error correct

bioinfocz · Aug 1, 2024 · 09684a7 · 09684a7
1 parent fb6ebfe
commit 09684a7
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Showing 7 changed files with 79 additions and 19 deletions.
diff --git a/R/manual_cell_annotation.R b/R/manual_cell_annotation.R
@@ -165,6 +165,7 @@ meta_heatmap_ploting <-
            mid = "white",
            high = "red",
            spatial = FALSE,
+           dimred = dimred,
            make_cell_plot = FALSE,
            out_dir = NULL) {
     cell_metadata <- metadata(sce)[["clustering_enrichment"]]
@@ -204,25 +205,28 @@ meta_heatmap_ploting <-
     metaDT <- metaDT %>%
       dplyr::group_by(variable) %>%
       dplyr::mutate(zscores_rescaled_per_gene = c(scales::rescale(zscores, to = c(-1, 1))))
-    # print(head(metaDT))
+
     # Calculate means
-    testmain <- metaDT %>%
-      dplyr::group_by(variable, !!!rlang::syms(main_factor)) %>%
-      dplyr::summarise(mean_value = mean(value))
+    main_factor <- clustering
 
+    testmain <- metaDT %>%
+      dplyr::group_by(variable, !!!rlang::syms(clustering)) %>%
+      dplyr::summarise(mean_value = mean(!!!rlang::syms(show_value)))
+
     # Step 2: Define the dfunction
     dfunction <- function(d, col_name1, col_name2, value.var) {
       d %>%
         tidyr::pivot_wider(names_from = {{ col_name2 }}, values_from = {{ value.var }})
     }
 
     # Step 3: Apply dfunction to testmain
-    testmain_matrix <- dfunction(d = testmain, col_name1 = variable, col_name2 = main_factor, value.var = mean_value)
+    testmain_matrix <- dfunction(d = testmain, col_name1 = variable, col_name2 = clustering, value.var = mean_value)
 
     testmain_mat <- as.matrix(testmain_matrix[, -1])
+
     rownames(testmain_mat) <- testmain_matrix$variable
     # for clusters
-    ## this part is ridiculusely redundant...it is just sorting rows and column based on hierarchic clustering!!!!
+    #
     cormatrix <- stats::cor(x = testmain_mat, method = clus_cor_method)
     cordist <- stats::as.dist(1 - cormatrix, diag = T, upper = T)
     corclus <- stats::hclust(d = cordist, method = clus_cluster_method)
@@ -252,7 +256,6 @@ meta_heatmap_ploting <-
     metaDT <- metaDT %>%
       dplyr::mutate(variable = as.character(variable)) # , levels = values_sort_names))
     ##
-    # print(head(metaDT))
 
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::geom_tile(
@@ -327,7 +330,61 @@ meta_heatmap_ploting <-
       anot_plot_out_pdf_file = out_pdf_file,
       anot_plot_out_png_file = out_png_file
     )
-
+
+    p <- plotReducedDim_mod(
+      sce = sce,
+      dimred = dimred,
+      colour_by = glue::glue("manual_annotation_{clustering}"),
+      text_by = glue::glue("manual_annotation_{clustering}"),
+      title = glue::glue("manual_annotation_{clustering}_dimred.pdf"),
+      use_default_ggplot_palette = TRUE,
+      legend_title = "Cluster"
+    )
+
+    out_pdf_file <-
+      fs::path(out_dir,
+               glue::glue("manual_annotation_{clustering}_dimred.pdf"))
+    out_png_file <- out_pdf_file
+    fs::path_ext(out_png_file) <- "png"
+    pl <- tryCatch({
+      scdrake::save_pdf(list(p), out_pdf_file, stop_on_error = TRUE)
+      ggplot2::ggsave(
+        filename = out_png_file,
+        plot = p,
+        device = "png",
+        dpi = 300
+      )
+      pl
+    },
+    error = function(e) {
+      if (stringr::str_detect(e$message, "Viewport has zero dimension")) {
+        cli_alert_warning(
+          str_space(
+            "Error catched: 'Viewport has zero dimension(s)'.",
+            "There are probably too many levels and the legend doesn't fit into the plot.",
+            "Removing the legend before saving the plot image."
+          )
+        )
+        p <- p + theme(legend.position = "none")
+        scdrake::save_pdf(list(p), out_pdf_file)
+        ggplot2::ggsave(
+          filename = out_png_file,
+          plot = p,
+          device = "png",
+          dpi = 150
+        )
+        p
+      }   else {
+        cli::cli_abort(e$message)
+      }
+    })
+    dimred_par <- tibble::tibble(
+      title = as.character(glue::glue("manual_annotation_{clustering}_dimred.pdf")),
+      anot_plot = list(p),
+      anot_plot_out_pdf_file = out_pdf_file,
+      anot_plot_out_png_file = out_png_file
+    )
+    par <- rbind(par, dimred_par)
     if (spatial) {
       man_anot_plot <- visualized_spots(sce,
         cell_color = glue::glue("manual_annotation_{clustering}"),

diff --git a/R/plans_single_sample.R b/R/plans_single_sample.R
@@ -420,6 +420,7 @@ get_norm_clustering_subplan <- function(cfg, cfg_pipeline, cfg_main) {
         show_value = !!cfg$SHOW_VALUE,
         out_dir = !!cfg$NORM_CLUSTERING_OTHER_PLOTS_OUT_DIR,
         spatial = !!cfg$SPATIAL,
+        dimred = !!cfg$HEATMAP_DIMRED,
         make_cell_plot = !!cfg$MAKE_CELL_PLOT
       )
     )

diff --git a/R/single_sample_norm_clustering.R b/R/single_sample_norm_clustering.R
@@ -331,7 +331,7 @@ sce_norm_hvg_fn <- function(sce_norm,
       nfeatures = hvg_selection_value
     )
     svg_ids <- Seurat::SVFInfo(seu_sce_norm, selection.method = "moransi", )
-    svg_ids <- rownames(result[result$moransi.spatially.variable == "TRUE", ])
+    svg_ids <- rownames(svg_ids[svg_ids$moransi.spatially.variable == "TRUE", ])
 
     hvg_ids <- unique(c(hvg_ids, svg_ids))
   }

diff --git a/R/visualization.R b/R/visualization.R
@@ -346,7 +346,7 @@ dimred_plots_clustering_fn <- function(sce_dimred,
       palete <- c(scales::hue_pal()(par$n_clusters))
       p_spat <- visualized_spots(sce_add_colData(sce_dimred, cell_data),
         cell_color = par$sce_column, color_as_factor = F,
-        point_shape = border, cell_color_code = palete, show_legend = F
+        point_shape = "border", cell_color_code = palete, show_legend = F
       )
       p <- cowplot::plot_grid(p, p_spat, ncol = 2, nrow = 1, rel_widths = c(1, 1.5))
     }

diff --git a/inst/Rmd/single_sample/02_norm_clustering.Rmd b/inst/Rmd/single_sample/02_norm_clustering.Rmd
@@ -374,15 +374,16 @@ if (isTRUE(cfg$MANUAL_ANNOTATION)) {
 }
   cat("\n\n")
 if (isTRUE(cfg$SPATIAL)) {
-scdrake::create_a_link(href = drake::readd(plot_annotation)$anot_plot_out_pdf_file[2],text = "**Cell annotation plot**", href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE),do_cat = TRUE)  
+scdrake::create_a_link(href = drake::readd(plot_annotation)$anot_plot_out_pdf_file[3],text = "**Cell annotation plot**", href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE),do_cat = TRUE)  
  cat("\n\n")
- scdrake::create_a_link(href = drake::readd(plot_annotation)$anot_plot_out_pdf_file[3],text = "**Enrichment cells plot**", href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE),do_cat = TRUE)  
+ scdrake::create_a_link(href = drake::readd(plot_annotation)$anot_plot_out_pdf_file[4],text = "**Enrichment cells plot**", href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE),do_cat = TRUE)  
  cat("\n\n")
- 
-scdrake::create_img_link(drake::readd(plot_annotation)$anot_plot_out_pdf_file[1],img_src = drake::readd(plot_annotation)$anot_plot_out_png_file[1],img_width = "500px",href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE))
+} 
+scdrake::create_img_link(drake::readd(plot_annotation)$anot_plot_out_pdf_file[1],img_src = drake::readd(plot_annotation)$anot_plot_out_png_file[1],img_width = "450px",href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE),do_cat = TRUE)
 
 
-}
+scdrake::create_img_link(drake::readd(plot_annotation)$anot_plot_out_pdf_file[2],img_src = drake::readd(plot_annotation)$anot_plot_out_png_file[2],img_width = "450px",href_rel_start = fs::path_dir(cfg$NORM_CLUSTERING_REPORT_HTML_FILE),do_cat = TRUE)
+
 ```
 
 

diff --git a/inst/config/single_sample/01_input_qc.default.yaml b/inst/config/single_sample/01_input_qc.default.yaml
@@ -6,7 +6,7 @@ INPUT_DATA:
   target_name: "target_name"
 
 INPUT_QC_REPORT_RMD_FILE: "Rmd/single_sample/01_input_qc.Rmd"
-#INPUT_QC_REPORT_RMD_FILE: "Rmd/single_saple/01_input_qc_spatial.Rmd"
+#INPUT_QC_REPORT_RMD_FILE: "Rmd/single_sample/01_input_qc_spatial.Rmd"
 ###############################################################################
 
 ### Spatial experiment ########################################################

diff --git a/inst/config/single_sample/02_norm_clustering.default.yaml b/inst/config/single_sample/02_norm_clustering.default.yaml
@@ -104,12 +104,13 @@ CELL_GROUPINGS: null
 
 ### Manual cell annotation signatures #########################################
 MANUAL_ANNOTATION: False
-ANNOTATION_MARKERS: null
+ANNOTATION_MARKERS: "input_data/brain_markers.csv"
 SCALE_ANNOTATION: False
 OVERLAP: 5
-ANNOTATION_CLUSTERING: null
+ANNOTATION_CLUSTERING: "cluster_kmeans_k4"
 SHOW_VALUE: "value"
-MAKE_CELL_PLOT: False
+MAKE_CELL_PLOT: True
+HEATMAP_DIMRED: "umap"
 ###############################################################################
 
 ### Dimensionality reduction plots ############################################