## ---- echo = FALSE, message = FALSE--------------------------------------
library(heatmaply)
library(heatmaplyExamples)
library(knitr)
knitr::opts_chunk$set(
# cache = TRUE,
dpi = 60,
comment = '#>',
tidy = FALSE)
## ------------------------------------------------------------------------
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')
## ---- fig.width=13, fig.height=10----------------------------------------
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')
## ---- fig.width=13, fig.height=10----------------------------------------
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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