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GSE111974.r
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GSE111974.r
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################################################################################
## bastu_GSE111974.r
## Francisco Martínez Picó - [email protected]
################################################################################
# Dataset info avaliable in:
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111974
Sys.info()[c('nodename', 'user')]
rm(list = ls())
R.version.string # 'R version 4.0.3 (2020-10-10)'
# LOAD PACKAGES -----------------------------------------------------------
library(GEOquery)
library(limma)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(ggrepel)
library(sva)
functions_path = '/Users/francisco/Desktop/TFM/functions'
dataset_path = '/Users/francisco/Desktop/TFM/datasets/GSE111974_bastu'
data_path = paste0(dataset_path, '/data')
results_path = '/Users/francisco/Desktop/TFM/datasets/results_sva_2'
source(paste0(functions_path, '/function_plot_PCAscores.r'))
source(paste0(functions_path, '/function_optimal_nclusters.r'))
source(paste0(functions_path, '/function_unsupervised_clustering.r'))
getwd()
setwd(dataset_path)
# READ PLATFORM FILE ------------------------------------------------------
# Platform = Agilent-014850
gpl_name = '/GPL17077.txt'
gpl = read.delim(file = paste0(data_path, gpl_name),
header = T,
sep = '\t',
comment.char = '#',
skip = 1,
quote = '',
stringsAsFactors = F)
dim(gpl) # [1] 50739 16
# READ EXPERIMENTAL DESSIGN -----------------------------------------------
series_name = '/GSE111974_series_matrix.txt'
series_file = paste0(data_path, series_name)
# Read characteristics
con = file(series_file, 'r') # open file
characteristics = c() # prepare empty vector to save data
while(TRUE) {
line = readLines(con, n=1)
if(length(line) == 0) {
break
} else if(startsWith(line, '!Sample_title')) {
titles = unlist(strsplit(line, '\t'))[-1]
titles = gsub('\\\'', '', titles)
} else if(startsWith(line, '!Sample_characteristics')) {
characteristics = c(characteristics, line)
} else if(startsWith(line, '!Sample_geo_accession')) {
accession = unlist(strsplit(line, '\t'))[-1]
accession = gsub('\\\'', '', accession)
}
}
close(con) # closes file
# Now we parse the info:
ed = data.frame(lapply(characteristics, function(x) {
values = unlist(strsplit(x, '\t'))[-1]
values = gsub('\\\'', '', values)
parts = strsplit(values, ': ')
name = parts[[1]][[1]]
values = sapply(parts, function(x) x[2])
out = list()
out[[name]] = values
return(out)
}))
ed = data.table(sample = accession, title = titles, ed)
# To Homogenize between dataframes:
ed = data.frame(sample = ed$sample, condition = ed$title, tissue = ed$tissue)
ed$tissue[ed$tissue == 'Endometrial tissue'] = 'endometrial tissue'
ed$condition[1:24] = 'control'
ed$condition[25:48] = 'RIF'
rownames(ed) = ed$sample
# DOWNLOAD RAW DATA FOR BASTU (GSE111974) ----------------------------------
getGEOSuppFiles('GSE111974', baseDir = './data/')
# if 'error: Timeout of 60 seconds was reached' then 'options(timeout = 300)'.
# Downloaded in '/raw_datasets/altmae_GSE71331/data/GSE71331'
# (Remember to unzip it and prepare paths)
# READ EXPRESSION DATA ----------------------------------------------------
my_files = list.files(path = 'data/GSE111974_RAW', full.names = T)
target_info = data.frame(FileName = my_files,
RIF_CONTROL = ed[,2],
stringsAsFactors = F)
gse111974raw = read.maimages(files = target_info,
source = 'agilent.median',
green.only = T)
# Use View(gse111974raw$E) and check with files that parsing is correct.
# SAVE RAW DATA, ED AND GPL ------------------------------------------------
save(ed, file = paste0(data_path, '/ed_bastu.rda'), version = 2)
save(gpl, file = paste0(data_path, '/gpl4133_bastu.rda'), version = 2)
save(gse111974raw, file = paste0(data_path, '/gse111974raw.rda'), version = 2)
# PRE-NORMALIZATION ANALYSIS ----------------------------------------------
load(paste0(data_path, '/ed_bastu.rda'), verbose = T)
load(paste0(data_path, '/GPL4133_bastu.rda'), verbose = T)
load(paste0(data_path, '/gse111974raw.rda'), verbose = T)
# RAW PLOTS ---------------------------------------------------------------
#### MDplot ####
plotMD(gse111974raw, column = 1,
main = 'MD plot Bastu (GSM3045867): raw control-1')
plotMD(gse111974raw, column = 43,
main = 'MD plot Bastu (GSM3045909): RIF')
# Warning message:
#In plotMD.EListRaw(gse111974raw, column = 1, main = 'MD plot Altmae (GSE414976):
# raw control-1') : NaNs produced
#### Boxplot ####
# boxplot(data.frame(log2(gse111974raw$Eb)), main = 'Green background')
boxplot(data.frame(log2(gse111974raw$E)), main = 'Raw data')
# RAW PCA -----------------------------------------------------------------
pca_raw = prcomp(t(log2(gse111974raw$E) + 1))
var_raw = round(summary(pca_raw)$importance[2, c(1,2)] * 100, 1)
toplot_raw = data.frame(pca_raw$x[, c(1,2)], stringsAsFactors = F)
lim_raw = max(abs(c(min(toplot_raw), max(toplot_raw))))
axis_limits_raw = c(-lim_raw, lim_raw)
# toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
toplot_raw$color = ed$condition
ggplot(data = toplot_raw,
aes_string(x = colnames(toplot_raw)[1], y = colnames(toplot_raw)[2])) +
geom_point(aes(color = color), size = 3) +
# geom_text_repel(label = ed$sample, size = 3) +
scale_color_manual(name = 'RIF', values = c('#D95F02', '#1B9E77')) +
xlab(paste0('PC1', ': ', var_raw[1], '%')) +
ylab(paste0('PC2', ': ', var_raw[2], '%')) +
ggtitle('PCA: Bastu (GSE111974)') +
xlim(axis_limits_raw) + ylim(axis_limits_raw) +
theme_light() +
theme(legend.position = 'bottom',
axis.title = element_text(size = 18),
axis.text = element_text(size = 15),
plot.title = element_text(size = 22, hjust = 0.5),
legend.title = element_blank(),
legend.text = element_text(size = 13))
# OUTLIER DETECTION (ARRAY QUALITY METRICS) -------------------------------
outdir = paste0(dataset_path, '/arrayQuality_report')
# We need to create an bastu_eset objet:
bastu_eset = ExpressionSet(assayData = assayDataNew(exprs = gse111974raw$E))
# Now we can use aqm:
arrayQualityMetrics(expressionset = bastu_eset,
outdir = outdir,
force = TRUE,
do.logtransform = TRUE) # Since data is not processed yet.
# Check index.html file in outdir for results.
# REMOVE OUTLIERS: --------------------------------------------------------
# We decide to remove 4 samples as they are probably outliers: GSM3045908,
# GSM3045909, GSM30459010, GSM3045911
# Remove GSM3045908:
gse111974raw$E = gse111974raw$E[, colnames(gse111974raw$E) != 'data/GSE111974_RAW/GSM3045908_SG12324209_253949427345_S001_GE1_1100_Jul11_1_2']
gse111974raw$Eb = gse111974raw$Eb[, colnames(gse111974raw$Eb) != 'data/GSE111974_RAW/GSM3045908_SG12324209_253949427345_S001_GE1_1100_Jul11_1_2']
gse111974raw$targets = gse111974raw$targets[, colnames(gse111974raw$targets) != 'data/GSE111974_RAW/GSM3045908_SG12324209_253949427345_S001_GE1_1100_Jul11_1_2']
ed = ed[rownames(ed) != 'GSM3045908',]
# Remove GSM3045909:
gse111974raw$E = gse111974raw$E[, colnames(gse111974raw$E) != 'data/GSE111974_RAW/GSM3045909_SG12324209_253949427345_S001_GE1_1100_Jul11_1_3']
gse111974raw$Eb = gse111974raw$Eb[, colnames(gse111974raw$Eb) != 'data/GSE111974_RAW/GSM3045909_SG12324209_253949427345_S001_GE1_1100_Jul11_1_3']
gse111974raw$targets = gse111974raw$targets[, colnames(gse111974raw$targets) != 'data/GSE111974_RAW/GSM3045909_SG12324209_253949427345_S001_GE1_1100_Jul11_1_3']
ed = ed[rownames(ed) != 'GSM3045909',]
# Remove GSM3045910:
gse111974raw$E = gse111974raw$E[, colnames(gse111974raw$E) != 'data/GSE111974_RAW/GSM3045910_SG12324209_253949427345_S001_GE1_1100_Jul11_1_4']
gse111974raw$Eb = gse111974raw$Eb[, colnames(gse111974raw$Eb) != 'data/GSE111974_RAW/GSM3045910_SG12324209_253949427345_S001_GE1_1100_Jul11_1_4']
gse111974raw$targets = gse111974raw$targets[, colnames(gse111974raw$targets) != 'data/GSE111974_RAW/GSM3045910_SG12324209_253949427345_S001_GE1_1100_Jul11_1_4']
ed = ed[rownames(ed) != 'GSM3045910',]
# Remove GSM3045911:
gse111974raw$E = gse111974raw$E[, colnames(gse111974raw$E) != 'data/GSE111974_RAW/GSM3045911_SG12324209_253949427345_S001_GE1_1100_Jul11_2_1']
gse111974raw$Eb = gse111974raw$Eb[, colnames(gse111974raw$Eb) != 'data/GSE111974_RAW/GSM3045911_SG12324209_253949427345_S001_GE1_1100_Jul11_2_1']
gse111974raw$targets = gse111974raw$targets[, colnames(gse111974raw$targets) != 'data/GSE111974_RAW/GSM3045911_SG12324209_253949427345_S001_GE1_1100_Jul11_2_1']
ed = ed[rownames(ed) != 'GSM3045911',]
# Check raw PCA:
pca_raw = prcomp(t(log2(gse111974raw$E) + 1))
var_raw = round(summary(pca_raw)$importance[2, c(1,2)] * 100, 1)
toplot_raw = data.frame(pca_raw$x[, c(1,2)], stringsAsFactors = F)
lim_raw = max(abs(c(min(toplot_raw), max(toplot_raw))))
axis_limits_raw = c(-lim_raw, lim_raw)
# toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
toplot_raw$color = ed$condition
ggplot(data = toplot_raw,
aes_string(x = colnames(toplot_raw)[1], y = colnames(toplot_raw)[2])) +
geom_point(aes(color = color), size = 3) +
geom_text_repel(label = ed$sample, size = 3) +
scale_color_manual(name = 'RIF', values = c('#D95F02', '#1B9E77')) +
xlab(paste0('PC1', ': ', var_raw[1], '%')) +
ylab(paste0('PC2', ': ', var_raw[2], '%')) +
ggtitle('PCA: Bastu (GSE111974)') +
xlim(axis_limits_raw) + ylim(axis_limits_raw) +
theme_light() +
theme(legend.position = 'bottom',
axis.title = element_text(size = 18),
axis.text = element_text(size = 15),
plot.title = element_text(size = 22, hjust = 0.5),
legend.title = element_blank(),
legend.text = element_text(size = 13))
# CORRECTING BACKGROUND ---------------------------------------------------
gse111974 = backgroundCorrect(gse111974raw, method = 'normexp', offset = 50)
# check if offset needed, we use 50 as default
# ANNOTATION --------------------------------------------------------------
eset = as.matrix(gse111974$E) # expression info
rownames(eset) = gse111974$genes$ProbeName
#### Filtering probes: controls and NAs ####
probesInfo = data.frame(gse111974$genes$ProbeName,
gse111974$genes$SystematicName,
gse111974$genes$ControlType,
stringsAsFactors = F)
probesInfo_aux = left_join(x = probesInfo,
y = gpl,
by = c('gse111974.genes.ProbeName' ='ID'))
dim(probesInfo) # 62976 3
dim(probesInfo_aux) # 62976 18
all(probesInfo$gse111974.genes.ProbeName == probesInfo_aux$gse111974.genes.ProbeName)
# Remove controls
indexestorm2 = which(probesInfo_aux$CONTROL_TYPE != FALSE)
# indexestorm2 = probesInfo_aux[probesInfo_aux$CONTROL_TYPE == FALSE,
# 'gse111974.genes.ProbeName']
exprFilt = eset[-indexestorm2, ]
# exprFilt2 = eset[indexestorm2, ]
# Check number of genes
probesInfo2 = probesInfo_aux[probesInfo_aux$CONTROL_TYPE == FALSE,]
all(probesInfo2$gse111974.genes.ProbeName == rownames(exprFilt))
# GROUP PROBESETS INFORMATION ---------------------------------------------
# Condense replicate probes by their average
exprbyprobe = avereps(exprFilt,ID=probesInfo2$gse111974.genes.ProbeName)
which(rownames(exprbyprobe) == '') # 0
which(is.na(rownames(exprbyprobe))) # 0
# GROUP GENES BY PROBESET ID ----------------------------------------------
exprbygene = avereps(exprFilt, ID=probesInfo2$GENE_SYMBOL)
dim(exprbygene) # 32.081 genes
colnames(exprbygene) = ed$sample
all(rownames(ed) == colnames(exprbygene))
# PLOT EXPRESSION BY GENE -------------------------------------------------
#### Raw expression ####
toplot1 = melt(exprbygene)
p1 = ggplot(toplot1, aes(x = Var2, y = value)) +
geom_boxplot() + ggtitle('Gene raw expression data') + xlab('') +
ylab('') + theme_bw() +
theme(plot.title = element_text(size = 35),
legend.position = 'none',
axis.text.y = element_text(size = 25, color = 'darkgrey'),
axis.text.x = element_blank(), axis.ticks.x = element_blank(),
legend.text = element_text(size = 25),
legend.title = element_blank())
# APPLY LOG2 TRANSFORMATION -----------------------------------------------
expr_log2 = log2(exprbygene)
colnames(expr_log2) = ed$sample
#### Plot log2 data ####
toplot2 = melt(expr_log2)
p2 = ggplot(toplot2, aes(x = Var2, y = value)) + geom_boxplot() +
ggtitle('Log2 expression data') + xlab('') + ylab('') + theme_bw() +
theme(plot.title = element_text(size = 35), legend.position = 'none',
axis.text.y = element_text(size = 25, color = 'darkgrey'),
axis.text.x = element_blank(), axis.ticks.x = element_blank(),
legend.text = element_text(size = 25),
legend.title = element_blank())
# QUANTILE NORMALIZATION --------------------------------------------------
# Final step of normalization
dat_time = normalizeBetweenArrays(expr_log2, method = 'quantile')
sum(is.na(rownames(dat_time))) # 0
sum(rownames(dat_time) == '') # 1
which(rownames(dat_time) == '') # 18th row
dat_time = dat_time[-18,] # We remove that row
dim(dat_time) # 32.080 genes
# NORMALIZED PLOTS --------------------------------------------------------
toplot3 = melt(dat_time)
p3 = ggplot(toplot3, aes(x = Var2, y = value)) + geom_boxplot() +
ggtitle('Quantile normalized expression data') + xlab('') + ylab('') +
theme_bw() +
theme(plot.title = element_text(size = 35),
legend.position = 'none',
axis.text.y = element_text(size = 25, color = 'darkgrey'),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.text = element_text(size = 25),
legend.title = element_blank())
#### Normalized MD-plots ####
plotMD(dat_time, column = 1, main = 'MD plot Bastu (GSE111974): RIF-1')
# plotMD(gse111974, column = 43,
# main = 'MD plot Bastu (GSM3045909): RIF')
#### Boxplot ####
boxplot(dat_time, main = 'Normalized data')
# NORMALIZED PCA ----------------------------------------------------------
pca_norm = prcomp(t(dat_time))
#### Components 1 & 2 ####
var_norm = round(summary(pca_norm)$importance[2, c(1,2)] * 100, 1)
toplot_norm = data.frame(pca_norm$x[, c(1,2)], stringsAsFactors = F)
lim_norm = max(abs(c(min(toplot_norm), max(toplot_norm))))
axis_limits_norm = c(-lim_norm, lim_norm)
# toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
toplot_norm$color = ed$condition
ggplot(data = toplot_norm,
aes_string(x = colnames(toplot_norm)[1], y = colnames(toplot_norm)[2])) +
geom_point(aes(color = color), size = 3) +
scale_color_manual(name = 'RIF', values = brewer.pal(n = 5, 'Dark2')) +
xlab(paste0('PC1', ': ', var_norm[1], '%')) +
ylab(paste0('PC2', ': ', var_norm[2], '%')) +
ggtitle('PCA: Bastu (GSE111974)') +
xlim(axis_limits_norm) + ylim(axis_limits_norm) +
theme_light() +
theme(legend.position = 'bottom',
axis.title = element_text(size = 18),
axis.text = element_text(size = 15),
plot.title = element_text(size = 22, hjust = 0.5),
legend.title = element_blank(),
legend.text = element_text(size = 13))
# If want labels add:
# geom_text(label = ed$sample, size = 3)
# SAVE .RDA ---------------------------------------------------------------
save(dat_time, ed, file = paste0(data_path, '/bastu_time.rda'), version = 2)
# CORRECT BATCH EFFECT AND TIME EFFECT ------------------------------------
load(paste0(data_path, '/bastu_time.rda'), verbose = T)
#### Show normalized PCA: ####
plot_PCAscores(dat = dat_time, ed = ed, condition1 = 'condition',
colors = c('#D95F02', '#1B9E77'),
title = 'PCA: Bastu (GSE111974)')
# We'want to remove the unknow batch effect that seems to affect our data
# (probably time-effect)t:
# At the beginning, we used non-supervised clustering as follows:
# (but finally we will use sva):
# Seems a good separation in PCAplot
# pca = prcomp(t(dat))
#
# var = round(summary(pca)$importance[2, c(1,2)] * 100, 1)
# toplot = data.frame(pca$x[, c(1,2)], stringsAsFactors = F)
# lim = max(abs(c(min(toplot), max(toplot))))
#
# axis_limits = c(-lim, lim)
# # toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
# toplot$color = c(rep('control', 24), rep('RIF', 24))
#
# ggplot(data = toplot,
# aes_string(x = colnames(toplot)[1], y = colnames(toplot)[2])) +
# geom_point(aes(color = color), size = 3) +
# scale_color_manual(name = 'RIF', values = brewer.pal(n = 5, 'Dark2')) +
# xlab(paste0('PC1', ': ', var[1], '%')) +
# ylab(paste0('PC2', ': ', var[2], '%')) +
# ggtitle('PCA: Bastu (GSE111974)') +
# xlim(axis_limits) + ylim(axis_limits) +
# theme_light() +
# theme(legend.position = 'bottom',
# axis.title = element_text(size = 18),
# axis.text = element_text(size = 15),
# plot.title = element_text(size = 22, hjust = 0.5),
# legend.title = element_blank(),
# legend.text = element_text(size = 13))
# #### Calculate how many clusters to use ####
# optimal_nclusters(dat = as.matrix(dat_time),
# method = 'silhouette',
# max_nclusters = 5)
#
# # optimal number of clusters = 3
#
# #### Unsupervised clustering ####
# clustering = unsupervised_clustering(dat = dat_time,
# method = 'kmeans',
# nclusters = 3)
# # clustering$cluster_results
# # clustering$cluster_group
# clustering$cluster_plot
#
# ed$cluster = as.character(clustering$cluster_group)
#
# #### Plot normalized PCA with calculated clusters ###
# plot_PCAscores(dat = dat_time, ed = ed,
# condition1 = 'condition', condition2 = 'cluster',
# components = c(1,2), colors = brewer.pal(n = 6, 'Dark2'),
# title = 'PCA: Bastu (GSE111974)')
#
# # pca = prcomp(t(dat))
# #
# # var = round(summary(pca)$importance[2, c(1,2)] * 100, 1)
# # toplot = data.frame(pca$x[, c(1,2)], stringsAsFactors = F)
# # lim = max(abs(c(min(toplot), max(toplot))))
# #
# # axis_limits = c(-lim, lim)
# # # toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
# # toplot$color = c(rep('control', 24), rep('RIF', 24))
# # toplot$cluster = as.character(clustering$cluster_group)
# #
# # ggplot(data = toplot,
# # aes_string(x = colnames(toplot)[1], y = colnames(toplot)[2])) +
# # geom_point(aes(color = color, shape = cluster), size = 3) +
# # scale_color_manual(name = 'RIF', values = brewer.pal(n = 5, 'Dark2')) +
# # xlab(paste0('PC1', ': ', var[1], '%')) +
# # ylab(paste0('PC2', ': ', var[2], '%')) +
# # ggtitle('PCA: Bastu (GSE111974)') +
# # xlim(axis_limits) + ylim(axis_limits) +
# # theme_light() +
# # theme(legend.position = 'bottom',
# # axis.title = element_text(size = 18),
# # axis.text = element_text(size = 15),
# # plot.title = element_text(size = 22, hjust = 0.5),
# # legend.title = element_blank(),
# # legend.text = element_text(size = 13))
#
# #### Remove time effect ####
# mdesign = model.matrix(~ed$condition)
#
# dat = removeBatchEffect(x = dat_time,
# design = mdesign,
# batch = ed$cluster)
#
# plot_PCAscores(dat = dat, ed = ed,
# condition1 = 'condition',
# components = c(1,2), colors = brewer.pal(n = 6, 'Dark2'),
# title = 'PCA: Bastu (GSE111974)')
#
# # pca = prcomp(t(dat))
# #
# # var = round(summary(pca)$importance[2, c(1,2)] * 100, 1)
# # toplot = data.frame(pca$x[, c(1,2)], stringsAsFactors = F)
# # lim = max(abs(c(min(toplot), max(toplot))))
# #
# # axis_limits = c(-lim, lim)
# # # toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
# # toplot$color = c(rep('control', 24), rep('RIF', 24))
# #
# # ggplot(data = toplot,
# # aes_string(x = colnames(toplot)[1], y = colnames(toplot)[2])) +
# # geom_point(aes(color = color), size = 3) +
# # scale_color_manual(name = 'RIF', values = brewer.pal(n = 5, 'Dark2')) +
# # xlab(paste0('PC1', ': ', var[1], '%')) +
# # ylab(paste0('PC2', ': ', var[2], '%')) +
# # ggtitle('PCA: Bastu (GSE111974)') +
# # xlim(axis_limits) + ylim(axis_limits) +
# # theme_light() +
# # theme(legend.position = 'bottom',
# # axis.title = element_text(size = 18),
# # axis.text = element_text(size = 15),
# # plot.title = element_text(size = 22, hjust = 0.5),
# # legend.title = element_blank(),
# # legend.text = element_text(size = 13))
# SVA FOR UNKNOWN BATCH-EFFECT CORRECTION ---------------------------------
mod = model.matrix( ~ 0 + ed$condition)
n = num.sv(dat = dat_time, mod = mod, method = 'leek') # 2
mod0 = model.matrix( ~ 1, data = ed)
svobj = sva(dat = dat_time, mod = mod, mod0 = mod0, n.sv = n)
dat = removeBatchEffect(x = dat_time, covariates = svobj$sv)
plot_PCAscores(dat = dat, ed = ed, condition1 = 'condition',
components = c(1, 2),
colors = c('#D95F02', '#1B9E77'),
title = 'PCA: Bastu (GSE111974)')
# SAVE .RDA ---------------------------------------------------------------
save(dat, ed, file = paste0(results_path, '/bastu.rda'), version = 2)
load(paste0(results_path, '/bastu.rda'), verbose = T)
head(dat)
head(ed)
plot_PCAscores(dat = dat, ed = ed, condition1 = 'condition',
components = c(1, 2),
colors = c('#D95F02', '#1B9E77'),
title = 'PCA: Bastu (GSE111974)')
################################################################################
pca_raw = prcomp(t(dat))
var_raw = round(summary(pca_raw)$importance[2, c(1,2)] * 100, 1)
toplot_raw = data.frame(pca_raw$x[, c(1,2)], stringsAsFactors = F)
lim_raw = max(abs(c(min(toplot_raw), max(toplot_raw))))
axis_limits_raw = c(-lim_raw, lim_raw)
# toplot$color = c(paste0(rep('Control-'), 1:2), paste0(rep('RIF-'), 1:3))
toplot_raw$color = ed$condition
ggplot(data = toplot_raw,
aes_string(x = colnames(toplot_raw)[1], y = colnames(toplot_raw)[2])) +
geom_point(aes(color = color), size = 3) +
geom_text_repel(label = ed$sample, size = 3) +
scale_color_manual(name = 'RIF', values = c('#D95F02', '#1B9E77')) +
xlab(paste0('PC1', ': ', var_raw[1], '%')) +
ylab(paste0('PC2', ': ', var_raw[2], '%')) +
ggtitle('PCA: Bastu (GSE111974)') +
xlim(axis_limits_raw) + ylim(axis_limits_raw) +
theme_light() +
theme(legend.position = 'bottom',
axis.title = element_text(size = 18),
axis.text = element_text(size = 15),
plot.title = element_text(size = 22, hjust = 0.5),
legend.title = element_blank(),
legend.text = element_text(size = 13))