library(heatmaply) library(heatmaplyExamples) library(knitr) knitr::opts_chunk$set( # cache = TRUE, dpi = 60, comment = '#>', tidy = FALSE)
Author: Alan O'Callaghan (alan.b.ocallaghan@gmail.com)
This vignette illustrates the clustering of non-centered breast cancer RNAseq data, similar to the centered data. shown in the main biological data vignette in this package.
pam50_genes <- intersect(pam50_genes, rownames(raw_expression)) raw_pam50_expression <- raw_expression[pam50_genes, ] voomed_pam50_expression <- voomed_expression[pam50_genes, ] log_raw_mat <- log2(raw_pam50_expression + 0.5) heatmaply(t(log_raw_mat), row_side_colors = tcga_brca_clinical, showticklabels = c(TRUE, FALSE), fontsize_col = 7.5, col = gplots::bluered(50), main = 'Pre-normalisation log2 counts, PAM50 genes', plot_method = 'plotly')
heatmaply_cor(cor(log_raw_mat), row_side_colors = tcga_brca_clinical, showticklabels = c(FALSE, FALSE), main = 'Sample-sample correlation based on log2-transformed PAM50 gene expression', plot_method = 'plotly')
heatmaply(t(voomed_pam50_expression), row_side_colors = tcga_brca_clinical, showticklabels = c(TRUE, FALSE), fontsize_col = 7.5, col = gplots::bluered(50), main = 'Normalised log2 CPM, PAM50 genes', plot_method = 'plotly')
heatmaply_cor(cor(voomed_pam50_expression), row_side_colors = tcga_brca_clinical, showticklabels = c(FALSE, FALSE), main = 'Sample-sample correlation based on normalised PAM50 gene expression', plot_method = 'plotly')
sessionInfo()
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