# load libraries
library(tidyverse)
# load data
load('data/rkip_wgcna.rda')
# global variables
figures_dir <- 'manuscript/figures'
# generate figure
df <- read_tsv('data/atlas_expression.tsv')
(df %>%
filter(`Gene Name` %in% c('GAPDH', 'PEBP1')) %>%
select(`Gene Name`, starts_with('GSM')) %>%
group_by(`Gene Name`) %>%
summarise_all(mean) %>%
gather(sample, expr, -`Gene Name`) %>%
left_join(design) %>%
mutate(disease = factor(disease, levels = unique(disease))) %>%
spread(`Gene Name`, expr) %>%
mutate(fc = PEBP1 - GAPDH) %>%
group_by(disease) %>%
summarise(fc = mean(fc)) %>%
mutate(fc = fc/filter(., disease == 'benign prostate tumor') %>% pull(fc)) %>%
ggplot(aes(x = disease, y = fc)) +
geom_col(width = .7) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(x = '', y = 'Relative fold-change\n')) %>%
ggsave(plot = .,
filename = paste(figures_dir, 'pebp1_level.png', sep = '/'),
width = 7, height = 10, units = 'cm')
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