library(knitr) knitr::opts_chunk$set( # cache = TRUE, dpi = 60, comment = '#>', tidy = FALSE)
Author: Alan O'Callaghan (alan.b.ocallaghan@gmail.com)
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# Let's load the packages library(heatmaply) library(heatmaplyExamples)
Following normalization, gene expression patterns appear roughly similar. This indicates that relative expression levels have not been altered unduly. Furthermore, slightly increased concordance with the pre-assigned cluster labels is observed in normalized data. Samples appear to cluster based Sample-sample correlation appears to show less concordance with row annotations than clustering based on gene expression. However, the use of different linkage criteria or distance measures may alter the observed clusters.
center_voom_mat <- voomed_pam50_expression - apply(voomed_pam50_expression, 1, median) voom_max <- max(abs(center_voom_mat)) voom_limits <- c(-voom_max, voom_max) heatmaply(t(center_voom_mat), row_side_colors=tcga_brca_clinical, fontsize_col = 7.5, showticklabels = c(TRUE, FALSE), col = cool_warm(50), limits = voom_limits, main = 'Normalised, centred log2 CPM, PAM50 genes', plot_method = 'plotly')
heatmaply_cor(cor(center_voom_mat), row_side_colors = tcga_brca_clinical, showticklabels = c(FALSE, FALSE), main = 'Sample-sample correlation based on centred, normalised PAM50 gene expression', plot_method = 'plotly')
It may be useful when examining expression
heatmaps to identify particularly high or low measures for a single
gene in a group of patients, or a gene which shows unusually high or low variance.
The mouse-over text available in the heatmaply
package allows visual assessment of measures of interest and quick identification
of samples or genes with unusual gene expression patterns.
Similarly, visualizing correlation heatmaps with heatmaply
allows the user to
rapidly identify samples with unusually high or low pairwise correlation.
sessionInfo()
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