wrap around shapes for cases that have more than 25 conditions

R/dimension_reduction.R

@@ -578,7 +578,7 @@ plot_pca <- function(data, design = NULL, plot_colors = NULL, plot_title = TRUE,
                      plot_size = 5, plot_alpha = NULL, plot_labels = FALSE, size_column = NULL,
                      pc_method = "fast_svd", x_pc = 1, y_pc = 2, max_overlaps = 20,
                      num_pc = NULL, expt_names = NULL, label_chars = 10,
-                     cond_column = "condition", batch_column = "batch",
+                     cond_column = "condition", batch_column = "batch", hex = TRUE,
                      ...) {
   arglist <- list(...)
   ## Set default columns in the experimental design for condition and batch
@@ -1036,6 +1036,9 @@ plot_pca <- function(data, design = NULL, plot_colors = NULL, plot_title = TRUE,
   } else {
     ## Leave the title blank.
   }
+
+  ## If want a hexagonal heatmap
+  #if(hexagonal) comp_plot + geom_hex()
 
   ## Finally, return a list of the interesting bits of what happened.
   pca_return <- list(
@@ -1748,11 +1751,34 @@ plot_pcs <- function(pca_data, first = "PC1", second = "PC2", variances = NULL,
         guide = ggplot2::guide_legend(override.aes = list(size = plot_size, fill = "grey")),
         values = 21:25)
   } else if (is.null(size_column) && num_batches > 5) {
+	### cannot mix integer with characters even using list
+	### 19 removed because it looks like 16, which might not be needed since shapes are reused for batches more than 25
+	#symbols = c(0:18,20:25)
+	symbols = c(0:25)
+	if(length(symbols) < num_batches){
+		idx = c(rep(1:length(symbols),
+			    num_batches %/% length(symbols)),
+			    1:(num_batches %% length(symbols)))
+		values = symbols[idx]
+	}else{
+		values = symbols[1:num_batches]
+	}
+
+	## change outline color to black for batches 21+
+	#if(num_batches > 20){
+#		cond20 = unique(pca_data[['condition']])[1:20]
+#		idx = which(pca_data[['condition']] %in% cond20)
+#		othern = nrow(pca_data) - length(idx)
+##	  	outline_col = c(pca_data[['condition']][idx],rep('black',othern))
+#	}else{
+#	  	outline_col = pca_data[['condition']]
+#	}
+
     pca_plot <- pca_plot +
       ggplot2::geom_point(size = plot_size, alpha = plot_alpha,
                           aes(shape = .data[["batch"]],
                               fill = .data[["condition"]],
-                              colour = .data[["condition"]])) +
+                              colour = outline_col)) +
       ggplot2::scale_color_manual(name = "Condition",
                                   ##guide = ggplot2::guide_legend(override.aes = list(size = plot_size)),
                                   guide = "legend",
@@ -1764,7 +1790,7 @@ plot_pcs <- function(pca_data, first = "PC1", second = "PC2", variances = NULL,
       ggplot2::scale_shape_manual(name = "Batch",
                                   labels = levels(as.factor(pca_data[["batch"]])),
                                   guide = ggplot2::guide_legend(overwrite.aes = list(size = plot_size)),
-                                  values = 1:num_batches)
+                                  values = values)
   } else if (!is.null(size_column) && num_batches <= 5) {
     ## This will require the 6 steps above and one more
     pca_plot <- ggplot(data = as.data.frame(pca_data),
@@ -1804,14 +1830,14 @@ plot_pcs <- function(pca_data, first = "PC1", second = "PC2", variances = NULL,
         values = as.numeric(levels(pca_data[["size"]])))
   } else if (!is.null(size_column) && num_batches > 5) {
     pca_plot <- pca_plot +
-      ggplot2::geom_point(alpha = plot_alpha,
+      ggplot2::geom_point(alpha = plot_alpha, 
                           aes(shape = .data[["batch"]],
                               colour = .data[["condition"]],
                               size = .data[["size"]])) +
       ggplot2::scale_shape_manual(name = "Batch",
                                   labels = levels(as.factor(pca_data[["batch"]])),
                                   guide = ggplot2::guide_legend(overwrite.aes = list(size = plot_size)),
-                                  values = 1:num_batches) +
+                                  values = values) +
       ggplot2::scale_color_manual(name = "Condition",
                                   guide = "legend",
                                   values = color_list) +

R/normalize_batch.R

@@ -310,11 +310,11 @@ all_adjusters <- function(input, design = NULL, estimate_type = "sva", batch1 =
       mesg("Attempting fsva surrogate estimation with ",
            chosen_surrogates, " ", sword, ".")
       type_color <- "darkred"
-      sva_object <- sm(sva::sva(log2_mtrx, conditional_model,
-                                null_model, n.sv = chosen_surrogates))
-      surrogate_result <- sva::fsva(log2_mtrx, conditional_model,
+      sva_object <- sink(sm(sva::sva(log2_mtrx, conditional_model,
+                                null_model, n.sv = chosen_surrogates)))
+      surrogate_result <- sink(sva::fsva(log2_mtrx, conditional_model,
                                     sva_object, newdat = as.matrix(log2_mtrx),
-                                    method = "exact")
+                                    method = "exact"))
       model_adjust <- as.matrix(surrogate_result[["newsv"]])
       source_counts <- surrogate_result[["new"]]
     },