ChAMP installation issue
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e1271094 • 0
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Last seen 6 days ago
Singapore

I have been facing issue to install "ChAMP" R package after updating R version 4.4.0. How do I resolve it?

Cha ChAMP • 374 views
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Without showing the commands you tried and the full output including error it is impossible to help. Please provide additional information.

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I apologise for any inconvenience. I have successfully installed the ChAMP package. However, I am now encountering a new error:

myLoad <- champ.filter(beta=myImport$beta,autoimpute=FALSE,SampleCutoff=0.1, arraytype="EPICv1") #please specify EPICv1 and EPICv2 [===========================]

[<<<< ChAMP.FILTER START >>>>>]

In New version ChAMP, champ.filter() function has been set to do filtering on the result of champ.import(). You can use champ.import() + champ.filter() to do Data Loading, or set "method" parameter in champ.load() as "ChAMP" to get the same effect.

This function is provided for user need to do filtering on some beta (or M) matrix, which contained most filtering system in champ.load except beadcount. User need to input beta matrix, pd file themselves. If you want to do filterintg on detP matrix and Bead Count, you also need to input a detected P matrix and Bead Count information.

Note that if you want to filter more data matrix, say beta, M, intensity... please make sure they have exactly the same rownames and colnames.

[ Section 1: Check Input Start ] You have inputed beta for Analysis.

pd file provided, checking if it's in accord with Data Matrix... pd file check success.

Parameter filterDetP is TRUE, checking if detP in accord with Data Matrix... !!! Parameter detP is not found, filterDetP is reset FALSE now.

Parameter filterBeads is TRUE, checking if beadcount in accord with Data Matrix... !!! Parameter beadcount is not found, filterBeads is reset FALSE now.

Checking Finished :filterMultiHit,filterSNPs,filterNoCG,filterXY would be done on beta. [ Section 1: Check Input Done ]

[ Section 2: Filtering Start >>

Filtering NoCG Start Only Keep CpGs, removing 2991 probes from the analysis.

Filtering SNPs Start Using general mask options Removing 98993 probes from the analysis.

Filtering MultiHit Start Filtering probes that align to multiple locations as identified in Nordlund et al Removing 3905 probes from the analysis.

Filtering XY Start Warning in data("probe.features.epicv1") : data set 'probe.features.epicv1' not found Error in champ.filter(beta = myImport$beta, autoimpute = FALSE, SampleCutoff = 0.1, : object 'probe.features' not found

data("probe.features.epicv1") Warning message: In data("probe.features.epicv1") : data set 'probe.features.epicv1' not found

data("probe.features.epicv2")

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I discovered that the dataset data("probe.features.epicv1") is unavailable. Instead, the dataset data("probe.features.epic") is accessible. To ensure the code executes correctly, it is necessary to load data("probe.features.epic") prior to running champ.filter()

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Hello. Could you please share how you managed to install ChAMP in R 4.4.0? I also have R 4.4.0 and Bioconductor 3.19 version and ChAMP fails to install in my computer.

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We are still waiting for the goseq to respond to our requests but will take action next week if it is not resolved. ChAMP is currently unavailable because goseq is failing.

In the meantime you can use this work around to grab the previously available versions from Bioconductor 3.18 HOWEVER it is NOT recommended as it will involve package version mismatches and are not guaranteed to work correctly -- esp any feature of ChAMP that relies on goseq. In your R session:

options(
    repos = c(
        getOption("repos"),
        Bioc3.18="https://bioconductor.org/packages/3.18/bioc"
    )
)
BiocManager::install("ChAMP")
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Thanks a lot. But unfortunately it didn't work. I guess I will have to wait until ChAMP becomes available for version 3.19 In the meantime I really like to try other packages for my differential methylation analysis. I have a beta matrix file. I was wondering whether I could use Limma for analysing direct beta value files. Could you please suggest any tutorial where the analysis is done from beta matrix files? Again thanks a lot for your prompt reply!

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If you have beta values, try the DSS package. An alternative is to compute the logit transform (to M-values), which are amenable to analysis using limma. If you are interested in DMRs, you could use either DMRcate or minfi.

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