Delete my_diffcyt.R

code/my_diffcyt.R

-my_diffcyt <-
-function (d_input, experiment_info = NULL, marker_info = NULL, 
-          design = NULL, formula = NULL, contrast, analysis_type = c("DA", 
-                                                                     "DS"), method_DA = c("diffcyt-DA-edgeR", "diffcyt-DA-voom", 
-                                                                                          "diffcyt-DA-GLMM"), method_DS = c("diffcyt-DS-limma", 
-                                                                                                                            "diffcyt-DS-LMM"), markers_to_test = NULL, clustering_to_use = NULL, 
-          cols_to_include = NULL, subsampling = FALSE, n_sub = NULL, 
-          seed_sub = NULL, transform = TRUE, cofactor = 5, cols_clustering = NULL, 
-          xdim = 10, ydim = 10, meta_clustering = FALSE, meta_k = 40, 
-          seed_clustering = NULL, min_cells = 3, min_samples = NULL, 
-          normalize = FALSE, norm_factors = "TMM", trend_method = "none", 
-          block_id = NULL, trend = TRUE, weights = TRUE, plot = TRUE, 
-          path = ".", verbose = TRUE) 
-{
-  analysis_type <- match.arg(analysis_type)
-  method_DA <- match.arg(method_DA)
-  method_DS <- match.arg(method_DS)
-  if (!is(d_input, "SingleCellExperiment")) {
-    if (is.null(experiment_info) | is.null(marker_info)) {
-      stop("'experiment_info' and 'marker_info' must be provided (unless using a SingleCellExperiment ", 
-           "object from CATALYST as input)")
-    }
-    if (verbose) 
-      message("preparing data...")
-    d_se <- prepareData(d_input, experiment_info, marker_info, 
-                        cols_to_include, subsampling, n_sub, seed_sub)
-    if (transform) {
-      if (verbose) 
-        message("transforming data...")
-      d_se <- transformData(d_se, cofactor)
-    }
-    if (verbose) 
-      message("generating clusters...")
-    d_se <- generateClusters(d_se, cols_clustering, xdim, 
-                             ydim, meta_clustering, meta_k, seed_clustering)
-  }
-  else if (is(d_input, "SingleCellExperiment")) {
-    if (verbose) 
-      message("using SingleCellExperiment object from CATALYST as input")
-    if (is.null(clustering_to_use)) {
-      stopifnot("cluster_id" %in% colnames(colData(d_input)))
-      if (verbose) 
-        message("using cluster IDs stored in column named 'cluster_id' in 'colData' of ", 
-                "SingleCellExperiment object from CATALYST")
-      clustering_name <- colnames(metadata(d_input)$cluster_codes)[1]
-    }
-    else if (!is.null(clustering_to_use)) {
-      stopifnot(as.character(clustering_to_use) %in% colnames(metadata(d_input)$cluster_codes))
-      stopifnot("cluster_id" %in% colnames(colData(d_input)))
-      if (verbose) 
-        message("using cluster IDs from clustering stored in column '", 
-                clustering_to_use, "' of 'cluster_codes' data frame in 'metadata' of SingleCellExperiment object from CATALYST")
-      code_id <- colData(d_input)$cluster_id
-      cluster_id <- metadata(d_input)$cluster_codes[, clustering_to_use][code_id]
-      stopifnot(length(cluster_id) == nrow(colData(d_input)), 
-                length(code_id) == nrow(colData(d_input)))
-      colData(d_input)$code_id <- code_id
-      colData(d_input)$cluster_id <- cluster_id
-      clustering_name <- clustering_to_use
-    }
-    stopifnot("sample_id" %in% colnames(colData(d_input)))
-    stopifnot("experiment_info" %in% names(metadata(d_input)))
-    stopifnot("cluster_id" %in% colnames(colData(d_input)))
-    stopifnot("cluster_codes" %in% names(metadata(d_input)))
-    cs_by_s <- split(seq_len(ncol(d_input)), colData(d_input)$sample_id)
-    cs <- unlist(cs_by_s[metadata(d_input)$experiment_info$sample_id])
-    es <- t(assays(d_input)[["exprs"]])[cs, , drop = FALSE]
-    d_se <- SummarizedExperiment(assays = list(exprs = es), 
-                                 rowData = colData(d_input)[cs, ], colData = rowData(d_input), 
-                                 metadata = metadata(d_input))
-  }
-  if (verbose) 
-    message("calculating features...")
-  d_counts <- calcCounts(d_se)
-  return(d_counts)
-  d_medians <- calcMedians(d_se)
-  d_medians_by_cluster_marker <- calcMediansByClusterMarker(d_se)
-  d_medians_by_sample_marker <- calcMediansBySampleMarker(d_se)
-  if (analysis_type == "DA" && method_DA == "diffcyt-DA-edgeR") {
-    if (verbose) 
-      message("calculating DA tests using method 'diffcyt-DA-edgeR'...")
-    res <- testDA_edgeR(d_counts, design, contrast, trend_method, 
-                        min_cells, min_samples, normalize, norm_factors)
-  }
-  if (analysis_type == "DA" && method_DA == "diffcyt-DA-voom") {
-    if (verbose) 
-      message("calculating DA tests using method 'diffcyt-DA-voom'...")
-    res <- testDA_voom(d_counts, design, contrast, block_id, 
-                       min_cells, min_samples, normalize, norm_factors, 
-                       plot, path)
-  }
-  if (analysis_type == "DA" && method_DA == "diffcyt-DA-GLMM") {
-    if (verbose) 
-      message("calculating DA tests using method 'diffcyt-DA-GLMM'...")
-    res <- testDA_GLMM(d_counts, formula, contrast, min_cells, 
-                       min_samples, normalize, norm_factors)
-  }
-  if (analysis_type == "DS" && method_DS == "diffcyt-DS-limma") {
-    if (verbose) 
-      message("calculating DS tests using method 'diffcyt-DS-limma'...")
-    res <- testDS_limma(d_counts, d_medians, design, contrast, 
-                        block_id, trend, weights, markers_to_test, min_cells, 
-                        min_samples, plot, path)
-  }
-  if (analysis_type == "DS" && method_DS == "diffcyt-DS-LMM") {
-    if (verbose) 
-      message("calculating DS tests using method 'diffcyt-DS-LMM'...")
-    res <- testDS_LMM(d_counts, d_medians, formula, contrast, 
-                      weights, markers_to_test, min_cells, min_samples)
-  }
-  if (!is(d_input, "SingleCellExperiment")) {
-    return(list(res = res, d_se = d_se, d_counts = d_counts, 
-                d_medians = d_medians, d_medians_by_cluster_marker = d_medians_by_cluster_marker, 
-                d_medians_by_sample_marker = d_medians_by_sample_marker))
-  }
-  else if (is(d_input, "SingleCellExperiment")) {
-    metadata(res) <- as.list(c(metadata(res), clustering_name = clustering_name))
-    return(list(res = res, d_counts = d_counts, d_medians = d_medians, 
-                d_medians_by_cluster_marker = d_medians_by_cluster_marker, 
-                d_medians_by_sample_marker = d_medians_by_sample_marker))
-  }
-}
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