fsva function does not work without newdat argument
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Last seen 16 months ago

Hi all,

i'm using the fsva function from the sva package to correct batch effects in my data and i think that i might have found a bug in the function

Here is an example from the fsva documentation:

dat <- bladderEset[1:50,]

pheno = pData(dat)
edata = exprs(dat)

trainIndicator = sample(1:57,size=30,replace=FALSE)
testIndicator = (1:57)[-trainIndicator]
trainData = edata[,trainIndicator]
testData = edata[,testIndicator]
trainPheno = pheno[trainIndicator,]
testPheno = pheno[testIndicator,]

mydata = list(x=trainData,y=trainPheno$cancer)
mytrain = pamr.train(mydata)

trainMod = model.matrix(~cancer,data=trainPheno)
trainMod0 = model.matrix(~1,data=trainPheno)
trainSv = sva(trainData,trainMod,trainMod0)

fsvaobj = fsva(trainData,trainMod,trainSv,testData)

which works great.

However, if i run the function without the newdat argument, i get the following error message:

fsvaobj = fsva(trainData,trainMod,trainSv)
Error in fsva(trainData, trainMod, trainSv) : object 'newV' not found

after setting the newV variable in my global environment, it works well again:

newV <- NULL
fsvaobj = fsva(trainData,trainMod,trainSv)

I guess that the variable is not defined within the function if the newdat argument is NULL. In summary, i think it is not a very important bug, but the authors might want to correct it anyway.

Cheers, Jakob

PS: here is the sessionInfo() output. Hope that it is not a versioning problem (sva version sva_3.30.1).

> sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

[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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] sva_3.30.1          BiocParallel_1.16.6 genefilter_1.64.0   mgcv_1.8-28         nlme_3.1-141       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2           compiler_3.5.3       pillar_1.4.2         bitops_1.0-6         tools_3.5.3          zeallot_0.1.0       
 [7] digest_0.6.20        bit_1.1-14           annotate_1.60.1      RSQLite_2.1.2        memoise_1.1.0        tibble_2.1.3        
[13] lattice_0.20-38      pkgconfig_2.0.2      rlang_0.4.0          Matrix_1.2-17        DBI_1.0.0            rstudioapi_0.10     
[19] yaml_2.2.0           parallel_3.5.3       S4Vectors_0.20.1     vctrs_0.2.0          IRanges_2.16.0       stats4_3.5.3        
[25] bit64_0.9-7          grid_3.5.3           Biobase_2.42.0       AnnotationDbi_1.44.0 survival_2.44-1.1    XML_3.98-1.20       
[31] limma_3.38.3         blob_1.2.0           matrixStats_0.54.0   backports_1.1.4      splines_3.5.3        BiocGenerics_0.28.0 
[37] xtable_1.8-4         RCurl_1.95-4.12      crayon_1.3.4        
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