update indentation in filter script

AlexsLemonade · Apr 15, 2024 · 1c450bb · 1c450bb
1 parent 4bcdecf
commit 1c450bb
Showing 1 changed file with 84 additions and 101 deletions.
diff --git a/bin/filter_sce.R b/bin/filter_sce.R
@@ -101,113 +101,96 @@ if (ncol(filtered_sce) == 0) {
   file.create(opt$filtered_file)
   quit(save = "no")
 }
- 
+
 # need to remove old gene-level rowData statistics first
 drop_cols <- colnames(rowData(filtered_sce)) %in% c("mean", "detected")
-  rowData(filtered_sce) <- rowData(filtered_sce)[!drop_cols]
-
-  # recalculate rowData and add to filtered sce
-  filtered_sce <- filtered_sce |>
-    scuttle::addPerFeatureQCMetrics()
-
-  # add prob_compromised to colData and miQC model to metadata
-  # since this can fail, we will check for success
-  miQC_worked <- FALSE
-  try({
-    filtered_sce <- scpcaTools::add_miQC(
-      filtered_sce,
-      posterior_cutoff = opt$prob_compromised_cutoff,
-      enforce_left_cutoff = opt$enforce_left_cutoff
+rowData(filtered_sce) <- rowData(filtered_sce)[!drop_cols]
+# recalculate rowData and add to filtered sce
+filtered_sce <- filtered_sce |>
+  scuttle::addPerFeatureQCMetrics()
+# add prob_compromised to colData and miQC model to metadata
+# since this can fail, we will check for success
+miQC_worked <- FALSE
+try({
+  filtered_sce <- scpcaTools::add_miQC(
+    filtered_sce,
+    posterior_cutoff = opt$prob_compromised_cutoff,
+    enforce_left_cutoff = opt$enforce_left_cutoff
+  )
+  metadata(filtered_sce)$prob_compromised_cutoff <- opt$prob_compromised_cutoff
+  miQC_worked <- TRUE
+})
+# set prob_compromised to NA if miQC failed
+if (!miQC_worked) {
+  warning("miQC failed. Setting `prob_compromised` to NA.")
+  filtered_sce$prob_compromised <- NA_real_
+  filtered_sce$miQC_pass <- NA
+  metadata(filtered_sce)$prob_compromised_cutoff <- NA
+}
+# grab names of altExp, if any
+alt_names <- altExpNames(filtered_sce)
+for (alt in alt_names) {
+  # remove old row data from unfiltered
+  drop_cols <- colnames(rowData(altExp(filtered_sce, alt))) %in% c("mean", "detected")
+  rowData(altExp(filtered_sce, alt)) <- rowData(altExp(filtered_sce, alt))[!drop_cols]
+  # add alt experiment features stats for filtered data
+  altExp(filtered_sce, alt) <- scuttle::addPerFeatureQCMetrics(altExp(filtered_sce, alt))
+}
+# calculate filtering QC from ADTs, if present
+if (!is.null(ambient_profile)) {
+  # Create data frame of ADTs and their target types
+  # If `target_type` column is not present, assume all ADTs are targets
+  adt_barcode_df <- readr::read_tsv(
+    opt$adt_barcode_file,
+    # if only 2 columns exist, only the first two col_names will be used
+    col_names = c("name", "barcode", "target_type")
+  )
+  # Assign default of `target` if no third column provided
+  if (!"target_type" %in% names(adt_barcode_df)) {
+    adt_barcode_df$target_type <- "target"
+  }
+  # Check for valid target types and warn if invalid found
+  expected_adt_values <- c("target", "neg_control", "pos_control")
+  if (!all(adt_barcode_df$target_type %in% expected_adt_values)) {
+    warning(
+      glue::glue(
+        "Warning: Invalid ADT type values provided in the ADT barcode file. Expected values are:
+        - target
+        - neg_control
+        - pos_control
+      Unknown ADT types will be treated as targets."
+      )
     )
-    metadata(filtered_sce)$prob_compromised_cutoff <- opt$prob_compromised_cutoff
-    miQC_worked <- TRUE
-  })
-
-  # set prob_compromised to NA if miQC failed
-  if (!miQC_worked) {
-    warning("miQC failed. Setting `prob_compromised` to NA.")
-    filtered_sce$prob_compromised <- NA_real_
-    filtered_sce$miQC_pass <- NA
-    metadata(filtered_sce)$prob_compromised_cutoff <- NA
   }
-
-  # grab names of altExp, if any
-  alt_names <- altExpNames(filtered_sce)
-
-  for (alt in alt_names) {
-    # remove old row data from unfiltered
-    drop_cols <- colnames(rowData(altExp(filtered_sce, alt))) %in% c("mean", "detected")
-    rowData(altExp(filtered_sce, alt)) <- rowData(altExp(filtered_sce, alt))[!drop_cols]
-
-    # add alt experiment features stats for filtered data
-    altExp(filtered_sce, alt) <- scuttle::addPerFeatureQCMetrics(altExp(filtered_sce, alt))
+  # Check that barcode file ADTs match SCE ADTs
+  if (!all.equal(sort(adt_barcode_df$name), sort(rownames(altExp(filtered_sce, adt_exp))))) {
+    stop("Mismatch between provided ADT barcode file and ADTs in SCE.")
   }
-
-
-  # calculate filtering QC from ADTs, if present
-  if (!is.null(ambient_profile)) {
-    # Create data frame of ADTs and their target types
-    # If `target_type` column is not present, assume all ADTs are targets
-    adt_barcode_df <- readr::read_tsv(
-      opt$adt_barcode_file,
-      # if only 2 columns exist, only the first two col_names will be used
-      col_names = c("name", "barcode", "target_type")
+  # Find any negative controls
+  neg_controls <- adt_barcode_df |>
+    dplyr::filter(target_type == "neg_control") |>
+    dplyr::pull(name)
+  # Calculate QC stats, providing negative controls if present
+  # note: function fails if controls is length 0 or null, so keep the `if`
+  if (length(neg_controls) == 0) {
+    adt_qc_df <- DropletUtils::cleanTagCounts(
+      counts(altExp(filtered_sce, adt_exp)),
+      ambient = ambient_profile
     )
-
-    # Assign default of `target` if no third column provided
-    if (!"target_type" %in% names(adt_barcode_df)) {
-      adt_barcode_df$target_type <- "target"
-    }
-    # Check for valid target types and warn if invalid found
-    expected_adt_values <- c("target", "neg_control", "pos_control")
-    if (!all(adt_barcode_df$target_type %in% expected_adt_values)) {
-      warning(
-        glue::glue(
-          "Warning: Invalid ADT type values provided in the ADT barcode file. Expected values are:
-          - target
-          - neg_control
-          - pos_control
-        Unknown ADT types will be treated as targets."
-        )
-      )
-    }
-
-    # Check that barcode file ADTs match SCE ADTs
-    if (!all.equal(sort(adt_barcode_df$name), sort(rownames(altExp(filtered_sce, adt_exp))))) {
-      stop("Mismatch between provided ADT barcode file and ADTs in SCE.")
-    }
-
-    # Find any negative controls
-    neg_controls <- adt_barcode_df |>
-      dplyr::filter(target_type == "neg_control") |>
-      dplyr::pull(name)
-
-    # Calculate QC stats, providing negative controls if present
-    # note: function fails if controls is length 0 or null, so keep the `if`
-    if (length(neg_controls) == 0) {
-      adt_qc_df <- DropletUtils::cleanTagCounts(
-        counts(altExp(filtered_sce, adt_exp)),
-        ambient = ambient_profile
-      )
-    } else {
-      adt_qc_df <- DropletUtils::cleanTagCounts(
-        counts(altExp(filtered_sce, adt_exp)),
-        ambient = ambient_profile,
-        controls = neg_controls
-      )
-    }
-
-    # Save QC stats to the altexp
-    colData(altExp(filtered_sce, adt_exp)) <- cbind(
-      colData(altExp(filtered_sce, adt_exp)),
-      adt_qc_df
+  } else {
+    adt_qc_df <- DropletUtils::cleanTagCounts(
+      counts(altExp(filtered_sce, adt_exp)),
+      ambient = ambient_profile,
+      controls = neg_controls
     )
-
-    # Add `target_type` to rowData
-    rowData(altExp(filtered_sce, adt_exp))$target_type <- adt_barcode_df$target_type
   }
-
-
-  # write filtered sce to output
-  readr::write_rds(filtered_sce, opt$filtered_file, compress = "bz2")
+  # Save QC stats to the altexp
+  colData(altExp(filtered_sce, adt_exp)) <- cbind(
+    colData(altExp(filtered_sce, adt_exp)),
+    adt_qc_df
+  )
+  # Add `target_type` to rowData
+  rowData(altExp(filtered_sce, adt_exp))$target_type <- adt_barcode_df$target_type
 }
+# write filtered sce to output
+readr::write_rds(filtered_sce, opt$filtered_file, compress = "bz2")