Style code

R/create_cell_colors.R

@@ -21,11 +21,10 @@
 #' @importFrom graphics barplot par
 #' @importFrom grDevices hcl
 #' @importFrom utils head
-create_cell_colors <- function(
-        cell_types = c("Astro", "Micro", "Oligo", "OPC", "Inhib", "Excit"),
-        pallet = c("classic", "gg", "tableau"),
-        split = NA,
-        preview = FALSE) {
+create_cell_colors <- function(cell_types = c("Astro", "Micro", "Oligo", "OPC", "Inhib", "Excit"),
+    pallet = c("classic", "gg", "tableau"),
+    split = NA,
+    preview = FALSE) {
     pallet <- match.arg(pallet)
 
     base_cell_types <- unique(ss(cell_types, pattern = split))

R/fetch_deconvo_data.R

@@ -45,7 +45,7 @@
 #'
 #' ## explore bulk data
 #' rse_gene
-#' 
+#'
 #' ## load example snRNA-seq data
 #' ## A SingleCellExperiment (4.79 MB)
 #' if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
@@ -64,11 +64,10 @@
 #'     file.path(tempdir(), "sce_DLPFC_annotated")
 #' )
 #' }
-fetch_deconvo_data <- function(
-        type = c("rse_gene", "sce", "sce_DLPFC_example"),
-        destdir = tempdir(),
-        eh = ExperimentHub::ExperimentHub(),
-        bfc = BiocFileCache::BiocFileCache()) {
+fetch_deconvo_data <- function(type = c("rse_gene", "sce", "sce_DLPFC_example"),
+    destdir = tempdir(),
+    eh = ExperimentHub::ExperimentHub(),
+    bfc = BiocFileCache::BiocFileCache()) {
     rse_gene <- sce_DLPFC_example <- NULL
 
     ## Choose a type among the valid options

R/findMarkers_1vAll.R

@@ -5,11 +5,11 @@
 #' @param assay_name Name of the assay to use for calculation
 #' @param cellType_col Column name on colData of the sce that denotes the celltype
 #' @param add_symbol Add the gene symbol column to the marker stats table
-#' @param mod String specifying the model used as design in findMarkers. 
-#' Can be `NULL` (default) if there are no blocking terms with uninteresting 
+#' @param mod String specifying the model used as design in findMarkers.
+#' Can be `NULL` (default) if there are no blocking terms with uninteresting
 #' factors as documented at [pairwiseTTests][scran::pairwiseTTests].
 #' @param verbose Boolean choosing to print progress messages or not
-#' @param direction Choice of direction tested as markers, "up" (default), 
+#' @param direction Choice of direction tested as markers, "up" (default),
 #' "any", or "down". Impacts p-values, if "up" genes with logFC < 0 will have
 #'  p.value=1.
 #'
@@ -39,26 +39,26 @@
 #'
 #' ## Get the 1vALL stats for each gene for each cell type defined in `cellType_broad_hc`
 #' marker_stats_1vAll <- findMarkers_1vAll(
-#'  sce = sce_DLPFC_example,
-#'  assay_name = "logcounts",
-#'  cellType_col = "cellType_broad_hc",
-#'  mod = "~BrNum"
+#'     sce = sce_DLPFC_example,
+#'     assay_name = "logcounts",
+#'     cellType_col = "cellType_broad_hc",
+#'     mod = "~BrNum"
 #' )
-#' 
+#'
 #' ## explore output, top markers have high logFC
 #' head(marker_stats_1vAll)
 #'
 #' @importFrom purrr map
 #' @importFrom dplyr mutate
 #' @importFrom scran findMarkers
 #' @importFrom tibble rownames_to_column as_tibble add_column
-findMarkers_1vAll <- function(sce, 
-                              assay_name = "counts", 
-                              cellType_col = "cellType", 
-                              add_symbol = FALSE,
-                              mod = NULL, 
-                              verbose = TRUE,
-                              direction = "up") {
+findMarkers_1vAll <- function(sce,
+    assay_name = "counts",
+    cellType_col = "cellType",
+    add_symbol = FALSE,
+    mod = NULL,
+    verbose = TRUE,
+    direction = "up") {
     # RCMD Fix
     gene <- rank_marker <- cellType.target <- std.logFC <- rowData <- Symbol <- NULL
 
@@ -68,7 +68,7 @@ findMarkers_1vAll <- function(sce,
     ## Traditional t-test with design as in PB'd/limma approach
     pd <- as.data.frame(SummarizedExperiment::colData(sce))
     # message("donor" %in% colnames(pd))
-    
+
     if (verbose) message("Running 1vALL Testing for ", direction, "-regulated genes")
 
     if (!is.null(mod)) {

R/get_mean_ratio.R

@@ -3,14 +3,14 @@
 #' Calculate the mean ratio value and rank for each gene for each cell type in the `sce`
 #' object, to identify effective marker genes for deconvolution.
 #'
-#' Note if a cell type has < 10 cells the MeanRatio results may be unstable. 
+#' Note if a cell type has < 10 cells the MeanRatio results may be unstable.
 #' See rational in OSCA: <https://bioconductor.org/books/3.19/OSCA.multisample/multi-sample-comparisons.html#performing-the-de-analysis>
 #'
 #' @param sce [SummarizedExperiment-class][SummarizedExperiment::SummarizedExperiment-class]
 #' (or any derivative class) object containing single cell/nucleus gene expression data
 #' @param cellType_col A `character(1)` name of the column in the
 #' [colData()][SummarizedExperiment::SummarizedExperiment-class] of `sce` that
-#' denotes the cell type or group of interest. 
+#' denotes the cell type or group of interest.
 #' @param assay_name A `character(1)` specifying the name of the
 #' [assay()][SummarizedExperiment::SummarizedExperiment-class] in the
 #' `sce` object to use to rank expression values. Defaults to `logcounts` since
@@ -28,7 +28,7 @@
 #' * `mean.2nd` is the mean expression of `gene` for `cellType.2nd`.
 #' * `MeanRatio` is the ratio of `mean.target/mean.2nd`.
 #' * `MeanRatio.rank` is the rank of `MeanRatio` for the cell type.
-#' * `MeanRatio.anno` is an annotation of the `MeanRatio` calculation helpful 
+#' * `MeanRatio.anno` is an annotation of the `MeanRatio` calculation helpful
 #' for plotting.
 #' * `gene_ensembl` & `gene_name` optional cols from `rowData(sce)`specified by
 #'  the user to add gene information
@@ -56,22 +56,22 @@
 #' SummarizedExperiment::rowData(sce_DLPFC_example)
 #'
 #' ## specify rowData col names for gene_name and gene_ensembl
-#'  get_mean_ratio(sce_DLPFC_example, 
-#'                 cellType_col = "cellType_broad_hc", 
-#'                 gene_name = "gene_name", 
-#'                 gene_ensembl = "gene_id")
+#' get_mean_ratio(sce_DLPFC_example,
+#'     cellType_col = "cellType_broad_hc",
+#'     gene_name = "gene_name",
+#'     gene_ensembl = "gene_id"
+#' )
 #'
 #' @importFrom dplyr mutate
 #' @importFrom dplyr arrange
 #' @importFrom purrr map
 #' @importFrom purrr map2
 #' @importFrom matrixStats rowMedians
-get_mean_ratio <- function(
-        sce,
-        cellType_col,
-        assay_name = "logcounts",
-        gene_ensembl = NULL,
-        gene_name = NULL) {
+get_mean_ratio <- function(sce,
+    cellType_col,
+    assay_name = "logcounts",
+    gene_ensembl = NULL,
+    gene_name = NULL) {
     # RCMD fix
     cellType.target <- NULL
     cellType <- NULL

R/plot_composition_bar.R

@@ -20,34 +20,33 @@
 #' est_prop_long <- est_prop |>
 #'     tibble::rownames_to_column("RNum") |>
 #'     tidyr::pivot_longer(!RNum, names_to = "cell_type", values_to = "prop") |>
-#'     dplyr::inner_join(pd |> dplyr::select(RNum, Dx)) 
-#'     
+#'     dplyr::inner_join(pd |> dplyr::select(RNum, Dx))
+#'
 #' est_prop_long
 #'
 #' # Create composition bar plots
 #' # Mean composition of all samples
-#' plot_composition_bar(est_prop_long) 
-#' 
+#' plot_composition_bar(est_prop_long)
+#'
 #' # Mean composition by Dx
 #' plot_composition_bar(est_prop_long, x_col = "Dx")
-#' 
-#' # control minimum value of text to add 
+#'
+#' # control minimum value of text to add
 #' plot_composition_bar(est_prop_long, x_col = "Dx", min_prop_text = 0.1)
-#' 
+#'
 #' # plot all samples, then facet by Dx
-#' plot_composition_bar(est_prop_long, x_col = "RNum", add_text = FALSE) + 
-#' ggplot2::facet_wrap(~Dx, scales = "free_x")
+#' plot_composition_bar(est_prop_long, x_col = "RNum", add_text = FALSE) +
+#'     ggplot2::facet_wrap(~Dx, scales = "free_x")
 #'
 #' @importFrom dplyr rename group_by summarise mutate arrange
 #' @importFrom ggplot2 ggplot geom_bar geom_text aes theme element_text
-plot_composition_bar <- function(
-        prop_long,
-        sample_col = "RNum",
-        x_col = "ALL",
-        prop_col = "prop",
-        ct_col = "cell_type",
-        add_text = TRUE,
-        min_prop_text = 0) {
+plot_composition_bar <- function(prop_long,
+    sample_col = "RNum",
+    x_col = "ALL",
+    prop_col = "prop",
+    ct_col = "cell_type",
+    add_text = TRUE,
+    min_prop_text = 0) {
     x_cat <- cell_type <- anno_y <- NULL
 
     # ct_col <- dplyr::enquo(ct_col)
@@ -82,12 +81,11 @@ plot_composition_bar <- function(
 }
 
 
-.get_cat_prop <- function(
-        prop_long,
-        sample_col = "RNum",
-        x_col = "ALL",
-        prop_col = "prop",
-        ct_col = "cell_type") {
+.get_cat_prop <- function(prop_long,
+    sample_col = "RNum",
+    x_col = "ALL",
+    prop_col = "prop",
+    ct_col = "cell_type") {
     cell_type <- prop <- mean_prop <- x_cat <- anno_y <- sum_prop <- n <- NULL
 
     prop_long <- prop_long |>

R/plot_gene_express.R

@@ -37,14 +37,15 @@
 #'
 #' @family expression plotting functions
 #'
-plot_gene_express <- function(sce,
-    genes,
-    assay_name = "logcounts",
-    category = "cellType",
-    color_pal = NULL,
-    title = NULL,
-    plot_points = FALSE,
-    ncol = 2) {
+plot_gene_express <- function(
+        sce,
+        genes,
+        assay_name = "logcounts",
+        category = "cellType",
+        color_pal = NULL,
+        title = NULL,
+        plot_points = FALSE,
+        ncol = 2) {
     stopifnot(any(genes %in% rownames(sce)))
 
     if (!category %in% colnames(colData(sce))) {

R/plot_marker_express.R

@@ -34,18 +34,17 @@
 #' )
 #' @family expression plotting functions
 #' @importFrom ggplot2 ggplot geom_violin geom_text facet_wrap stat_summary
-plot_marker_express <- function(
-        sce,
-        stats,
-        cell_type,
-        n_genes = 4,
-        rank_col = "MeanRatio.rank",
-        anno_col = "MeanRatio.anno",
-        gene_col = "gene",
-        cellType_col = "cellType",
-        color_pal = NULL,
-        plot_points = FALSE,
-        ncol = 2) {
+plot_marker_express <- function(sce,
+    stats,
+    cell_type,
+    n_genes = 4,
+    rank_col = "MeanRatio.rank",
+    anno_col = "MeanRatio.anno",
+    gene_col = "gene",
+    cellType_col = "cellType",
+    color_pal = NULL,
+    plot_points = FALSE,
+    ncol = 2) {
     stopifnot(cellType_col %in% colnames(colData(sce)))
     stopifnot(cell_type %in% sce[[cellType_col]])
     stopifnot(cell_type %in% stats$cellType.target)

R/plot_marker_express_ALL.R

@@ -42,17 +42,16 @@
 #' @importFrom ggplot2 ggplot geom_violin geom_text facet_wrap stat_summary
 #' @importFrom SummarizedExperiment colData
 #' @importFrom purrr map
-plot_marker_express_ALL <- function(
-        sce,
-        stats,
-        pdf_fn = NULL,
-        n_genes = 10,
-        rank_col = "MeanRatio.rank",
-        anno_col = "MeanRatio.anno",
-        gene_col = "gene",
-        cellType_col = "cellType",
-        color_pal = NULL,
-        plot_points = FALSE) {
+plot_marker_express_ALL <- function(sce,
+    stats,
+    pdf_fn = NULL,
+    n_genes = 10,
+    rank_col = "MeanRatio.rank",
+    anno_col = "MeanRatio.anno",
+    gene_col = "gene",
+    cellType_col = "cellType",
+    color_pal = NULL,
+    plot_points = FALSE) {
     stopifnot(cellType_col %in% colnames(colData(sce)))
 
     if (is.factor(sce[[cellType_col]])) {

R/plot_marker_express_List.R

@@ -49,13 +49,14 @@
 #' @family expression plotting functions
 #' @importFrom ggplot2 ggplot geom_violin geom_text facet_wrap stat_summary
 #' @importFrom SummarizedExperiment colData
-plot_marker_express_List <- function(sce,
-    gene_list,
-    pdf_fn = NULL,
-    cellType_col = "cellType",
-    gene_name_col = "gene_name",
-    color_pal = NULL,
-    plot_points = FALSE) {
+plot_marker_express_List <- function(
+        sce,
+        gene_list,
+        pdf_fn = NULL,
+        cellType_col = "cellType",
+        gene_name_col = "gene_name",
+        color_pal = NULL,
+        plot_points = FALSE) {
     stopifnot(cellType_col %in% colnames(colData(sce)))
 
     if (!identical(rownames(sce), SummarizedExperiment::rowData(sce)[[gene_name_col]])) {

README.Rmd

@@ -72,7 +72,6 @@ BiocManager::install("LieberInstitute/DeconvoBuddies")
 suppressMessages({
     library("DeconvoBuddies")
 })
-
 ```
 
 ### Access Datasets
@@ -95,9 +94,11 @@ and plotting functions to quickly visualize the expression of selected genes in
 snRNA-seq data.
 
 ```{r plot_gene_expression, echo=FALSE, warning=FALSE}
-plot_gene_express(sce = sce_DLPFC_example, 
-                  category = "cellType_broad_hc", 
-                  genes = c("GAD2", "CD22"))
+plot_gene_express(
+    sce = sce_DLPFC_example,
+    category = "cellType_broad_hc",
+    genes = c("GAD2", "CD22")
+)
 ```
 
 ### Plot Deconvoltion Cell Type Proportions
@@ -109,12 +110,14 @@ set.seed(123)
 ## pivot data to long format and join with test estimated proportion data
 est_prop_long <- est_prop |>
     tibble::rownames_to_column("RNum") |>
-    tidyr::pivot_longer(!RNum, names_to = "cell_type", values_to = "prop") 
+    tidyr::pivot_longer(!RNum, names_to = "cell_type", values_to = "prop")
 
 ## composition bar plot
-plot_composition_bar(est_prop_long |> 
-                       dplyr::filter(RNum %in% sample(rownames(est_prop), 10)),
-                     x_col = "RNum") 
+plot_composition_bar(
+    est_prop_long |>
+        dplyr::filter(RNum %in% sample(rownames(est_prop), 10)),
+    x_col = "RNum"
+)
 ```
 
 ## Citation

README.md

@@ -61,7 +61,7 @@ proportion data from the human DLPFC.
 ``` r
 ## Access data with fetch_deconvo_data
 sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 12:39:29.443059 loading file /Users/louise.huuki/Library/Caches/org.R-project.R/R/BiocFileCache/58f79a421ca_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-13 11:40:40.459055 loading file /Users/louise.huuki/Library/Caches/org.R-project.R/R/BiocFileCache/58f79a421ca_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 
 ## explore the single cell experiment object
 sce_DLPFC_example