper,

I am working on methylation EPIC array data.
I was using the functions getMeth and getUnmeth to respectively extract 'methylated' and 'unmethylated' signals from a MethylSet object.
However, I think that until the laste release of minfi, when I use this function getMeth(MethylSet) I get this error message :
Error: is(BSseq, "BSseq") is not TRUE

This error does'nt appear when using getUnmeth(MethylSet).

Is there any conflict between functions ? Should I use another function ?

Many thanks for your help !

Best regards,

Nizar Touleimat

Hi Kasper,

Would it be possible to add keepCN argument to preprocessFunnorm. Now by default both beta-values and CN-data is returned. I all most always use only the beta-values. This will reduce the size of the returned object, which is useful when normalizing a large number of samples.

Regards,
Maarten

I have been trying to get estimateCellCounts() to work, and keep getting a similar error with both 450k and EPIC data. I am currently working through the "hansenlab/tutorial.450k," which is giving the same error.

library(FlowSorted.Blood.450k)
cellCounts <- estimateCellCounts(RGSet)
[estimateCellCounts] Combining user data with reference (flow sorted) data.

Error in [[<-.data.frame(*tmp*, i, value = c("IlluminaHumanMethylation450k", :
replacement has 66 rows, data has 6

EDIT: When the code was altered around line 40 -

combinedRGset <- combineArrays(rgSet, referenceRGset, outType = "IlluminaHumanMethylation450k")

to be

combinedRGset <- combine(rgSet, referenceRGset)

the function worked on methyl450k data, but still will not work for EPIC data that has been converted to 450k data via the convertArray() function.

Any ideas?

getSnpBeta should probably not use the manifest at all? I was getting errors

snps=getSnpBeta(RGset)
Error in getRed(object)[snpProbesI.Red$AddressB, , drop = FALSE] :
subscript out of bounds

when applying getSnpBeta on a combined 450k and EPIC RGset. Changing all the references from manifest to object seems to have resolved this (some of the probes in the manifest aren't present in the object)

==== Code I ran follows =====

getSnpBeta <-
function (object)
{
#    .isRGOrStop(object)
    manifest <- getManifest(object)
    snpProbesII <- getProbeInfo(object, type = "SnpII")$Name
    M.II <- getGreen(object)[getProbeInfo(object, type = "SnpII")$AddressA,
        , drop = FALSE]
    U.II <- getRed(object)[getProbeInfo(object, type = "SnpII")$AddressA,
        , drop = FALSE]
    snpProbesI.Green <- getProbeInfo(object, type = "SnpI")[getProbeInfo(object,
        type = "SnpI")$Color == "Grn", ]
    snpProbesI.Red <- getProbeInfo(object, type = "SnpI")[getProbeInfo(object,
        type = "SnpI")$Color == "Red", ]
    M.I.Red <- getRed(object)[snpProbesI.Red$AddressB, , drop = FALSE]
    U.I.Red <- getRed(object)[snpProbesI.Red$AddressA, , drop = FALSE]
    M.I.Green <- getGreen(object)[snpProbesI.Green$AddressB,
        , drop = FALSE]
    U.I.Green <- getGreen(object)[snpProbesI.Green$AddressA,
        , drop = FALSE]
    M <- rbind(M.II, M.I.Red, M.I.Green)
    U <- rbind(U.II, U.I.Red, U.I.Green)
    rownames(M) <- rownames(U) <- c(snpProbesII, snpProbesI.Red$Name,
        snpProbesI.Green$Name)
    beta <- M/(U + M + 100)
    return(beta)
}

Hi Kasper,
I have an rgset (EPIC) that I have created using minfi, I would like to run shinymethyl. However, I can not load it into my devel version. I saw your posts regarding updateObject, but I cant even load the object.
I do not have enough memory on devel version to build a new rgset (300 samples).
Any ideas?
Kathleen

Illumina has decided to remove a small number of probes from the DMAP files for the EPIC array.

They also have a new manifest to correspond to their new DMAP files.

Currently, however, the idats contain fewer probes and minfi does not have a manifest to associate with these data.

require(minfi)
RGset <- read.metharray.exp("/ScanData/201220980007", extended = T)
getManifest(RGset)
##Loading required package: Unknownmanifest
##Error in getManifest(RGset) : 
##  cannot load manifest package Unknownmanifest
##In addition: Warning message:
##In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
##  there is no package called ‘Unknownmanifest’
RGset
##RGChannelSetExtended (storageMode: lockedEnvironment)
##assayData: 1051943 features, 8 samples 
##  element names: Green, GreenSD, NBeads, Red, RedSD 
##An object of class 'AnnotatedDataFrame': none
##Annotation
##  array: Unknown
##  annotation: Unknown

Is there a way to add this new manifest and annotate the RGSet appropriately so it will work with other functions?

Thanks!
Eric

It would be convenient to have a way to easily import data from GEO. This would probably be based on GEOquery.

Given a GEO series ID we could retrieve the series matrix which contains processed beta values and always has a predictable file name and location. (See the "Series Matrix File(s)" link on http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35069, for example).

IDATs are unfortunately not available from GEO. U and M values are usually available but only as supplementary files without predictable file names or formats.

library(minfi)
require(minfiData)
GRset.funnorm <- preprocessFunnorm(RGsetEx)

gives the error:

Loading required package: minfiData
Loading required package: IlluminaHumanMethylation450kmanifest
Loading required package: IlluminaHumanMethylation450kanno.ilmn12.hg19
[preprocessFunnorm] Background and dye bias correction with noob 
Using sample number 2 as reference level...
[preprocessFunnorm] Mapping to genome
[preprocessFunnorm] Quantile extraction
[preprocessFunnorm] Normalization
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function 'getListElement' for signature '"CompressedSplitDataFrameList"'

Here is my sessionInfo

R version 3.1.1 (2014-07-10)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
[1] C

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

other attached packages:
 [1] minfiData_0.6.0                                   
 [2] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
 [3] IlluminaHumanMethylation450kmanifest_0.4.0        
 [4] minfi_1.13.0                                      
 [5] bumphunter_1.4.2                                  
 [6] locfit_1.5-9.1                                    
 [7] iterators_1.0.7                                   
 [8] foreach_1.4.2                                     
 [9] Biostrings_2.32.1                                 
[10] XVector_0.4.0                                     
[11] GenomicRanges_1.16.4                              
[12] GenomeInfoDb_1.0.2                                
[13] IRanges_1.22.10                                   
[14] lattice_0.20-29                                   
[15] Biobase_2.24.0                                    
[16] BiocGenerics_0.13.0                               
[17] BiocInstaller_1.14.3                              

loaded via a namespace (and not attached):
 [1] AnnotationDbi_1.26.1  DBI_0.3.1             MASS_7.3-35          
 [4] R.methodsS3_1.6.1     RColorBrewer_1.0-5    RSQLite_0.11.4       
 [7] Rcpp_0.11.3           S4Vectors_0.5.0       XML_3.98-1.1         
[10] annotate_1.42.1       base64_1.1            beanplot_1.2         
[13] codetools_0.2-8       digest_0.6.4          doRNG_1.6            
[16] genefilter_1.46.1     grid_3.1.1            illuminaio_0.6.1     
[19] limma_3.20.9          matrixStats_0.10.3    mclust_4.4           
[22] multtest_2.20.0       nlme_3.1-117          nor1mix_1.2-0        
[25] pkgmaker_0.22         plyr_1.8.1            preprocessCore_1.26.1
[28] quadprog_1.5-5        registry_0.2          reshape_0.8.5        
[31] rngtools_1.2.4        siggenes_1.38.0       splines_3.1.1        
[34] stats4_3.1.1          stringr_0.6.2         survival_2.37-7      
[37] tools_3.1.1           xtable_1.7-4          zlibbioc_1.10.0      

There is support for gzipped files in illuminaio. But currently, read.metharray() generates IDAT file name from basename. This should be fixed to both generate with and without .gz and then search for files to decide which one to use.

I get an error regarding the slot names for the RGset when implementing my code to determine cell counts from methylEPIC data. Could anyone help me out here?

##Read in data
targets<-read.metharray.sheet(base = "~/Documents/IDATS", pattern = "csv$", ignore.case = TRUE,
recursive = TRUE, verbose = TRUE )

path<-"~/Documents/IDATS"
RGset<-read.metharray.exp(base = path, targets = targets, extended = FALSE,recursive = FALSE, verbose = FALSE, force = FALSE)
pData<- pData(RGset)
pData$Sample_Name <- as.character(pData$Sample_Name)
pData(RGset) <- pData

require(FlowSorted.Blood.450k)

cellCounts <- estimateCellCounts(RGset)

Error in colData(referenceRGset) :
no slot of name "colData" for this object of class "RGChannelSet"
Error in nrow(x@elementMetadata) :
no slot of name "elementMetadata" for this object of class "RGChannelSet"

Hey there,

I have recently started to write my first R package, mostly convenience functions around the minfi package to streamline my 450K idat processing. When I tried to call the minfi::read.450k function from within my package I got this error:

Error in validMsg(NULL, assayDataValidMembers(assayData(object), c("Red", :
could not find function "assayDataValidMembers"

I think this is because the function is not imported into minfi. When I used library(Biobase) it worked fine.

All the best

Christoph

Two bugs related to the preprocessMethod slot in MethylSet and friends:
(1) The 450k manifest is hardcoded into the code.
(2) preprocessNoob does not set preprocessMethod.

Idea: How about preprocessMethod recording the preprocessCall made, and keeps a list of calls?

Hi Kasper,

I recently needed to estimate the cell mixture proportion in a series of blood cell DNA methylation data and came across this fantastic function in minfi package. However, I encountered a error when calling this function using code

  estPro <- estimateCellCounts(rgSet, cellTypes = c("Neu", "CD8T", "CD4T", "Gran"))

I think the problem is that in pickCompProbes function, the probeList contains NA's for Neu. A dirty but quick fix that I can come up with is to add after line 112 of the scripte estimateCellCounts.R the code below

  trainingProbes <- trainingProbes[!is.na(trainingProbes)]

I hope this might help and thanks for developing this package.

Regards,
Yi

In the function controlStripPlot() (line 45 of qc.R)

if (!is.null(sampNames)) colnames(ctlWide) <- sampNames

ctlWide is a numeric and can not be assigned colnames(). I assume this was changed from names() which works fine. I have tried the most recent (not devel) version of minfi and the error still persists.

The error returns:

Error in colnames<-(*tmp*, value = c("687L", "686L", : attempt to set 'colnames' on an object with less than two dimensions

Further down in the same function (line 50, qc.R) it also tries to do this for the green channel.

I also have had issues with preprocessFunnorm() but I have yet to trace the issue. It may be related since it was cropping up in the same analysis.

Cheers

Hi,

We used the latest devel version of minfi and used estimateCellCounts() to estimate cell proportion with EPIC data. Everything runs smoothly. But when it comes to large data (like more than 2000 samples), running the function can easily hit the memory limit. We are using cluster which is offering 256G memory.

We could run estimateCellCounts() plate by plate, but will this introduce potential biases compared with running it with the whole data?

Thanks!

Hi! I am using the mapToGenome function to get genome annotation for my Illumina HumanMethylation EPIC data. Here is my code:
myDataGenome<-mapToGenome(myDataSIBoth)
and here is the returned message:
Loading required package: IlluminaHumanMethylationEPICanno.ilmn10.hg19 Error in getAnnotationObject(object) : cannot load annotation package IlluminaHumanMethylationEPICanno.ilmn10.hg19
It seems to me that the " IlluminaHumanMethylationEPICanno.ilmn10.hg19" package has been updated to “ IlluminaHumanMethylationEPICanno.ilmn10b2.hg19”. So I guess maybe the released version of minfi did not include this update?
Thanks.

Hi,

I'm following Kasper's request from here, to raise the issue that applying preprocessNoob(), then preprocessQuantile() produces an error.

 if ((is(object, "MethylSet") || is(object, "GenomicMethylSet")) && 
        preprocessMethod(object)["rg.norm"] != "Raw (no normalization or bg correction)") 
        warning("preprocessQuantile has only been tested with 'preprocessRaw'")

The above code block is the culprit, and it's due to the state of preprocessMethod(object)["rg.norm"]. It's expecting rg.norm to be available, when the result is mu.norm.

Cheers

Dear developers,

It seems that the options of "offset" and "type" do not work when applying getBeta() directly to an RGChannelSet. No warning or error message is given, the betas are simply returned as calculated with no offset.
I am not sure if this is intentional or not - I could not directly determine this from the documentation. I just wanted to bring it to your attention.

Thank you,

Rory

Hi Kasper,

Just thought we'd give you a heads up. We ran a few EPIC arrays from different batches and turns out some had old dmap files. As a result of this, when reading the G.idats and R.idats objects with readIDAT from "illuminaio" in the read.metharray() function, the G.idats[[]]$Quants and R.idats[[]]$Quants matrices do not have the same ordering. The subsequent do.call(cbind) operations then create GreenMean(SD|NBeads) and RedMean(SD|NBeads) objects that can have sample intensities out of order in some of the columns becauase the rowname probe addresses of the first sample will be forced onto the rest of the cbind samples even though they have a matrix with a different row ordering because the older dmap file--many of samples will then have shuffled probe intensity values.

Genome Studio correctly handles this problem so we completely missed it in the QC in that software. We only found it later when our collaborators ran minfi and it appeared as if all the samples with with old dmap files had high background and extremely poor detP's--this was only because all of the negative control probes were shuffled and not assigned the proper address.

Let me know if this doesn't make sense. This problem should be rare since i believe illumina has fixed the dmap problem but just in case it might be safe to just order those G.idats[[]]$Quants and R.idats[[]]$Quants by rownames before cbind'ing.

best,
will

metharray<-read.metharray(basename,extended = FALSE, verbose = FALSE, force = FALSE)
detP<-detectionP(metharray)
rownames<-rownames(detP)
filterpronames<-rownames[which(detP<=0.05)]
metharray<-subsetByLoci(metharray, includeLoci = filterpronames)
mset <- preprocessIllumina(metharray)

#Here I can not go on ~ ~ I want to dorp some probes which failed detection p-value testing.

Error in getGreen(rgSet)[getProbeInfo(rgSet, type = "II")$AddressA, ] : ...

Dear all,

i couldn't load "minfi", i installed it using:

source("https://bioconductor.org/biocLite.R")
biocLite("minfi")

Apparently no errors:

BioC_mirror: https://bioconductor.org
Using Bioconductor 3.5 (BiocInstaller 1.26.0), R 3.4.0 (2017-04-21).
Installing package(s) ‘minfi’
essai de l'URL 'https://bioconductor.org/packages/3.5/bioc/bin/macosx/el-capitan/contrib/3.4/minfi_1.22.1.tgz'
Content type 'application/x-gzip' length 825920 bytes (806 KB)

downloaded 806 KB

The downloaded binary packages are in
/var/folders/mf/_xyr2hm93rx845kbc_8hcwwc0000gn/T//RtmpNwrMaY/downloaded_packages

Them when i tried to load it:

library(minfi)

I'm getting:

Le chargement a nécessité le package : bumphunter
Error: package or namespace load failed for ‘bumphunter’ in dyn.load(file, DLLpath = DLLpath, ...):
impossible de charger l'objet partagé '/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so':
dlopen(/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so, 6): Library not loaded: /usr/local/opt/openssl/lib/libssl.1.0.0.dylib
Referenced from: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so
Reason: image not found
Erreur : le package ‘bumphunter’ ne peut être chargé

So i tried reinstalling "bumphunter"

remove.packages("bumphunter")

source("https://bioconductor.org/biocLite.R")

biocLite("bumphunter")

library(bumphunter)

Erreur : package or namespace load failed for ‘bumphunter’ in dyn.load(file, DLLpath = DLLpath, ...):
impossible de charger l'objet partagé '/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so':
dlopen(/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so, 6): Library not loaded: /usr/local/opt/openssl/lib/libssl.1.0.0.dylib
Referenced from: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so
Reason: image not found

So i tried reinstalling "rtracklayer"

remove.packages("rtracklayer")

source("https://bioconductor.org/biocLite.R")
biocLite("rtracklayer")

library(rtracklayer)

Erreur : package or namespace load failed for ‘rtracklayer’ in dyn.load(file, DLLpath = DLLpath, ...):
impossible de charger l'objet partagé '/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so':
dlopen(/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so, 6): Library not loaded: /usr/local/opt/openssl/lib/libssl.1.0.0.dylib
Referenced from: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rtracklayer/libs/rtracklayer.so
Reason: image not found

So i checked too for maybe old package using biocValid():

[1] TRUE

So i have no clue how is going on, if some on could help me please.

Thanks.

Jp

R session info sessionInfo()

R version 3.4.0 (2017-04-21)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.4

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8

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

other attached packages:
[1] devtools_1.12.0 tidyr_0.6.2
[3] locfit_1.5-9.1 iterators_1.0.8
[5] foreach_1.4.3 Biostrings_2.44.0
[7] XVector_0.16.0 SummarizedExperiment_1.6.1
[9] DelayedArray_0.2.1 matrixStats_0.52.2
[11] Biobase_2.36.2 GenomicRanges_1.28.1
[13] GenomeInfoDb_1.12.0 IRanges_2.10.0
[15] S4Vectors_0.14.0 BiocGenerics_0.22.0
[17] BiocInstaller_1.26.0

loaded via a namespace (and not attached):
[1] doRNG_1.6.6 GenomeInfoDbData_0.99.0 Rsamtools_1.28.0
[4] RSQLite_1.1-2 lattice_0.20-35 digest_0.6.12
[7] Matrix_1.2-10 XML_3.98-1.7 biomaRt_2.32.0
[10] zlibbioc_1.22.0 xtable_1.8-2 BiocParallel_1.10.1
[13] tibble_1.3.0 pkgmaker_0.22 withr_1.0.2
[16] magrittr_1.5 memoise_1.1.0 tools_3.4.0
[19] registry_0.3 stringr_1.2.0 rngtools_1.2.4
[22] AnnotationDbi_1.38.0 compiler_3.4.0 grid_3.4.0

[25] RCurl_1.95-4.8 rstudioapi_0.6 bitops_1.0-6

[28] codetools_0.2-15 DBI_0.6-1 GenomicAlignments_1.12.0
[31] stringi_1.1.5 Rcpp_0.12.10

In your compartments paper you filtered out SNPs according their minor allele frequencies. It would be great to have such option in minfi, next to openSea argument.

Do you plan to add a read.850K function in minfi ? I would be happy to propose one.

Using minfi version 1.22.1 in R 3.4.0 I'm encountering an error when loading a GEO (example) data set.
It seems that there is a problem in constructing the pData dataFrame, please can you have a look at it?
Thank in advance,
Hindrik

gmset=readGEORawFile("GSE29290_Matrix_Signal.txt",Uname="Signal_A",Mname="Signal_B",sep="\t")
Read 485577 rows and 45 (of 67) columns from 0.156 GB file in 00:00:03
Error in .local(assays, ...) :
unused argument (pData = <S4 object of class "DataFrame">)

In matrixStats (>= 0.50.0), which is now on CRAN, we introduce support for subsetted calculations that are optimized both for speed and memory usage. There are now new arguments idxs, rows and cols for most functions which allow you to very easily make use of this.

For your package, I believe the following functions could gain from this:

$minfi$.getSex
[1] matrixStats::colMedians(CN[xIndex, ], na.rm = TRUE)
[2] matrixStats::colMedians(CN[yIndex, ], na.rm = TRUE)

$minfi$bgIntensitySwan
[1] matrixStats::colMedians(getGreen(rgSet)[getControlAddress(rgSet, controlType = "NEGATIVE"), ])
[2] redMed <- matrixStats::colMedians(getRed(rgSet)[getControlAddress(rgSet, controlType = "NEGATIVE"), ])

by updating to something like:

$minfi$.getSex
[1] matrixStats::colMedians(CN, rows = xIndex, na.rm = TRUE)
[2] matrixStats::colMedians(CN, rows = yIndex, na.rm = TRUE)

$minfi$bgIntensitySwan
[1] matrixStats::colMedians(getGreen(rgSet), rows = getControlAddress(rgSet, controlType = "NEGATIVE"))
[2] redMed <- matrixStats::colMedians(getRed(rgSet), rows = getControlAddress(rgSet, controlType = "NEGATIVE"))

and adding matrixStats (>= 0.50.0) to your DESCRIPTION file.

I tried to run minfi under R3.3.3, and got error when using read.metharray.exp. Seemed that the RGset was generated, but both annotation and manifest were listed as "unknown", not as humanEPICmanifest and ilm10b2.hg19 even though I did install IlluminaHumanMethylationEPICmanifest and IlluminaHumanMethylationEPICanno.ilm10b2.hg19. therefore, when perform preprocessRaw, the error said "there is no package called 'Unknownmanifest'. One month ago, when I ran under R3.3.2, everything was perfectly fine. I also tried ChAMP which uses minfi to load data; same issue. What could be the problem? thanks.

Throw a suitable error message.

From Maarten van Iterson [email protected]

In a few analyses with >500 samples we noticed that often the number of
PCs should be larger than the default of 2 for functional normalization.
Now we extract some code from minfi, not exported, using the triple colon
operator, to create such plots. It would be nice if there was a function to
plot the variance explained of the first few pc of the control matrix. For
example, something along the lines of:

controlMatrix <- .buildControlMatrix450k(.extractFromRGSet450k(RGset))
pc <- prcomp(controlMatrix)
##set nmax e.g. to 10 or so
nmax <- ifelse(nmax > nrow(controlMatrix), nrow(controlMatrix), nmax)
barplot(summary(pc)$importance[2,1:nmax], ylab="Proportion of Variance")

and optionally return the pc-object e.g. to correlate with known covariates
for further inspection.

I am working with a combined dataset of 450k and EPIC data. My rgset is type RGChannelSetExtended and I used the combineArrays function.
Can I use this new virutal array that has rgset type RGChannelSetExtended with the estimatecellcounts function in minfi? Or, will it only accept the RGChannelSet type?

Thx,
Shantel

For RGChannelSetExtended.

From: Maarten van Iterson [email protected]

Add function argument na.rm=FALSE/TRUE to detectionP which should be
passed to colMedians and colMads such that detectionP can handle NAs in
the Red and Green intensity matrices of an rgSet. If na.rm=TRUE some
detection P-values will be NA, if these were NA on the probe-level, but
this is we want. For example, we use this for some probe-level filtering
steps e.g. on the number of
beads minimally required.

From Stewart Morris [email protected]

I'm getting an error from read.metharray.exp() and wonder if you can check this out please.

The script I'm running is as follows:

library(minfi)
targets <-read.metharray.sheet('idats', pattern="02032017_RW.csv$")
RGset <- read.metharray.exp(targets=targets, extended=TRUE, verbose=TRUE)

The error is:

...
[read.metharray] Reading201004900067_R05C02_Red.idat
[read.metharray] Reading201004900067_R06C02_Red.idat
[read.metharray] Read idat files in 57.392seconds
[read.metharray] Creating data matrices ...
done in42.3seconds
[read.metharray] Instantiating final object ...
Error: cannot subset by character when names are NULL

From Tajuddin, Salman [email protected]

I was going through your 450K data analysis with minif, located here http://kasperdanielhansen.github.io/genbioconductor/html/minfi.html and encountered an error when merging the GEO phenotype data with the methylation data. Not sure what went wrong here.

The error message reads like this:

> pData(rgSet) <- pD
Error in (function (classes, fdef, mtable)  :
  unable to find an inherited method for function ‘pData<-’ for signature ‘"RGChannelSet", "data.frame"’

sessionInfo() is below for your reference

Thanks for your help.

Salman

Hi,

I'm running the preprocessFunnorm in my 450K Methylation analysis, but I receive this error message:

Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function 'getListElement' for signature '"CompressedSplitDataFrameList"'

Please, help!

Thank you!

I would like to use the controlStripPlot function for different control types.
There is only one feature present for one of the controls, which leads to the following error:

Error in UseMethod("melt", data) : 
  no applicable method for 'melt' applied to an object of class "c('double', 'numeric')"

because a numeric vector is extracted instead of a matrix in line:
ctlWide <- log2(r[ctrlAddress, ])
Replacing it by:
ctlWide <- log2(r[ctrlAddress, , drop = FALSE]) solves the issue.
Would it be possible to include this fix in a new version? Or is there a more optimal code?
Thanks in advance!

Hi,
I was trying to use minfi to estimate cell counts for illumina EPIC data. I upgraded both R and bioconductor, but couldn't execute estimateCellCounts because package "FlowSorted.Blood.EPIC" couldn't be loaded. Am I doing something wrong?

biocLite("FlowSorted.Blood.EPIC")
BioC_mirror: https://bioconductor.org
Using Bioconductor 3.4 (BiocInstaller 1.24.0), R 3.3.2 (2016-10-31).
Installing package(s) ‘FlowSorted.Blood.EPIC’
Warning message:
package ‘FlowSorted.Blood.EPIC’ is not available (for R version 3.3.2)
sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X El Capitan 10.11.4

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
[1] FlowSorted.Blood.450k_1.12.0 gap_1.1-16
[3] som_0.3-5.1 pvclust_2.0-0
[5] reshape_0.8.6 data.table_1.10.2
[7] foreign_0.8-67 preprocessCore_1.36.0
[9] gtools_3.5.0 RnBeads_1.6.1
[11] plyr_1.8.4 methylumi_2.20.0
[13] minfi_1.20.2 bumphunter_1.14.0
[15] locfit_1.5-9.1 iterators_1.0.8
[17] foreach_1.4.3 Biostrings_2.42.1
[19] XVector_0.14.0 SummarizedExperiment_1.4.0
[21] FDb.InfiniumMethylation.hg19_2.2.0 org.Hs.eg.db_3.4.0
[23] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicFeatures_1.26.2
[25] AnnotationDbi_1.36.2 reshape2_1.4.2
[27] scales_0.4.1 Biobase_2.34.0
[29] illuminaio_0.16.0 matrixStats_0.51.0
[31] limma_3.30.9 gridExtra_2.2.1
[33] gplots_3.0.1 ggplot2_2.2.1
[35] fields_8.10 maps_3.1.1
[37] spam_1.4-0 ff_2.2-13
[39] bit_1.1-12 cluster_2.0.5
[41] RColorBrewer_1.1-2 MASS_7.3-45
[43] GenomicRanges_1.26.2 GenomeInfoDb_1.10.2
[45] IRanges_2.8.1 S4Vectors_0.12.1
[47] BiocGenerics_0.20.0 BiocInstaller_1.24.0

loaded via a namespace (and not attached):
[1] nlme_3.1-130 bitops_1.0-6 httr_1.2.1
[4] tools_3.3.2 doRNG_1.6 nor1mix_1.2-2
[7] R6_2.2.0 KernSmooth_2.23-15 DBI_0.5-1
[10] lazyeval_0.2.0 colorspace_1.3-2 base64_2.0
[13] pkgmaker_0.22 rtracklayer_1.34.1 caTools_1.17.1
[16] genefilter_1.56.0 quadprog_1.5-5 stringr_1.1.0
[19] digest_0.6.12 Rsamtools_1.26.1 siggenes_1.48.0
[22] GEOquery_2.40.0 RSQLite_1.1-2 mclust_5.2.2
[25] BiocParallel_1.8.1 RCurl_1.95-4.8 magrittr_1.5
[28] Matrix_1.2-8 Rcpp_0.12.9 munsell_0.4.3
[31] stringi_1.1.2 zlibbioc_1.20.0 gdata_2.17.0
[34] lattice_0.20-34 splines_3.3.2 multtest_2.30.0
[37] annotate_1.52.1 beanplot_1.2 rngtools_1.2.4
[40] codetools_0.2-15 biomaRt_2.30.0 XML_3.98-1.5
[43] gtable_0.2.0 openssl_0.9.6 assertthat_0.1
[46] xtable_1.8-2 survival_2.40-1 tibble_1.2
[49] GenomicAlignments_1.10.0 registry_0.3 memoise_1.0.0

When I try to convert an EPIC RGSet to a 450k RGSet using minfi 1.20.0 I get the following error:

rgset450k <- convertArray(RGsetEpic, "IlluminaHumanMethylation450k")
[convertArray] Casting as IlluminaHumanMethylation450k
Error: all(probes1$ProbeSeqA == probes2$ProbeSeqB) is not TRUE

It looks like the problem is in lines 303 and 304 of https://github.com/kasperdanielhansen/minfi/blob/master/R/combineArrays.R

The code reads:
stopifnot(all(probes1$ProbeSeqA == probes2$ProbeSeqB))
stopifnot(all(probes1$ProbeSeqB == probes2$ProbeSeqA))

So currently it's:
A == B
B == A

If you change it to:
A == A
B == B

The code runs as expected.
Is this a typo?

Thanks!
Eric

I'm trying to use minfi's readGEORawFile function to read in methylation data from the GEO series GSE42752, but I keep getting the following error:

gmset=readGEORawFile("GSE42752_methylated_unmethylated_signals.txt",Uname="Signal_A",Mname="Signal_B",sep="\t")

Error in FUN(X[[i]], ...) : 
  assay colnames() must be NULL or equal colData rownames()

I tried the example code in refrence manual and get the same error

gmset=readGEORawFile("GSE29290_Matrix_Signal.txt",Uname="Signal_A",Mname="Signal_B",sep="\t")

Error in FUN(X[[i]], ...) : 
  assay colnames() must be NULL or equal colData rownames()

I'm not very familiar with R and hope that someone could help me with this problem, and thanks for developing the package.

Hi,

I encountered the error when using preprocessQuantile function on RGChannelSet.
mSetSq <- preprocessQuantile(rgSet)
[preprocessQuantile] Mapping to genome.
[preprocessQuantile] Fixing outliers.
Error in base::which(x, arr.ind, useNames, ...) :
argument to 'which' is not logical

Thanks in advance for help.

SessionInfo() of mine is in the following.

R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04 LTS

locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C

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

other attached packages:
[1] stringr_1.1.0 DMRcate_1.10.2
[3] DMRcatedata_1.10.1 DSS_2.14.0
[5] bsseq_1.10.0 Gviz_1.18.1
[7] minfiData_0.20.0 matrixStats_0.51.0
[9] missMethyl_1.8.0 RColorBrewer_1.1-2
[11] IlluminaHumanMethylation450kmanifest_0.4.0 IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
[13] minfi_1.20.2 bumphunter_1.14.0
[15] locfit_1.5-9.1 iterators_1.0.8
[17] foreach_1.4.3 Biostrings_2.42.1
[19] XVector_0.14.0 SummarizedExperiment_1.4.0
[21] GenomicRanges_1.26.2 GenomeInfoDb_1.10.2
[23] IRanges_2.8.1 S4Vectors_0.12.1
[25] Biobase_2.34.0 BiocGenerics_0.20.0
[27] limma_3.30.7

loaded via a namespace (and not attached):
[1] colorspace_1.3-2 siggenes_1.48.0
[3] mclust_5.2.1 biovizBase_1.22.0
[5] htmlTable_1.8 base64enc_0.1-3
[7] dichromat_2.0-0 base64_2.0
[9] interactiveDisplayBase_1.12.0 AnnotationDbi_1.36.0
[11] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0 codetools_0.2-15
[13] R.methodsS3_1.7.1 methylumi_2.20.0
[15] knitr_1.15.1 Formula_1.2-1
[17] Rsamtools_1.26.1 annotate_1.52.1
[19] cluster_2.0.5 GO.db_3.4.0
[21] R.oo_1.21.0 shiny_0.14.2
[23] httr_1.2.1 backports_1.0.4
[25] assertthat_0.1 Matrix_1.2-7.1
[27] lazyeval_0.2.0 acepack_1.4.1
[29] htmltools_0.3.5 tools_3.3.2
[31] gtable_0.2.0 doRNG_1.6
[33] Rcpp_0.12.8 multtest_2.30.0
[35] preprocessCore_1.36.0 nlme_3.1-128
[37] rtracklayer_1.34.1 mime_0.5
[39] ensembldb_1.6.2 rngtools_1.2.4
[41] gtools_3.5.0 statmod_1.4.27
[43] XML_3.98-1.5 beanplot_1.2
[45] org.Hs.eg.db_3.4.0 AnnotationHub_2.6.4
[47] zlibbioc_1.20.0 MASS_7.3-45
[49] scales_0.4.1 BSgenome_1.42.0
[51] VariantAnnotation_1.20.2 BiocInstaller_1.24.0
[53] GEOquery_2.40.0 yaml_2.1.14
[55] memoise_1.0.0 gridExtra_2.2.1
[57] ggplot2_2.2.1 pkgmaker_0.22
[59] biomaRt_2.30.0 rpart_4.1-10
[61] reshape_0.8.6 latticeExtra_0.6-28
[63] stringi_1.1.2 RSQLite_1.1-2
[65] genefilter_1.56.0 permute_0.9-4
[67] checkmate_1.8.2 GenomicFeatures_1.26.2
[69] BiocParallel_1.8.1 bitops_1.0-6
[71] nor1mix_1.2-2 lattice_0.20-34
[73] ruv_0.9.6 GenomicAlignments_1.10.0
[75] plyr_1.8.4 magrittr_1.5
[77] R6_2.2.0 Hmisc_4.0-2
[79] DBI_0.5-1 foreign_0.8-67
[81] survival_2.40-1 RCurl_1.95-4.8
[83] nnet_7.3-12 tibble_1.2
[85] data.table_1.10.0 digest_0.6.11
[87] xtable_1.8-2 httpuv_1.3.3
[89] illuminaio_0.16.0 R.utils_2.5.0
[91] openssl_0.9.6 munsell_0.4.3
[93] registry_0.3 BiasedUrn_1.07
[95] quadprog_1.5-5

e.g.:

m = preprocessQuantile(rgSet)
getBeta(m, betaThreshold=0.001)

fails.

Hello,

I seems current estimateCellCounts function does not support EPIC array. Is it possible that future version will add this feature or the algorithm itself may not work for EPIC array at all (e.g. missing signature CpGs in EPIC)?

Than you.

From: Maarten van Iterson [email protected]

Are there any plans to support reading idats-files in parallel using the
BiocParallel functionality? For example, read.450k.exp could easily be
parallelized and for the reduce step 'combine' from Biobase can be used.

Change current @preprocessMethod to be a sequence of calls. Each new call should just add to the vector/list. Use match.call(). Also need to modify the print method, and fix the minfiData package.

Hi,

I found some issues understanding the output of minfi's estimateCellCounts(). The thing is that I obtain negative and >1 values. I don't know how to interpret this numbers.

Thanks

Alberto

I tried to get a list of the control probe types of the example manifest using getControlTypes(), but I get the following output:

> getControlTypes(IlluminaHumanMethylation450kmanifest)
Error in NSBS(i, x, exact = exact, upperBoundIsStrict = !allow.append) : subscript contains invalid names

This was after loading the minfi and minfiData packages. Using the following works fine:

> unique(getProbeInfo(IlluminaHumanMethylation450kmanifest, type="Control")$Type)
 [1] "STAINING"                "EXTENSION"
 [3] "HYBRIDIZATION"           "TARGET REMOVAL"
 [5] "BISULFITE CONVERSION I"  "BISULFITE CONVERSION II"
 [7] "SPECIFICITY I"           "SPECIFICITY II"
 [9] "NON-POLYMORPHIC"         "NEGATIVE"
[11] "RESTORATION"             "NORM_A"
[13] "NORM_G"                  "NORM_C"
[15] "NORM_T"

My sessionInfo():

> sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 15.10

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=nl_NL.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=nl_NL.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=nl_NL.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=nl_NL.UTF-8 LC_IDENTIFICATION=C

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

other attached packages:
 [1] minfiData_0.12.0
 [2] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
 [3] IlluminaHumanMethylation450kmanifest_0.4.0
 [4] minfi_1.16.0
 [5] bumphunter_1.10.0
 [6] locfit_1.5-9.1
 [7] iterators_1.0.8
 [8] foreach_1.4.3
 [9] Biostrings_2.38.2
[10] XVector_0.10.0
[11] SummarizedExperiment_1.0.1
[12] GenomicRanges_1.22.2
[13] GenomeInfoDb_1.6.1
[14] IRanges_2.4.6
[15] S4Vectors_0.8.5
[16] lattice_0.20-33
[17] Biobase_2.30.0
[18] BiocGenerics_0.16.1

loaded via a namespace (and not attached):
 [1] mclust_5.1              rgl_0.95.1247           base64_1.1
 [4] Rcpp_0.12.2             corpcor_1.6.8           Rsamtools_1.22.0
 [7] digest_0.6.8            plyr_1.8.3              futile.options_1.0.0
[10] ellipse_0.3-8           RSQLite_1.0.0           ggplot2_2.0.0
[13] zlibbioc_1.16.0         GenomicFeatures_1.22.7  annotate_1.48.0
[16] preprocessCore_1.32.0   splines_3.2.3           BiocParallel_1.4.3
[19] stringr_1.0.0           igraph_1.0.1            RCurl_1.95-4.7
[22] biomaRt_2.26.1          munsell_0.4.2           rtracklayer_1.30.1
[25] multtest_2.26.0         pkgmaker_0.22           GEOquery_2.36.0
[28] quadprog_1.5-5          codetools_0.2-14        matrixStats_0.50.1
[31] XML_3.98-1.3            reshape_0.8.5           GenomicAlignments_1.6.1
[34] MASS_7.3-45             bitops_1.0-6            grid_3.2.3
[37] nlme_3.1-122            xtable_1.8-0            gtable_0.1.2
[40] registry_0.3            DBI_0.3.1               magrittr_1.5
[43] scales_0.3.0            stringi_1.0-1           genefilter_1.52.0
[46] doRNG_1.6               limma_3.26.3            futile.logger_1.4.1
[49] nor1mix_1.2-1           lambda.r_1.1.7          RColorBrewer_1.1-2
[52] mixOmics_5.2.0          siggenes_1.44.0         tools_3.2.3
[55] illuminaio_0.12.0       rngtools_1.2.4          survival_2.38-3
[58] colorspace_1.2-6        AnnotationDbi_1.32.2    beanplot_1.2

Hi there, I am currently trying to run the estimateCellCounts function across several different experiments and am hitting an error (shown below). I am using the developer version of both R and Bioconductor (session info at the bottom).

> if(require(FlowSorted.Blood.450k)) {
+     wh.WBC <- which(FlowSorted.Blood.450k$CellType == "WBC")
+     wh.PBMC <- which(FlowSorted.Blood.450k$CellType == "PBMC")
+     RGset <- FlowSorted.Blood.450k[, c(wh.WBC, wh.PBMC)]
+     ## The following line is purely to work around an issue with repeated
+     ## sampleNames and Biobase::combine()
+     sampleNames(RGset) <- paste(RGset$CellType,
+                                 c(seq(along = wh.WBC), seq(along = wh.PBMC)), sep = "_")
+     counts <- estimateCellCounts(RGset, meanPlot = FALSE)
+     round(counts, 2)
+ }
[estimateCellCounts] Combining user data with reference (flow sorted) data.

Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘colData<-’ for signature ‘"RGChannelSet", "data.frame"’

I was receiving the above error when trying to look at different data sets of my own (both 450K and EPIC data), so thought I'd run through the example provided with the function to check - I end up with the same error.

I've tried working through the source code line by line and have come across a few issues:

• I was getting the above error at this line combinedRGset <- combineArrays(rgSet, referenceRGset, outType = "IlluminaHumanMethylation450k"), running require(minfiData) && require(minfiDataEPIC) fixed this - I see minfiData is a dependency but it wasn't loaded up front for whatever reason.
• the error remained and appears to be located to this line colData(combinedRGset) <- newpd, which I believe is intended to add an extra column to the phenotype data of the rgset. I replaced it with this colData(combinedRGset)$studyIndex <- newpd$studyIndex and was able to move past the error.

I've got further ahead but am now finding issues with missing functions such as pickCompProbes, which I found here but am unable to see how this is sourced via the minfi package itself. I've added these functions manually but am hitting a series of other issues now, so I'm beginning to wonder if I'm missing something obvious.

Hopefully this is somewhat coherent and helpful, as always I'm more than happy to help/test where I can and really appreciate the work you guys put into this great package.

R session information:

> sessionInfo()
R Under development (unstable) (2017-03-22 r72384)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux stretch/sid

Matrix products: default
BLAS: /home/miles/svn/R/r-devel/build/lib/libRblas.so
LAPACK: /home/miles/svn/R/r-devel/build/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] quadprog_1.5-5                                      genefilter_1.57.0                                  
 [3] minfiDataEPIC_1.1.1                                 IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
 [5] IlluminaHumanMethylationEPICmanifest_0.3.0          minfiData_0.21.1                                   
 [7] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0  IlluminaHumanMethylation450kmanifest_0.4.0         
 [9] FlowSorted.Blood.450k_1.13.1                        minfi_1.21.5                                       
[11] bumphunter_1.15.0                                   locfit_1.5-9.1                                     
[13] iterators_1.0.8                                     foreach_1.4.3                                      
[15] Biostrings_2.43.6                                   XVector_0.15.2                                     
[17] SummarizedExperiment_1.5.7                          DelayedArray_0.1.7                                 
[19] matrixStats_0.51.0                                  Biobase_2.35.1                                     
[21] GenomicRanges_1.27.23                               GenomeInfoDb_1.11.9                                
[23] IRanges_2.9.19                                      S4Vectors_0.13.15                                  
[25] BiocGenerics_0.21.3                                 BiocInstaller_1.25.3                               

loaded via a namespace (and not attached):
 [1] mclust_5.2.3              base64_2.0                Rcpp_0.12.10              lattice_0.20-35           Rsamtools_1.27.13        
 [6] digest_0.6.12             R6_2.2.0                  plyr_1.8.4                RSQLite_1.1-2             httr_1.2.1               
[11] zlibbioc_1.21.0           GenomicFeatures_1.27.10   data.table_1.10.4         annotate_1.53.1           Matrix_1.2-9             
[16] preprocessCore_1.37.0     splines_3.4.0             BiocParallel_1.9.5        stringr_1.2.0             RCurl_1.95-4.8           
[21] biomaRt_2.31.4            compiler_3.4.0            rtracklayer_1.35.9        multtest_2.31.0           pkgmaker_0.22            
[26] openssl_0.9.6             GEOquery_2.41.0           GenomeInfoDbData_0.99.0   codetools_0.2-15          XML_3.98-1.5             
[31] reshape_0.8.6             GenomicAlignments_1.11.12 MASS_7.3-47               bitops_1.0-6              grid_3.4.0               
[36] nlme_3.1-131              xtable_1.8-2              registry_0.3              DBI_0.6                   magrittr_1.5             
[41] stringi_1.1.3             doRNG_1.6                 limma_3.31.19             nor1mix_1.2-2             RColorBrewer_1.1-2       
[46] siggenes_1.49.0           tools_3.4.0               illuminaio_0.17.0         rngtools_1.2.4            survival_2.41-2          
[51] yaml_2.1.14               AnnotationDbi_1.37.4      beanplot_1.2              memoise_1.0.0  

• Miles

Annotation packages can be loaded but not attached

loadNamespace("IlluminaHumanMethylation450kanno.ilmn12.hg19")

but then getLocations() fails

> library(minfi)
> minfi::getLocations(IlluminaHumanMethylation450kanno.ilmn12.hg19::IlluminaHumanMethylation450kanno.ilmn12.hg19)
Error in as.environment(pointer$envir) : 
  no item called "package:IlluminaHumanMethylation450kanno.ilmn12.hg19" on the search list

The context is from a submitted package, Bioconductor/Contributions#121 from @YinanZheng