Add metric QC function

DESCRIPTION

@@ -78,7 +78,8 @@ Imports:
     limma,
     statmod,
     MatrixGenerics,
-    rlang
+    rlang,
+    dplyr
 RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 URL: https://github.com/LieberInstitute/spatialLIBD

NAMESPACE

@@ -3,6 +3,7 @@
 export(add10xVisiumAnalysis)
 export(add_images)
 export(add_key)
+export(add_qc_metrics)
 export(annotate_registered_clusters)
 export(check_modeling_results)
 export(check_sce)
@@ -83,8 +84,14 @@ importFrom(SummarizedExperiment,"rowRanges<-")
 importFrom(SummarizedExperiment,assay)
 importFrom(SummarizedExperiment,assayNames)
 importFrom(SummarizedExperiment,assays)
+importFrom(SummarizedExperiment,colData)
 importFrom(benchmarkme,get_ram)
 importFrom(cowplot,plot_grid)
+importFrom(dplyr,group_by)
+importFrom(dplyr,left_join)
+importFrom(dplyr,mutate)
+importFrom(dplyr,select)
+importFrom(dplyr,summarize)
 importFrom(edgeR,calcNormFactors)
 importFrom(edgeR,filterByExpr)
 importFrom(fields,image.plot)
@@ -124,6 +131,7 @@ importFrom(methods,new)
 importFrom(png,readPNG)
 importFrom(rlang,arg_match)
 importFrom(rtracklayer,import)
+importFrom(scater,isOutlier)
 importFrom(scater,plotReducedDim)
 importFrom(scuttle,aggregateAcrossCells)
 importFrom(sessioninfo,session_info)

R/add_qc_metrics.R

@@ -0,0 +1,144 @@
+#' Quality Control for Spatial Data
+#'
+#' This function identify spots in a
+#' [SpatialExperiment-class][SpatialExperiment::SpatialExperiment-class] (SPE)
+#'  with outlier quality control values: low `sum_umi` or `sum_gene`, or high
+#'  `expr_chrM_ratio`, utilizing `scran::isOutlier()`. Also identifies in-tissue
+#'  edge spots and distance to the edge for each spot.
+#'
+#' @param spe a [SpatialExperiment][SpatialExperiment::SpatialExperiment-class]
+#'
+#' @return A [SpatialExperiment][SpatialExperiment::SpatialExperiment-class]
+#' with added quiality control information added to the colData.
+#'
+#' @export
+#' @importFrom dplyr group_by summarize left_join select mutate
+#' @importFrom SummarizedExperiment colData
+#' @importFrom scater isOutlier
+#'
+#' @examples
+#' if (enough_ram()) {
+#'     ## Obtain the necessary data
+#'     if (!exists("spe")) spe <- fetch_data("spe")
+#'
+#'     ## fake out tissue spots in example data (TODO add pre-qc data)
+#'     spe_qc <- spe
+#'     spe_qc$in_tissue[spe_qc$array_col < 10] <- FALSE
+#'
+#'     ## adds QC metrics to colData of the spe
+#'     spe_qc <- add_qc_metrics(spe_qc)
+#'     colData(spe_qc)
+#'
+#'     ## visualize edge spots
+#'     vis_clus(spe_qc, sampleid = "151507", clustervar = "edge_spot")
+#'     vis_gene(spe_qc, sampleid = "151507", geneid = "edge_distance", minCount = -1)
+#'
+#'     ## visualize scran QC flags
+#'
+#'     vis_clus(spe_qc, sample_id = "151507", clustervar = "scran_low_lib_size")
+#'
+#'     # scater::plotColData(spe_qc[, spe_qc$in_tissue], x = "sample_id", y = "sum_umi", colour_by = "scran_low_lib_size")
+#'
+#'     vis_clus(spe_qc, sampleid = "151507", clustervar = "scran_discard")
+#'     vis_clus(spe_qc, sampleid = "151507", clustervar = "scran_low_lib_size_edge")
+#' }
+#' 
+add_qc_metrics <- function(spe) {
+    stopifnot("in_tissue" %in% colnames(colData(spe)))
+    stopifnot("sum_umi" %in% colnames(colData(spe)))
+    stopifnot("sum_gene" %in% colnames(colData(spe)))
+    stopifnot("expr_chrM_ratio" %in% colnames(colData(spe)))
+
+    spe$in_tissue <- as.logical(spe$in_tissue)
+    spe_in <- spe[, spe$in_tissue]
+
+    ## QC in-tissue spots
+
+    # define variables
+    low_lib_size <- low_n_features <- in_tissue <- sample_id <- NULL
+
+    qc_df <- data.frame(
+        log2sum = log2(spe_in$sum_umi),
+        log2detected = log2(spe_in$sum_gene),
+        subsets_Mito_percent = spe_in$expr_chrM_ratio * 100,
+        sample_id = spe_in$sample_id
+    )
+
+    qcfilter <- data.frame(
+        low_lib_size = scater::isOutlier(qc_df$log2sum, type = "lower", log = TRUE, batch = qc_df$sample_id),
+        low_n_features = scater::isOutlier(qc_df$log2detected, type = "lower", log = TRUE, batch = qc_df$sample_id),
+        high_subsets_Mito_percent = scater::isOutlier(qc_df$subsets_Mito_percent, type = "higher", batch = qc_df$sample_id)
+    ) |>
+        dplyr::mutate(discard = (low_lib_size | low_n_features) | high_subsets_Mito_percent)
+
+    ## Add qcfilter cols to colData(spe) after factoring
+    ## discard
+    spe$scran_discard <- NA
+    spe$scran_discard[which(spe$in_tissue)] <- qcfilter$discard
+    spe$scran_discard <- factor(spe$scran_discard, levels = c("TRUE", "FALSE"))
+
+    ## low_lib_size
+    spe$scran_low_lib_size <- NA
+    spe$scran_low_lib_size[which(spe$in_tissue)] <- qcfilter$low_lib_size
+    spe$scran_low_lib_size <- factor(spe$scran_low_lib_size,
+        levels = c("TRUE", "FALSE")
+    )
+    ## low_n_features
+    spe$scran_low_n_features <- NA
+    spe$scran_low_n_features[which(spe$in_tissue)] <- qcfilter$low_n_features
+    spe$scran_low_n_features <- factor(spe$scran_low_n_features,
+        levels = c("TRUE", "FALSE")
+    )
+
+    ## high mito percent
+    spe$scran_high_Mito_percent <- NA
+    spe$scran_high_Mito_percent[which(spe$in_tissue)] <-
+        qcfilter$high_subsets_Mito_percent
+    spe$scran_high_Mito_percent <-
+        factor(spe$scran_high_Mito_percent, levels = c("TRUE", "FALSE"))
+
+    ## Find edge spots
+    # define variables 
+    array_row <- array_col <- edge_row <- edge_col <- row_distance <- NULL
+    col_distance <- high_subsets_Mito_percent <- NULL
+
+    spot_coords <- colData(spe_in) |>
+        as.data.frame() |>
+        select(in_tissue, sample_id, array_row, array_col) |>
+        group_by(sample_id, array_row) |>
+        mutate(
+            edge_col = array_col == min(array_col) | array_col == max(array_col),
+            col_distance = pmin(
+                abs(array_col - min(array_col)),
+                abs(array_col - max(array_col))
+            )
+        ) |>
+        group_by(sample_id, array_col) |>
+        mutate(
+            edge_row = array_row == min(array_row) | array_row == max(array_row),
+            row_distance = pmin(
+                abs(array_row - min(array_row)),
+                abs(array_row - max(array_row))
+            )
+        ) |>
+        group_by(sample_id) |>
+        mutate(
+            edge_spot = edge_row | edge_col,
+            edge_distance = pmin(row_distance, col_distance)
+        )
+
+
+    ## Add Edge info to spe
+    spe$edge_spot <- NA
+    spe$edge_spot[which(spe$in_tissue)] <- spot_coords$edge_spot
+    spe$edge_spot <- factor(spe$edge_spot, levels = c("TRUE", "FALSE"))
+
+    spe$edge_distance <- NA
+    spe$edge_distance[which(spe$in_tissue)] <- spot_coords$edge_distance
+
+    spe$scran_low_lib_size_edge <- NA
+    spe$scran_low_lib_size_edge[which(spe$in_tissue)] <- qcfilter$low_lib_size & spot_coords$edge_spot
+    spe$scran_low_lib_size_edge <- factor(spe$scran_low_lib_size_edge, levels = c("TRUE", "FALSE"))
+
+    return(spe)
+}

tests/testthat/test-add_qc_metrics.R

@@ -0,0 +1,14 @@
+test_that(
+    "add_qc_metrics returns modified spe",
+    {
+        if (!exists("spe")) spe <- fetch_data("spe")
+
+        # run metrics spe
+        spe_qc <- add_qc_metrics(spe)
+        expect_equal(ncol(spe), ncol(spe_qc)) ## same number of spots
+        expect_equal(ncol(colData(spe)) + 7, ncol(colData(spe_qc))) ## add 7 QC cols to colData
+        # [1] "scran_discard"                   "scran_low_lib_size"              "scran_low_n_features"           
+        # [4] "scran_high_subsets_Mito_percent" "edge_spot"                       "edge_distance"                  
+        # [7] "scran_low_lib_size_edge" 
+    }
+)