wenbostar / metax Goto Github PK
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metaX: a flexible and comprehensive software for processing omics data.
After reading metaX article, metaX manual and R documentation for this package, I have two major questions on methods for QC-based batch correction implemented in metaX R package.
Hi, here's my question about export the result of doQCRLSC().
I used to normalize my intensity table using metaX::normalize() and export this result by getPeaksTable(). Here's my code:
para <- reSetPeaksData(para)
para <- metaX::normalize(para,method="pqn",valueID="value")
tab <- getPeaksTable(para, valueID = 'value')
write.table(tab, file = 'test.txt', quote = FALSE, sep = '\t')
However, when I tried to export the result of doQCRLSC(), I found the result of doQCRLSC() a table of batch and group information, without a corrected intensity table which is what I actually need. Also, running doQCRLSC() didn't cause any changes in para@peaksData. Therefore, can you show me the appropriate way to export QC-RLSC corrected data after performing doQCRLSC()?
Thanks!
样本校正相关的下面3行代码
mpa <- peaksData %>% dplyr::group_by(ID) %>% dplyr::summarise(mpa=median(value,na.rm = TRUE))
peaksData <- dplyr::mutate(peaksData,valuePredict=valuePredict/mpa)
peaksData$valueNorm <- peaksData$value/peaksData$valuePredict
我理解mpa是raw.value的中位值,valuePredict/mpa的比值是校正系数。valueNorm应该是raw.value * 校正系数,而不是除以校正系数,与第三行代码的**不一致。
按照第三行代码计算得到的QC.valueNorm与 QC.valuePredict的值是不一样的。
请指正。
Hi,
Thank for your package! It seems very useful!
But why do I get this message from Bioconductor (Warning: Package 'metaX' is deprecated and will be removed from Bioconductor) and your package seems to be deprecated since Bioconductor 3.4 (https://bioconductor.org/about/removed-packages/)
I'm a little worried about spending significant time learning the package if it is being deprecated...
thanks!
sebastien
Hi again! @wenbostar
Another question for you : How to export results of intermediate processes from metaX ?
For my case, which is quite complicated due to a cross-species analysis demand, I need to export the ion intensity after missingvalue filtering and imputation, before normalization. Unfortunately, I couldn't find a function to do this in R document of metaX.
Thanks!
hello,
when i run examples of doQCRLSC, i don't know why it occurs error like this:
?doQCRLSC
rm(list = ls())
para <- new("metaXpara")
pfile <- system.file("extdata/MTBLS79.txt",package = "metaX")
sfile <- system.file("extdata/MTBLS79_sampleList.txt",package = "metaX")
rawPeaks(para) <- read.delim(pfile,check.names = FALSE)[1:20,]
sampleListFile(para) <- sfile
para <- reSetPeaksData(para)
Reset data!
Convert <=0 to NA:121
para <- missingValueImpute(para)
missingValueImpute: value
Wed Nov 15 18:48:31 2023 Missing value imputation for 'value'
Missing value in total: 121
Missing value in QC sample: 18
Missing value in non-QC sample: 103
Wed Nov 15 18:48:31 2023 The ratio of missing value: 3.5174%
<=0: 0
Missing value in total after missing value inputation: 0
<=0 value in total after missing value inputation: 0
res <- doQCRLSC(para,cpu=1)
Using cpu: 1
The number of NA value in peaksData before QC-RLSC: 0
Error in is.null(para@sampleList) || is.na(para@sampleList) :
'length = 688' in coercion to 'logical(1)'
i can't understand how to solve it.
Thanks for your help.
Bo,
Thank you so much for your software and continual support to keep it running. I am trying to run the software on your test data (and my own data) and cannot seem to get past the following error message. Might you be able to help?
plot PCA for value 'valueNorm'
scaling in PCA: none
centering in PCA: FALSE
change in estimate: 0
Bhattacharyya distance matrix:
[,1] [,2] [,3] [,4]
[1,] 0.00000000 0.86499144 0.67873091 0.06768484
[2,] 0.86499144 0.00000000 0.03060409 0.37393193
[3,] 0.67873091 0.03060409 0.00000000 0.25861844
[4,] 0.06768484 0.37393193 0.25861844 0.00000000
Mean of the Bhattacharyya distance:
[1] 0.3790936
Error in peakStat(para = para, plsdaPara = plsdaPara, doROC = doROC, pcaLabel = pcaLabel, :
object 'pcaColor' not found
The website is not available. Thus, the "example" files are not accessible. http://metax.genomics.cn/
Thanks!
Hi,I have two questions about data-processing 0f metabolomic data,as follows:
1.Can we compare intensities of different featrues in the same sample?Whether different features intensity are comparable?If not, can we solve this problem by Z-score or other normalization methods for intensity normalization?
2.Whether the features in positive and negative ion modes can be combined together in data processing(from missing value imputation and normlization to downstream analysis)?If not, can we correct features in two different ion modes to the same level by any method ?
Thanks a lot!
Dear Wen,
First, thanks for your sharing of metaX.
When run install packages,
install.packages(c("Nozzle.R1","ggplot2","parallel","reshape2","plyr","BBmisc","mixOmics","missForest","doParallel","DiscriMiner","xcms","ape","scatterplot3d","pheatmap","bootstrap","boot","caret","dplyr","stringr","RColorBrewer","DiffCorr","RCurl","lattice","data.table","igraph","tidyr","scales","VennDiagram","pROC","readr","e1071","randomForest","coop","fpc","ROCR"))
The following warning presented:
"Warning messages:
1: packages ‘parallel’, ‘xcms’ are not available (for R version 3.4.3)
2: package ‘parallel’ is a base package, and should not be updated "
when running:
biocLite(c("pcaMethods","vsn","pls","faahKO","mzR","preprocessCore","impute","CAMERA","ropls","sva","SSPA"))
The following warning presented:
Warning messages:
1: running command '"C:/PROGRA1/R/R-341.3/bin/x64/R" CMD INSTALL -l "C:\Users\TANG Zhi\Documents\R\win-library\3.4" C:\Users\TANGZH~1\AppData\Local\Temp\RtmpqEeOw3/downloaded_packages/faahKO_1.18.0.tar.gz' had status 1
2: In install.packages(pkgs = doing, lib = lib, ...) :
installation of package ‘faahKO’ had non-zero exit status
How to resolve these problems? Thanks for your help in advance.
I try to install metaX package in the following two ways (windows platform), but they all failed.
https://support.bioconductor.org/. For details see also
https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.
Error in inDL(x, as.logical(local), as.logical(now), ...) :
无法载入共享目标对象‘C:/Program Files/R/R-3.4.3/library/KernSmooth/libs/x64/KernSmooth.dll’::
`已达到了DLL数目的上限...
ERROR: lazy loading failed for package 'metaX'
Please help me fix this problem.
Hi @wenbostar , I have a questions on reference for pqn implemented in metaX R package.What is the reference used in pqn? QC samples or Samples?
Dear Bo Wen,
First of all, let me congratulate you for this awesome work.
I’m trying to use your R package, metaX but, unfortunately I was unable to install it neither in Windows nor in OSX.
It gives me the following errors in windows (10, professional ed, using RStudio):
Installing package into ‘C:/Users/Eduardo Chicano/Documents/R/win-library/3.4’
(as ‘lib’ is unspecified)
These are the messages from console when I try to install in OSX (10.13 macOS High Sierra), also with R 3.4.2 and RStudio:
install.packages("metaX_1.4.16.tar.gz",repos=NULL,type="source")
Warning in strptime(xx, f <- "%Y-%m-%d %H:%M:%OS", tz = tz) :
unknown timezone 'default/Europe/Madrid'
What am I doing wrong? I’m currently using R 3.4.2, and I have installed all dependencies required for your package.
I’m planning to use this package with Maldi Imaging lipidomics data but I have the data in metaboanalyst format (.csv). As far as I have read, this package loads this csv without problems, isn’t it?
Thanks in advance!
Best regards
Hi,
Is there a away to modify your plotting functions (e.g. plotTreeMap, plotPCA) such that they output to the current active graphing device instead of directly outputting to a file?
It is not proper R standard to directly output to a graph, because it doesn't allow the user to do any modifications to the graph.
thank you,
Dear Bo. You did an excellent job.
However, I met with something error in using metaX. I am trying to use the Normalization module in metaX package to fix my data. Although it did work with your example data, it returns "Error in checkForRemoteErrors(val) 👍 one node produced an error: error in runFit!" with my data. So I wonder if it is the problem of my data? But I did exactly like your example data.
By the way, I work in OS 10.14.1 and Rstudio Version 1.1.456. Thanks
Information about traceback():
8: stop("one node produced an error: ", firstmsg, domain = NA)
7: checkForRemoteErrors(val)
6: staticClusterApply(cl, fun, length(x), argfun)
5: clusterApply(cl = cl, x = splitList(X, nchunks), fun = lapply,
FUN = fun, ...)
4: do.call(c, clusterApply(cl = cl, x = splitList(X, nchunks), fun = lapply,
FUN = fun, ...), quote = TRUE)
3: parLapply(cl, unique(qcData$ID_batch), .runFit1, qcData = qcData,
maxOrder = maxOrder)
2: doQCRLSC(para, cpu = 1)
1: doQCRLSC(para, cpu = 1)
metaX can no longer be installed on R 3.2 or greater. It looks like the package dependency mixOmics is only supported on R versions 3.5, however metaX is only supported on R version 3.2, therefore due to the higher mixOmics package dependency metaX cannot be installed on any versions of R.
Is there any plan to resolve this in the near future?
need some instructions
install.packages("~/Downloads/metaX_1.4.16.tar.gz",repos=NULL)
Installing package into ‘/mnt/code/R/x86_64-pc-linux-gnu-library/3.5’
(as ‘lib’ is unspecified)
* installing *source* package ‘metaX’ ...
** R
** inst
** byte-compile and prepare package for lazy loading
in method for ‘plotLoading’ with signature ‘object="mvr"’: no definition for class “mvr”
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (metaX)
Dear author,
Thank you very much for developing this convenient metabolomics analysis tool. I have a small question that I would like to ask you. Upon reviewing the source code, I noticed that in the runPLSDA function, the metabolomics data is scaled (default: uv). However, it seems that the scaling is applied to all the grouped data together. For example, if I have two paired groups (A vs B; A vs C), the scaling is performed on all the data from groups A, B, and C combined. Might it be more appropriate to scale the data based on each individual paired group?
Thank you for your attention and I look forward to your insights on this matter.
Best regards,
Tonny
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