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Question: Error Combat (SVA)
0
gravatar for alison.sara.waller
13 months ago by
Germany
alison.sara.waller0 wrote:

Hi there,

I am analyzing high-throughput metabolomics data.

The samples were run in 6 batches.

I previously used sva/combat to normalize for the batch effects.  We have since re-run the feature finding, I am running my old script on new data and now getting an error.  The rows are the samples, and the columns are the metabolites.

    df_CmB<-ComBat(t(df),batch=df_MSBa,mod=NULL)

> str(df)
 num [1:5771, 1:5164] 10.3 11.2 11.1 13 11.7 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:5771] "PA14_EM_1-1_A-10_1839" "PA14_EM_1-1_A-11_1840" "PA14_EM_1-1_A-12_1841" "PA14_EM_1-1_A-1_1830" ...
  ..$ : chr [1:5164] "nf_7526" "nf_3587" "nf_1096" "nf_4568" ...

> str( df_MSBa)
 Factor w/ 6 levels "MSB1","MSB2",..: 1 1 1 1 1 1 1 1 1 1 ...

> length(df_MSBa)
[1] 5771

I"m getting 2 different error messages.  The first time it said 0 covariates, the second 6.

Any help is appreciated.

> df_CmB<-ComBat(t(df),batch=df_MSBa,mod=NULL)
Found6batches
Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Error in solve.default(crossprod(design), tcrossprod(t(design), as.matrix(dat))) :
  Lapack routine dgesv: system is exactly singular: U[6,6] = 0

> df_CmB<-ComBat(t(df),batch=df_MSBa,mod=NULL)
Found6batches
Adjusting for-6covariate(s) or covariate level(s)
Standardizing Data across genes
Error in tcrossprod(t(design), as.matrix(dat)) :
  non-conformable arguments

 

> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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

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

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

other attached packages:
[1] sva_3.26.0          BiocParallel_1.12.0 genefilter_1.60.0   mgcv_1.8-22        
[5] nlme_3.1-131        plyr_1.8.4          dplyr_0.7.4         gdata_2.18.0       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.14         compiler_3.4.3       bindr_0.1           
 [4] bitops_1.0-6         tools_3.4.3          digest_0.6.13       
 [7] bit_1.1-12           annotate_1.56.1      RSQLite_2.0         
[10] memoise_1.1.0        tibble_1.3.4         lattice_0.20-35     
[13] pkgconfig_2.0.1      rlang_0.1.6          Matrix_1.2-12       
[16] DBI_0.7              parallel_3.4.3       bindrcpp_0.2        
[19] IRanges_2.12.0       S4Vectors_0.16.0     gtools_3.5.0        
[22] stats4_3.4.3         bit64_0.9-7          grid_3.4.3          
[25] glue_1.2.0           Biobase_2.38.0       R6_2.2.2            
[28] AnnotationDbi_1.40.0 survival_2.41-3      XML_3.98-1.9        
[31] limma_3.34.5         blob_1.1.0           magrittr_1.5        
[34] matrixStats_0.52.2   splines_3.4.3        BiocGenerics_0.24.0
[37] assertthat_0.2.0     xtable_1.8-2         RCurl_1.95-4.9

 

 

 

 

sva • 297 views
ADD COMMENTlink written 13 months ago by alison.sara.waller0
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