Dear all,

I have a large metabolomics data set. About 6000 samples were run over a few months in 5 batches, or 61 batches (depending on definition).

At the moment for each sample I have the intensity for 21 peaks (metabolites).

    > head(df$dat)
                                          peak1    peak2    peak3    peak4    peak5     peak6    peak7
PA14_EM_14-4_E-3_P1-E-3_01_11213.mzXML 5.440897 6.505249 5.251598 7.206793 7.467628 10.759801 9.294075
PA14_EM_1-2_D-4_P1-D-4_01_2002.mzXML   5.108385 6.556920 5.543050 7.652522 6.748898  9.606819 9.019394
PA14_EM_10-4_H-5_P1-H-5_01_8890.mzXML  5.591401 6.766761 5.381610 7.471881 8.100680 10.481429 9.689601
PA14_EM_2-3_A-12_P1-A-12_01_2500.mzXML 4.618323 6.485310 4.498478 7.309714 8.813708  9.658948 9.379349
PA14_EM_9-2_C-5_P1-C-5_01_6835.mzXML   5.836406 7.378964 6.446740 8.505912 7.779362 10.045803 9.704689
PA14_EM_2_B-6_P1-B-6_01_11723.mzXML    5.231878 6.639438 5.473027 7.712421 7.425328 10.343695 9.246132
                                          peak8    peak9    peak10   peak11   peak12   peak13   peak14
PA14_EM_14-4_E-3_P1-E-3_01_11213.mzXML 9.130252 9.879932 10.441853 8.277511 7.258236 8.837902 4.522068
PA14_EM_1-2_D-4_P1-D-4_01_2002.mzXML   9.058104 8.606485  9.272817 8.047970 6.825918 7.924373 4.738949
PA14_EM_10-4_H-5_P1-H-5_01_8890.mzXML  9.744228 9.416476  9.936105 8.577914 7.534848 8.511881 4.592875
PA14_EM_2-3_A-12_P1-A-12_01_2500.mzXML 9.455950 8.490708  8.859265 8.305719 7.257221 7.529841 4.244724
PA14_EM_9-2_C-5_P1-C-5_01_6835.mzXML   9.779392 9.128307  9.420374 8.487148 7.413307 7.872341 4.345545
PA14_EM_2_B-6_P1-B-6_01_11723.mzXML    9.358493 9.539392 10.017200 8.228972 7.089368 8.362185 4.132186
                                         peak15   peak16   peak17   peak18   peak19   peak20   peak21
PA14_EM_14-4_E-3_P1-E-3_01_11213.mzXML 7.503102 8.519748 7.118348 6.301519 4.083066 5.801221 9.971810
PA14_EM_1-2_D-4_P1-D-4_01_2002.mzXML   7.843904 8.123712 6.916606 6.550114 4.741928 6.003363 9.010882
PA14_EM_10-4_H-5_P1-H-5_01_8890.mzXML  7.618536 8.226453 6.932789 6.565171 4.615487 5.906193 8.728420
PA14_EM_2-3_A-12_P1-A-12_01_2500.mzXML 7.341069 8.136234 6.191456 6.195770 4.499564 5.737833 8.022057
PA14_EM_9-2_C-5_P1-C-5_01_6835.mzXML   7.287684 8.216923 6.457609 6.364861 4.857282 5.839109 8.712801
PA14_EM_2_B-6_P1-B-6_01_11723.mzXML    7.123842 8.229959 6.656468 6.620781 4.688067 5.586544 9.881702

Here is my command:

df_cmB<-ComBat(dat=as.matrix(df$dat),batch=df$MSBa,mod=NULL)

And here is the error mesage:

Error in solve(t(design) %*% design) %*% t(design) %*% t(as.matrix(dat)) :
  non-conformable arguments

I read a similar post on stacked overflow and it was solved by removing variables with near zero variance, but I don't have any such variables

Any help is appreciated

Most software intended for the analysis of high-dimensional data (microarray, RNA-Seq, metabolomics, etc) expects that the data will be in a format with samples in columns and observations in rows. Your data are just the opposite, so try

df_cmB<-ComBat(dat=t(as.matrix(df$dat)),batch=df$MSBa,mod=NULL)

where the only difference is that I wrapped your data matrix in t() to transpose it.