Draft PR from Tina

README.md

@@ -21,8 +21,9 @@ Numbat does not require paired DNA or genotype data and operates solely on the d
 
 > [Teng Gao, Ruslan Soldatov, Hirak Sarkar, Adam Kurkiewicz, Evan Biederstedt, Po-Ru Loh, Peter Kharchenko. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nature Biotechnology (2022).](https://www.nature.com/articles/s41587-022-01468-y)
 
-## Numbat-multiome
+## Numbat-Paired (ATAC and RNA)
 Numbat was later extended to multi-modality (single-cell RNA and ATAC) data. Check out the [vignette](https://kharchenkolab.github.io/numbat/articles/numbat-multiome.html) and paper below:
+- 10X Multiome data can be ran with this version as well, treating each barcode separately based on modality. The Numbat-Multiome weighting CNV probabilities based on evidence from ATAC and RNA is currently under development.
 
 > [Ruitong Li, Jean-Baptiste Alberge, Tina Keshavarzian, Junko Tsuji, Johan Gustafsson, Mahshid Rahmat, Elizabeth D Lightbody, Stephanie L Deng, Santiago Riviero, Mendy Miller, F Naz Cemre Kalayci, Adrian Wiestner, Clare Sun, Mathieu Lupien, Irene Ghobrial, Erin Parry, Teng Gao, Gad Getz. Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data. Briefings in Bioinformatics (2025).](https://academic.oup.com/bib/article/26/5/bbaf516/8290422)
 

inst/bin/input_prep.R

@@ -48,25 +48,80 @@ cntsubN <- function(cnt,f,maxn,seed){
   return(cnt[f,sample(colnames(cnt),size=min(maxn,ncol(cnt)))])
   }
 }
-binCnt <- function(bincntF,seed,maxCB=10000){
+binCnt <- function(bincntF,seed,maxCB=10000,add_suffix=FALSE,suffix_tag=c("_RNA","_ATAC")){
   cntmat <- map(bincntF,loadcnt)
   sharedFeature <- intersect(rownames(cntmat[[1]]),rownames(cntmat[[2]]))
-  comb_cnt <- cbind(cntsubN(cntmat[[1]],sharedFeature,maxCB,seed),
-                    cntsubN(cntmat[[2]],sharedFeature,maxCB,seed))
-  return(comb_cnt)
+
+  if(add_suffix){
+    if(length(suffix_tag) != 2){
+      stop("suffix_tag must be a character vector of length 2")
+    }
+
+    # get all unique column names across both matrices
+    all_cols_1 <- colnames(cntmat[[1]])
+    all_cols_2 <- colnames(cntmat[[2]])
+    all_unique_cols <- unique(c(all_cols_1, all_cols_2))
+
+    # subsample from all barcodes
+    set.seed(seed)
+    n_to_sample <- min(maxCB, length(all_unique_cols))
+    sampled_cells <- sample(all_unique_cols, size = n_to_sample)
+
+    result_cols <- character()
+    result_mat <- NULL
+
+    for(cell in sampled_cells){
+      cell_in_1 <- cell %in% all_cols_1
+      cell_in_2 <- cell %in% all_cols_2
+
+      if(cell_in_1){
+        cell_data_1 <- cntmat[[1]][sharedFeature, cell, drop=FALSE]
+        colnames(cell_data_1) <- paste0(cell, suffix_tag[1])
+        if(is.null(result_mat)){
+          result_mat <- cell_data_1
+        } else {
+          result_mat <- cbind(result_mat, cell_data_1)
+        }
+        result_cols <- c(result_cols, colnames(cell_data_1))
+      }
+
+      if(cell_in_2){
+        cell_data_2 <- cntmat[[2]][sharedFeature, cell, drop=FALSE]
+        colnames(cell_data_2) <- paste0(cell, suffix_tag[2])
+        if(is.null(result_mat)){
+          result_mat <- cell_data_2
+        } else {
+          result_mat <- cbind(result_mat, cell_data_2)
+        }
+        result_cols <- c(result_cols, colnames(cell_data_2))
+      }
+    }
+
+    colnames(result_mat) <- result_cols
+    return(result_mat)
+
+  } else {
+    # no suffix needed, subsample independently then combine
+    cnt1_sub <- cntsubN(cntmat[[1]],sharedFeature,maxCB,seed)
+    cnt2_sub <- cntsubN(cntmat[[2]],sharedFeature,maxCB,seed)
+    comb_cnt <- cbind(cnt1_sub,cnt2_sub)
+    return(comb_cnt)
+  }
 }
 
 library(purrr)
 library(Matrix)
 
-binCnt_union <- function(bincntF, seed, maxCB = 10000) {
+binCnt_union <- function(bincntF, seed, maxCB = 10000, 
+                         add_suffix = FALSE, suffix_tag = c("_RNA","_ATAC"), 
+                         runMultiomeAsSummed = FALSE) {
   # 1. Load the two count matrices
   cnt1 <- loadcnt(bincntF[1])
   cnt2 <- loadcnt(bincntF[2])
 
   allFeat <- union(rownames(cnt1), rownames(cnt2))
 
-  # 3. For each matrix, add zero‐rows for any features it’s missing
+  # 3. For each matrix, add zero‐rows for any features it's missing
   add_missing <- function(mat, allFeat) {
     missing <- setdiff(allFeat, rownames(mat))
     if (length(missing)) {
@@ -77,16 +132,184 @@ binCnt_union <- function(bincntF, seed, maxCB = 10000) {
                            dimnames = list(missing, colnames(mat)))
       mat <- rbind(mat, zero_block)
     }
-    # reorder to match allFeat
     mat[allFeat, , drop = FALSE]
   }
   cnt1_u <- add_missing(cnt1, allFeat)
   cnt2_u <- add_missing(cnt2, allFeat)
+
+  if(runMultiomeAsSummed){
+    print('Summing read counts for each modality, resulting one summed feature per cell barcode. Use the --add_suffix and --suffix_tag to run multiome as paired. Note: this is only applicable if you are running multiome data as paired and want each barcode to be counted once.  If you are looking for the true Multiome version, please follow the appropriate steps in the vignette.')
+    if(add_suffix){
+      print('Multiome can either be used in paired mode or as summed mode for this algorithm. Either --runMultiomeAsSummed can be true or --add_suffix. ')
+      stop('Please specify either --runMultiomeAsSummed or --add_suffix, but not both.')
+    }
+    intersect_cells <- intersect(colnames(cnt1), colnames(cnt2))
+    set.seed(seed)
+    n_to_sample <- min(maxCB, length(intersect_cells))
+    sampled_cells <- sample(intersect_cells, size = n_to_sample)
+    cnt <- cnt1[,sampled_cells] + cnt2[,sampled_cells]
+    return(cnt)
+  }
+
+  if(add_suffix){
+    print('Adding suffix tags to the barcodes used in the count matrices. Note: this is only applicable if you are running multiome data as paired, since we want to ensure each barcode is unique for the algorithm. If you are looking for the true Multiome version, please follow the appropriate steps in the vignette.')
+    if(length(suffix_tag) != 2){
+      stop("suffix_tag must be a character vector of length 2")
+    }
+
+    all_cols_1 <- colnames(cnt1_u)
+    all_cols_2 <- colnames(cnt2_u)
+    all_unique_cols <- unique(c(all_cols_1, all_cols_2))
+
+    # subsample from the union of ALL columns (without suffixes)
+    set.seed(seed)
+    n_to_sample <- min(maxCB, length(all_unique_cols))
+    sampled_cells <- sample(all_unique_cols, size = n_to_sample)
+
+    # for each sampled cell, check which modalities it appears in and add appropriate columns
+    result_cols <- character()
+    result_mat <- NULL
+
+    for(cell in sampled_cells){
+      cell_in_1 <- cell %in% all_cols_1
+      cell_in_2 <- cell %in% all_cols_2
+
+      if(cell_in_1){
+        cell_data_1 <- cnt1_u[, cell, drop=FALSE]
+        colnames(cell_data_1) <- paste0(cell, suffix_tag[1])
+        if(is.null(result_mat)){
+          result_mat <- cell_data_1
+        } else {
+          result_mat <- cbind(result_mat, cell_data_1)
+        }
+        result_cols <- c(result_cols, colnames(cell_data_1))
+      }
+
+      if(cell_in_2){
+        cell_data_2 <- cnt2_u[, cell, drop=FALSE]
+        colnames(cell_data_2) <- paste0(cell, suffix_tag[2])
+        if(is.null(result_mat)){
+          result_mat <- cell_data_2
+        } else {
+          result_mat <- cbind(result_mat, cell_data_2)
+        }
+        result_cols <- c(result_cols, colnames(cell_data_2))
+      }
+    }
+
+    colnames(result_mat) <- result_cols
+    return(result_mat)
+
+  } else {
+    # if no suffix addition is needed, thensubsample independently then combine
+    cnt1_sub <- cntsubN(cnt1_u, allFeat, maxCB, seed)
+    cnt2_sub <- cntsubN(cnt2_u, allFeat, maxCB, seed)
+    comb_cnt <- cbind(cnt1_sub, cnt2_sub)
+    return(comb_cnt)
+  }
+}
+
+
+
+
+# input prep for allele dataframe if running multiome as paired
+#' Combine and harmonize allele counts from RNA and ATAC modalities
+#' @param alleleCounts_RNA Character. Path to the RNA-based allele counts file
+#' @param alleleCounts_ATAC Character. Path to the ATAC-based allele counts file
+#' @param output_file Character. File for combined allele outputs 
+#' @param compress Logical. Whether to compress the output file (default: TRUE)
+#' @return Character. Path to the output file
+combine_allele_counts <- function(alleleCounts_RNA,
+                                  alleleCounts_ATAC,
+				  output_file, 
+				  addBarcodeSuff = FALSE,
+                                  compress = TRUE) {
+  geno_files <- c(alleleCounts_RNA, alleleCounts_ATAC)
+
+  if (!all(file.exists(geno_files))) {
+    missing_files <- geno_files[!file.exists(geno_files)]
+    stop("The following genotype files do not exist: ", 
+         paste(missing_files, collapse = ", "))
+  }
+
 
-  # 4. Subsample & combine
-  comb_cnt <- cbind(
-    cntsubN(cnt1_u, allFeat, maxCB, seed),
-    cntsubN(cnt2_u, allFeat, maxCB, seed)
-  )
-  comb_cnt
+#  message("Harmonizing allele counts for sample: ", sample_id)
+
+  # Read first allele counts file (RNA)
+  df1 <- tryCatch({
+    data.table::fread(alleleCounts_RNA, header = TRUE, sep = "\t") %>% 
+      filter(CHROM %in% 1:22) %>%
+      mutate(CHROM = factor(CHROM, 1:22))
+  }, error = function(e) {
+    stop("Error reading RNA allele counts file: ", e$message)
+  })
+
+  # Extract unique SNPs and genotypes from RNA allele counts file
+  geno1 <- df1 %>% distinct(snp_id, GT)
+
+  # Read second allele counts file (ATAC)
+  df2 <- tryCatch({
+    data.table::fread(alleleCounts_ATAC, header = TRUE, sep = "\t") %>% 
+      filter(CHROM %in% 1:22) %>%
+      mutate(CHROM = factor(CHROM, 1:22))
+  }, error = function(e) {
+    stop("Error reading ATAC allele counts file: ", e$message)
+  })
+
+  # Extract unique SNPs and genotypes from ATAC allele counts file
+  geno2 <- df2 %>% distinct(snp_id, GT)
+
+  # Find shared SNPs between the two datasets
+  sharedSNP <- intersect(geno1$snp_id, geno2$snp_id)
+  message("Found ", length(sharedSNP), " shared SNPs between modalities")
+
+  # Create reference dataframe from second file's genotypes
+  snp_df <- geno2 %>% 
+    filter(snp_id %in% sharedSNP) %>% 
+    column_to_rownames("snp_id")
+
+  # Update genotypes in first file to match second file for shared SNPs
+  geno1 <- geno1 %>%
+    mutate(GT = case_when(
+      snp_id %in% sharedSNP ~ snp_df[snp_id, "GT"],
+      TRUE ~ GT
+    ))
+
+  geno1_df <- geno1 %>% column_to_rownames("snp_id")
+  df1$GT <- geno1_df[df1$snp_id, "GT"]
+
+  if(addBarcodeSuff == TRUE){
+	  df1$cell = paste0(df1$cell, '_RNA')
+	  df2$cell = paste0(df2$cell, '_ATAC')
+  } 
+  combined_df <- rbind(df1, df2)
+
+#  output_file <- file.path(output_dir, paste0(sample_id, "_allele_counts_consistent.tsv"))
+
+  tryCatch({
+    data.table::fwrite(
+      combined_df, 
+      output_file, 
+      quote = FALSE, 
+      row.names = FALSE, 
+      #nThread = 3, 
+      sep = "\t"
+    )
+    message("Successfully wrote combined allele counts to: ", output_file)
+  }, error = function(e) {
+    stop("Error writing combined file: ", e$message)
+  })
+
+  if (compress) {
+    tryCatch({
+      system2("gzip", args = output_file)
+      output_file <- paste0(output_file, ".gz")
+      message("Successfully compressed output file")
+    }, error = function(e) {
+      warning("Failed to compress output file: ", e$message)
+    })
+  }
+
+  return(output_file)
 }
+

inst/bin/run_numbat_multiome.R

@@ -8,6 +8,7 @@ option_list = list(make_option("--countmat", default = NULL),
                    make_option("--ref", default = NULL),
                    make_option("--out_dir", default = NULL),
                    make_option("--parL", default = NULL)
+                   make_option("--runMultiomeAsPaired", default = FALSE)
                    )
 args = parse_args(OptionParser(option_list = option_list))
 
@@ -83,8 +84,17 @@ if(mean(covs)>150){
   df_allele <-  df_allele%>%sample_frac(frac)
 }
 
-#==== Parameter change ====
+#==== Quick check if we are running multiome data as paired ====
 cat('Running Numbat\n')
+if(args$runMultiomeAsPaired){
+  # check if suffix for count matrix / allele dataframe is present
+  if(!(any(endsWith(colnames(df_count), '_RNA')) && any(endsWith(colnames(df_count), '_ATAC')))){
+    stop("Suffix _RNA or _ATAC not found in count matrix column names")
+  }
+  if(!(any(endsWith(df_allele$cell, '_RNA')) && any(endsWith(colnames(df_allele$cell), '_ATAC')))){
+    stop("Suffix _RNA or _ATAC not found in allele dataframe cell names")
+  }
+}
 numbatPars$count_mat = df_count
 numbatPars$lambdas_ref =readRDS(args$ref) %>% as.matrix()
 numbatPars$df_allele =df_allele