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To access this count table:
Go to the W:\training\rnaseq\runs\run3_RNAseq\results folder that contains the results from running the nfcore/rnaseq pipeline. The output folders from task 3 look like this:
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The count table can be found in the star_salmon folder. A list of files and folders in the star_salmon folder will look like this:
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a. First create a new folder in H:\workshop\RNAseq . Call it something suitable, such as ‘DE_analysis_workshop’
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We will be using the pretty heatmap package to accomplish this.
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#### 4b5b. Samples heatmap and dendrogram #### # This section plots a heatmap and dendrogram of pairwise relationships between samples. In this way you can see if samples cluster by treatment group. # See here: https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/ # Define annotation column annot_columns <- data.frame(meta$group[meta$group %in% plotgroups]) # Make the row names the sample IDs row.names(annot_columns) <- meta$sample_ID[meta$group %in% plotgroups] colnames(annot_columns) <- "Treatment groups" # Need to factorise it annot_columns[[1]] <- factor(annot_columns[[1]]) # Generate dendrogram and heatmap pheatmap(distance_matrix, color=colorRampPalette(c("white", "#9999FF", "#990000"))(50), cluster_rows = TRUE, show_rownames = TRUE, treeheight_row = 0, treeheight_col = 70, fontsize_col = 12, annotation_names_col = F, annotation_col = annot_columns, filename = paste0("./figures/Pairwise_sample_heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff")) # Notes about heatmap colours. # You can change the colours used in the heatmap itself by changing the colour names (color=colorRampPalette....) # If you want to change the annotation colours, see here: https://zhiganglu.com/post/pheatmap_change_annotation_colors/ |
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This section plots a heatmap and dendrogram of DE gene expression per sample. Expression counts are scaled and centered so that groupwise relationships can be examined.
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#### 5c6c. DE genes heatmaps and dendrograms #### # Make the row names gene symbols. DE_genes <- na.omit(DE_genes) row.names(DE_genes) <- make.unique(DE_genes$SYMBOL) # sort by p-value DE_genes <- DE_genes[order(DE_genes$padj), ] # Pull out normalised counts only siggc <- DE_genes[colnames(DE_genes) %in% colnames(expdata)] # Scale and center each row. This is important to visualise relative differences between groups and not have row-wise colouration dominated by high or low gene expression. xts <- scale(t(siggc)) xtst <- t(xts) # Define annotation column annot_columns <- data.frame(meta$group[meta$group %in% degroups]) # Make the row names the sample IDs row.names(annot_columns) <- meta$sample_ID[meta$group %in% degroups] colnames(annot_columns) <- "Treatment groups" # Need to factorise it annot_columns[[1]] <- factor(annot_columns[[1]]) # Generate dendrogram and heatmap for ALL DE genes pheatmap(xtst, color=colorRampPalette(c("#D55E00", "white", "#0072B2"))(100), annotation_col=annot_columns, annotation_names_col = F, fontsize_col = 12, fontsize_row = 7, labels_row = row.names(siggc), show_rownames = F, filename = paste0("./figures/All_DEG_Heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff")) # Generate dendrogram and heatmap for top 20 DE genes pheatmap(xtst[1:20,], color=colorRampPalette(c("#D55E00", "white", "#0072B2"))(100), annotation_col=annot_columns, annotation_names_col = F, fontsize_col = 14, fontsize_row = 12, labels_row = row.names(siggc), show_rownames = T, filename = paste0("./figures/Top_DEG_Heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff")) # NOTE: you can plot more than 20 top genes by adjusting 'xtst[1:20,]'. If you wanted to plot the top 50 genes you'd change this to 'xtst[1:50,]' |