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)