[This article was first published on R – DATABIOMICS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The heatmaps are a tool of data visualization broadly widely used with biological data. The concept is to represent a matrix of values as colors where usually is organized by a gradient. We can find a large number of these graphics in scientific articles related with gene expressions, such as microarray or RNA-seq.
In the next example, we are going to represent a dataframe of gene expression values of 20 genes and 20 patients.
# Load of libraries library(reshape2) library(ggplot2) # Labels of rows and columns name_genes <- paste(rep("GEN", 20), LETTERS[1:20], sep="_") # rows name_patients <- paste(rep("PATIENT", 20), seq(1,20,1), sep="_") # columns # Generation of dataframe value_expression <- data.frame(genes = name_genes, matrix(rnorm(400, 2, 1.8),nrow = 20, ncol = 20)) names(value_expression)[2:21] <- name_patients # Melt dataframe df_heatmap <- melt(value_expression, id.vars = "genes") names(df_heatmap)[2:3] <- c("patient", "expression_level") head(df_heatmap) genes patient expression_level 1 GEN_A PATIENT_1 -0.5312459 2 GEN_B PATIENT_1 3.4286519 3 GEN_C PATIENT_1 -3.4287675 4 GEN_D PATIENT_1 2.7277366 5 GEN_E PATIENT_1 4.4684617 6 GEN_F PATIENT_1 2.3539905
Once we have our dataframe (df_heatmap), we can visualize the values with the package ggplot2.
# Elaboration of heatmap (white - steelblue) ggplot(df_heatmap, aes(patient, genes )) + geom_tile(aes(fill = expression_level), color = "white") + scale_fill_gradient(low = "white", high = "steelblue") + ylab("List of genes ") + xlab("List of patients") + theme(legend.title = element_text(size = 10), legend.text = element_text(size = 12), plot.title = element_text(size=16), axis.title=element_text(size=14,face="bold"), axis.text.x = element_text(angle = 90, hjust = 1)) + labs(fill = "Expression level")
# Elaboration of heatmap with custom gradient (red-steelblue) ggplot(df_heatmap, aes(patient, genes )) + geom_tile(aes(fill = expression_level), , color = "white") + scale_fill_gradient(low = "red", high = "steelblue") + ylab("List of genes ") + xlab("List of patients") + theme(legend.title = element_text(size = 10), legend.text = element_text(size = 12), plot.title = element_text(size=16), axis.title=element_text(size=14,face="bold"), axis.text.x = element_text(angle = 90, hjust = 1)) + labs(fill = "Expression level")
# Elaboration of heatmap with custom gradient (red - green) ggplot(df_heatmap, aes(patient, genes )) + geom_tile(aes(fill = expression_level), , color = "white") + scale_fill_gradient(low = "red", high = "green") + ylab("List of genes ") + xlab("List of patients") + theme(legend.title = element_text(size = 10), legend.text = element_text(size = 12), plot.title = element_text(size=16), axis.title=element_text(size=14,face="bold"), axis.text.x = element_text(angle = 90, hjust = 1)) + labs(fill = "Expression level")
To leave a comment for the author, please follow the link and comment on their blog: R – DATABIOMICS.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.