markrobinsonuzh
commented
9 months ago Hi @CNicholasMDA,
Just one thing of note is that if you are using plot_clustering_heatmap_wrapper(), then you have a very old version of the cytofWorkflow. So, I suggest to first look here:
And then, for the heatmaps, the function plotMultiHeatmap in the CATALYST package has really a lot of options. But ultimately, if it's difficult to create a heatmap that want with the functions, the thing to do is pull out the summaries that you want and call your favourite heatmap software on those.
Cheers, Mark
CNicholasMDA
Hi, I'm using Workflow for cyTOF, and have a question about generating heatmaps using pheatmap. I ran clustering on a dataset that has two clinical timepoints in it. I want all the timepoints clustered together, but I want to generate separate "cluster vs antibody expression" heatmaps for Timepoint 1 and Timepoint 2 (and in future, with other subgroups defined in the metadata). How do I amend the below script in order to achieve this? Please let me know if you need more or different information. Thanks!
heatmap_clusters.pdf
----plot-clustering-heatmap1 (function)
plot_clustering_heatmap_wrapper <- function(expr, expr01, cell_clustering, color_clusters, cluster_merging = NULL){
Calculate the median expression, if column name starts with number, a “x” will be added to the name of each column and causes error for data matching. Panel renaming is necessary.
expr_median <- data.frame(expr, cell_clustering = cell_clustering) %>% group_by(cell_clustering) %>% summarize_all(funs(median)) expr01_median <- data.frame(expr01, cell_clustering = cell_clustering) %>% group_by(cell_clustering) %>% summarize_all(funs(median))
Sort the cell clusters with hierarchical clustering
clustering_table <- as.numeric(table(cell_clustering)) clustering_prop <- round(clustering_table / sum(clustering_table) * 100, 2)
Calculate cluster frequencies
d <- dist(expr_median[, colnames(expr)], method = "euclidean") cluster_rows <- hclust(d, method = "average")
expr_heat <- as.matrix(expr01_median[, colnames(expr01)]) rownames(expr_heat) <- expr01_median$cell_clustering
Colors for the heatmap
color_heat <- colorRampPalette(rev(brewer.pal(n = 9, name = "RdYlBu")))(100) legend_breaks = seq(from = 0, to = 1, by = 0.2) labels_row <- paste0(expr01_median$cell_clustering, " (", clustering_prop , "%)")
Annotation for the original clusters
annotation_row <- data.frame(Cluster = factor(expr01_median$cell_clustering)) rownames(annotation_row) <- rownames(expr_heat) color_clusters1 <- color_clusters[1:nlevels(annotation_row$Cluster)] names(color_clusters1) <- levels(annotation_row$Cluster) annotation_colors <- list(Cluster = color_clusters1)
Annotation for the merged clusters
if(!is.null(cluster_merging)){ cluster_merging$new_cluster <- factor(cluster_merging$new_cluster) annotation_row$Cluster_merging <- cluster_merging$new_cluster color_clusters2 <- color_clusters[1:nlevels(cluster_merging$new_cluster)] names(color_clusters2) <- levels(cluster_merging$new_cluster) annotation_colors$Cluster_merging <- color_clusters2 }
pheatmap(expr_heat, color = color_heat, cluster_cols = FALSE, cluster_rows = cluster_rows, labels_row = labels_row, display_numbers = TRUE, number_color = "black", fontsize = 8, fontsize_number = 3, legend_breaks = legend_breaks, annotation_row = annotation_row, annotation_colors = annotation_colors) }
heatmap of clusters
pdf(glue("./{out_dir}/heatmap_clusters.pdf")) plot_clustering_heatmap_wrapper(expr = expr[, lineage_markers_ord], expr01 = expr01[, lineage_markers_ord], cell_clustering = cell_clustering1, color_clusters = color_clusters) dev.off()