multiSampleComparisonClonalCN Error in vegaMC_R(mtx = mtx, output_file_name = paste("./output/", sample, : NA/NaN/Inf in foreign function call (arg 1)

[Prakrithi-P]

Hi,
Thanks for the wonderful tool. However, I have been facing some issues. When I run the single sample pipeline, it works fine (some samples throw an error which I had raised in an already open issue). When I combine multiple samples, I get the following error:

Error in vegaMC_R(mtx = mtx, output_file_name = paste("./output/", sample, : NA/NaN/Inf in foreign function call (arg 1)
Traceback:

1. multiSampleComparisonClonalCN(listCountMtx, listNormCells, analysisName = "P5_combined")
2. lapply(names(listCountMtx), function(x) {
   . pipelineCNA(listCountMtx[[x]], norm_cell = listNormCells[[x]],
   . sample = x, SUBCLONES = FALSE, ClonalCN = TRUE, par_cores = par_cores,
   . organism = organism)
   . })
3. FUN(X[[i]], ...)
4. pipelineCNA(listCountMtx[[x]], norm_cell = listNormCells[[x]],
   . sample = x, SUBCLONES = FALSE, ClonalCN = TRUE, par_cores = par_cores,
   . organism = organism)
5. classifyTumorCells(res_proc$count_mtx_norm, res_proc$count_mtx_annot,
   . sample, par_cores = par_cores, ground_truth = NULL, norm_cell_names = norm_cell,
   . SEGMENTATION_CLASS = TRUE, SMOOTH = TRUE, beta_vega = beta_vega)
6. getBreaksVegaMC(mtx_vega, annot_mtx[, 3], sample, beta_vega)
7. vegaMC_R(mtx = mtx, output_file_name = paste("./output/", sample,
   . "vega_output"), beta = beta_vega)

Could you please help me fix this?
code used:

List of matrices names

matrix_names <- c('P5_N','P5_SCC_BCC')

Create an empty list

listCountMtx <- list()

Populate the list with matrices

for (name in matrix_names) {
listCountMtx[[name]] <- get(name)
}

List of normal cell

N_P5SCC<-P5_SCC_BCC[,c('AAACGGGCATTGCGGC-2',
'AAAGCAAGTCTGATCA-2',
'AAAGTAGCAGGATTGG-2',
'AAAGTAGCATTTCAGG-2',
'AAATGCCAGACTCGGA-2',
'AACACGTGTGTAAGTA-2',
'AACTCAGAGTGGTAAT-2',
'AACTCTTAGGCAATTA-2',
'AAGGAGCGTAATAGCA-2',
'AATCGGTCATACAGCT-2',
'ACACCCTAGGCTAGAC-2',
'ACACCCTCAGGAACGT-2',
'ACACCGGGTCCAGTAT-2',
'ACAGCTAGTTGATTGC-2',
'ACATCAGGTCTAGTGT-2',
'ACGATACCAAATCCGT-2',
'ACGATACGTAAACCTC-2',
'ACGCAGCAGTAGATGT-2',
'ACGCAGCGTCAGAGGT-2',
'ACGGAGAAGTGTCCCG-2',
'ACGGGCTGTCCGTTAA-2',
'ACTATCTCATCCAACA-2',
'ACTGAACGTGCTAGCC-2',
'ACTGAGTGTGATGCCC-2',
'ACTGTCCAGGATGTAT-2')]
#matrix_names <- c('P1_N','P1_SCC','P2_N','P2_SCC1','P2_SCC2','P3_IEC','P3_N','P4_N','P4_SCC','P5_N','P5_SCC_BCC')
matrix_names <- c('P5_N','N_P5SCC')

Create an empty list

listNormCells <- list()

Populate the list with matrices

for (name in matrix_names) {
listNormCells[[name]] <- get(name)
}

res5<-multiSampleComparisonClonalCN(listCountMtx, listNormCells, analysisName="P5_combined")

Fixing this would be really helpful for my project.

Thanks!
Prakrithi

[Prakrithi-P]

Update: I realized I had to specify a list of the list of normal cells and not a list of normal cell matrices.

However, I get the error
[1] " raw data - genes: 34937 cells: 2887"
[1] "1) Filter: cells > 200 genes"
[1] "filtered out 183 cells past filtering 2708 cells"
[1] "low data quality"
[1] "2) Filter: genes > 5% of cells"
[1] "5959 genes past filtering"
[1] "3) Annotations gene coordinates"
[1] "5607 genes annotated"
[1] "4) Filter: genes involved in the cell cycle"
[1] "5283 genes past filtering "
[1] "5) Filter: cells > 5genes per chromosome "
[1] "6) Log Freeman Turkey transformation"
[1] "A total of 2245 cells, 5283 genes after preprocessing"
[1] "7) Measuring baselines (confident normal cells)"
Error in count_mtx - basel: non-conformable arrays
Traceback:

1. multiSampleComparisonClonalCN(listCountMtx, main_list, analysisName = "P5_combined")
2. lapply(names(listCountMtx), function(x) {
   . pipelineCNA(listCountMtx[[x]], norm_cell = listNormCells[[x]],
   . sample = x, SUBCLONES = FALSE, ClonalCN = TRUE, par_cores = par_cores,
   . organism = organism)
   . })
3. FUN(X[[i]], ...)
4. pipelineCNA(listCountMtx[[x]], norm_cell = listNormCells[[x]],
   . sample = x, SUBCLONES = FALSE, ClonalCN = TRUE, par_cores = par_cores,
   . organism = organism)
5. classifyTumorCells(res_proc$count_mtx_norm, res_proc$count_mtx_annot,
   . sample, par_cores = par_cores, ground_truth = NULL, norm_cell_names = norm_cell,
   . SEGMENTATION_CLASS = TRUE, SMOOTH = TRUE, beta_vega = beta_vega)