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thesis.R
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rm(list = ls(all = TRUE))
library(ggplot2)
library(stringr)
library(plyr)
library(grid)
library(gridExtra)
library(methods)
library(xtable)
library(knitr)
library(dunn.test)
library(pander)
library(reshape2)
library(data.table)
library(extrafont)
library(Cairo)
library(scales)
library(multcomp)
#font_import(prompt = FALSE)
options(bitmapType="cairo")
# DATA loading and mangling
codedirpath <- dirname(
tryCatch(normalizePath(parent.frame(2)$ofile), # works when using source
error=function(e) # works when using R CMD
normalizePath(unlist(strsplit(commandArgs()[
grep('^--file=', commandArgs())], '='))[2])))
codedirpath <- "/media/dump/writingswork/draftthesis"
datadir <- normalizePath(file.path(codedirpath, "data"))
InvivoOneday <- read.csv(file.path(
datadir, "2012.12.09.1dayTD.csv"),
header = TRUE)
InvivoOnedayNicenames <- read.table(
file.path(datadir, "2012.12.09.1dayTD.csv"),
header = FALSE, sep = ",",
nrows = 1,
stringsAsFactors = FALSE)
InvivoThreeday <- read.csv(
file.path(datadir, "2012.12.12.3daysTD.csv"),
header = TRUE)
InvivoThreedayNicenames <- read.table(
file.path(datadir, "2012.12.12.3daysTD.csv"),
header = FALSE,
sep = ",",
nrows = 1,
stringsAsFactors = FALSE)
InvivoSevenday <- read.csv(
file.path(datadir, "2012.08.23.7daysTD.csv"),
header = TRUE)
InvivoSevendayNicenames <- read.table(
file.path(datadir, "2012.08.23.7daysTD.csv"),
header = FALSE,
sep = ",",
nrows = 1,
stringsAsFactors = FALSE)
InvitroCelldiams <- read.csv(
file.path(datadir, "2012.08.26.celldiameters.csv"),
header = TRUE)
SynthesisInCells <- read.csv(
file.path(datadir, "2014.06.30.protein.synthesis.csv"),
header = TRUE)
colnames(SynthesisInCells)[colnames(SynthesisInCells)=="Condition"] = "treatment"
SynthesisInCells$cell_protein_density_microgram_per_cmsq_normalized_to_first_day <- NA
SynthesisInCells$cell_protein_density_microgram_per_cmsq_normalized_to_vehicle <- NA
DegradationInCells <- read.csv(
file.path(datadir, "2014.06.30.protein.degradation.csv"),
header = TRUE)
colnames(DegradationInCells)[colnames(DegradationInCells)=="Condition"] = "treatment"
DegradationWithInhibitors <- read.csv(
file.path(datadir, "2014.08.31.inhibitors.csv"),
header = TRUE)
DegradationWithInhibitors <-DegradationWithInhibitors[
!(DegradationWithInhibitors$treatment %in% c("VB", "DB", "DTB", "base", "DT", "DTP")), ]
#column additions
for (i in levels(SynthesisInCells$treatment)) {
SynthesisInCells$cell_protein_density_microgram_per_cmsq_normalized_to_first_day[
SynthesisInCells$treatment == as.character(i)] <-
SynthesisInCells$cell_protein_density_microgram_per_cmsq[
SynthesisInCells$treatment == as.character(i)] /
mean(SynthesisInCells$cell_protein_density_microgram_per_cmsq[
(SynthesisInCells$TimeDays == 0 &
SynthesisInCells$treatment == as.character(i))])
}
for (i in 0:3) {
SynthesisInCells$cell_protein_density_microgram_per_cmsq_normalized_to_vehicle[
SynthesisInCells$TimeDays == as.character(i)] <-
SynthesisInCells$cell_protein_density_microgram_per_cmsq[
SynthesisInCells$TimeDays == as.character(i)] /
mean(SynthesisInCells$cell_protein_density_microgram_per_cmsq[
(SynthesisInCells$TimeDays == as.character(i) &
SynthesisInCells$treatment == "V")])
}
#renaming of conditions
levels(InvivoOneday$treatment)[levels(InvivoOneday$treatment)=="A"] <- "C"
levels(InvivoOneday$treatment)[levels(InvivoOneday$treatment)=="E"] <- "D"
levels(InvivoOneday$treatment)[levels(InvivoOneday$treatment)=="W"] <- "V"
levels(InvivoOneday$treatment)[levels(InvivoOneday$treatment)=="S"] <- "T"
levels(InvivoThreeday$treatment)[levels(InvivoThreeday$treatment)=="B"] <- "C"
levels(InvivoThreeday$treatment)[levels(InvivoThreeday$treatment)=="G"] <- "D"
levels(InvivoThreeday$treatment)[levels(InvivoThreeday$treatment)=="X"] <- "V"
levels(InvivoThreeday$treatment)[levels(InvivoThreeday$treatment)=="U"] <- "T"
#frequently used sets of conditions
condsVDC <- c("V", "D", "C")
condsVDTC <- c("V", "D", "T", "C")
condsVDCdiameters <- c("V", "D", "DT")
condsVDCBdiameters <- c("V", "D", "DT", "DU") # DU is higher testosterone in cell diameter
condsVDCmetabolism <- c("V", "DD", "DDTT")
condsVDCAmetabolism <- c("V", "DD", "DDT", "DDTT")
condsVDCABmetabolism <- c("V", "D", "DD", "DDT", "DDTT")
condsVDCinhibitors <- c("V", "D", "DC", "DM", "DTT", "DTTP")
conditionsVDC <- c("Veh", "Dexa", "Comb")
conditionsVDCpresentation <- c("Veh", "Dexa", "Dexa + Testo")
conditionsVDCpresentationwrap <- c("Veh", "Dexa", "Dexa\n&Testo")
conditionsVDTC <- c("Veh", "Dexa", "Testo", "Comb")
conditionsVDCmetabolism <- c("Veh", "1 µM Dexa", "1 µM Dexa\n+ 500 nM T")
conditionsVDCAmetabolism <- c(
"Veh",
"1 µM Dexa",
"1 µM Dexa\n+ 100 nM T",
"1 µM Dexa\n+ 500 nM T")
conditionsVDCABmetabolism <- c(
"Veh",
"100 nM Dexa",
"1 µM Dexa",
"1 µM Dexa\n+ 100 nM T",
"1 µM Dexa\n+ 500 nM T")
conditionsVDCinhibitors <- c(
"Veh",
"100 nM Dexa",
"100 nM Dexa\n+ 25 µM CHQ",
"100 nM Dexa\n+ 5 µM MG132",
"100 nM Dexa\n+ 300 nM T",
"100 nM Dexa\n+ 300 nM T\n+ 50 nM PPP")
contraststhree <- c("V vs D", "V vs DT", "D vs DT")
contrastsfour <- c("V vs D", "V vs T", "D vs T", "V vs DT", "D vs DT", "T vs DT")
VvsDthreeways <- match("V vs D", contraststhree)[[1]]
DvsCthreeways <- match("D vs DT", contraststhree)[[1]]
VvsCthreeways <- match("V vs DT", contraststhree)[[1]]
VvsDfourways <- match("V vs D", contrastsfour)[[1]]
VvsTfourways <- match("V vs T", contrastsfour)[[1]]
DvsCfourways <- match("D vs DT", contrastsfour)[[1]]
#re-leveling
InvivoOneday$treatment <- factor(
InvivoOneday$treatment,
levels = condsVDTC)
InvivoThreeday$treatment <- factor(
InvivoThreeday$treatment,
levels = condsVDTC)
InvivoSevenday$treatment <- factor(
InvivoSevenday$treatment,
levels = condsVDTC)
InvitroCelldiams$treatment <- factor(
InvitroCelldiams$treatment,
levels = condsVDCBdiameters)
#subsetting for convenience
InvivoOnedayCVD <- InvivoOneday[InvivoOneday$treatment %in% condsVDC, ]
InvivoOnedayCVD$treatment <- factor(
InvivoOnedayCVD$treatment,
levels = condsVDC)
InvivoOnedayV <- InvivoOneday[InvivoOneday$treatment == "V", ]
InvivoOnedayD <- InvivoOneday[InvivoOneday$treatment == "D", ]
InvivoOnedayC <- InvivoOneday[InvivoOneday$treatment == "C", ]
InvivoOnedayT <- InvivoOneday[InvivoOneday$treatment == "T", ]
InvivoThreedayCVD <- InvivoThreeday[InvivoThreeday$treatment %in% condsVDC, ]
InvivoThreedayCVD$treatment <- factor(
InvivoThreedayCVD$treatment,
levels = condsVDC)
InvivoThreedayV <- InvivoThreeday[InvivoThreeday$treatment == "V", ]
InvivoThreedayD <- InvivoThreeday[InvivoThreeday$treatment == "D", ]
InvivoThreedayC <- InvivoThreeday[InvivoThreeday$treatment == "C", ]
InvivoThreedayT <- InvivoThreeday[InvivoThreeday$treatment == "T", ]
InvivoSevendayCVD <- InvivoSevenday[InvivoSevenday$treatment %in% condsVDC, ]
InvivoSevendayCVD$treatment <- factor(
InvivoSevendayCVD$treatment,
levels = condsVDC)
InvivoSevendayV <- InvivoSevenday[InvivoSevenday$treatment == "V", ]
InvivoSevendayD <- InvivoSevenday[InvivoSevenday$treatment == "D", ]
InvivoSevendayC <- InvivoSevenday[InvivoSevenday$treatment == "C", ]
InvivoSevendayT <- InvivoSevenday[InvivoSevenday$treatment == "T", ]
InvitroCelldiamsCVD <-InvitroCelldiams[
InvitroCelldiams$treatment %in% condsVDCdiameters , ]
InvitroCelldiamsCVD$treatment <- factor(
InvitroCelldiamsCVD$treatment,
levels = condsVDCdiameters)
SynthesisInCellsVDC <- SynthesisInCells[(
SynthesisInCells$treatment %in% condsVDCmetabolism), ]
SynthesisInCellsVDC$treatment <- factor(
SynthesisInCellsVDC$treatment,
levels = condsVDCmetabolism)
SynthesisInCellsVDCA <- SynthesisInCells[(
SynthesisInCells$treatment %in% condsVDCAmetabolism), ]
SynthesisInCellsVDCA$treatment <- factor(
SynthesisInCellsVDCA$treatment,
levels = condsVDCAmetabolism)
SynthesisInCellsV <- SynthesisInCells[(SynthesisInCells$treatment == "V"), ]
SynthesisInCellsD <- SynthesisInCells[(SynthesisInCells$treatment == "DD"), ]
SynthesisInCellsC <- SynthesisInCells[(SynthesisInCells$treatment == "DDT"), ]
SynthesisInCellsA <- SynthesisInCells[(SynthesisInCells$treatment == "DDTT"), ]
SynthesisInCellsQ <- SynthesisInCells[(SynthesisInCells$treatment == "DDTTP"), ]
DegradationInCellsVDC <- DegradationInCells[(
DegradationInCells$treatment %in% condsVDCmetabolism), ]
DegradationInCellsVDC$treatment <- factor(
DegradationInCellsVDC$treatment,
levels = condsVDCmetabolism)
DegradationInCellsVDCA <- DegradationInCells[(
DegradationInCells$treatment %in% condsVDCAmetabolism), ]
DegradationInCellsVDCA$treatment <- factor(
DegradationInCellsVDCA$treatment,
levels = condsVDCAmetabolism)
DegradationInCellsVDCAB <- DegradationInCells[(
DegradationInCells$treatment %in% condsVDCABmetabolism), ]
DegradationInCellsVDCAB$treatment <- factor(
DegradationInCellsVDCAB$treatment,
levels = condsVDCABmetabolism)
DegradationInCellsV <- DegradationInCells[(DegradationInCells$treatment == "V"), ]
DegradationInCellsD <- DegradationInCells[(DegradationInCells$treatment == "DD"), ]
DegradationInCellsC <- DegradationInCells[(DegradationInCells$treatment == "DDT"), ]
DegradationInCellsA <- DegradationInCells[(DegradationInCells$treatment == "DDTT"), ]
DegradationInCellsQ <- DegradationInCells[(DegradationInCells$treatment == "DDTTP"), ]
Degradation6 <- DegradationInCells[(DegradationInCells$TimeDays == "0") ,]
Degradation24 <- DegradationInCells[(DegradationInCells$TimeDays == "1") ,]
Degradation48 <- DegradationInCells[(DegradationInCells$TimeDays == "2") ,]
Degradation72 <- DegradationInCells[(DegradationInCells$TimeDays == "3") ,]
DegradationWithInhibitors$treatment <- factor(
DegradationWithInhibitors$treatment,
levels = condsVDCinhibitors)
#literal constants
unistar <- sprintf('\u2736')
unidagger <- sprintf('\u2020')
threeemptystrings <- c("", "", "")
threespaces <- c(" ", " ", " ")
threetworowspaces <- c(" \n ", " ", " ")
threeidenticalgroups <- c("a", "a", "a")
#helper maths functions
SEM <- function(x) {
return(sqrt(var(x, na.rm = TRUE) / truecount(x)))
}
longdescription <- function(x) {
return(paste0(
format(truemean(x), digits = 3),
" (",
format(SEM(x), digits = 3),
"; n = ",
truecount(x), ")"))
}
CI95 <- function(x) {
return(qt(.975, df = (truecount(x) - 1)) * SEM(x))
}
sig3 <- function(x) {
return(format(x, digits = 3))
}
semInterval <- function(x) {
lims <- c(
mean(x, na.rm = TRUE) - SEM(x),
mean(x, na.rm = TRUE) + SEM(x))
names(lims) <- c('ymin','ymax')
return(lims)
}
statstringyunderbar <-function(x){
return(min(semInterval(x)))
}
statstringyoverbar <-function(x){
return(max(semInterval(x)))
}
truecount <- function(x) {
return(length(na.omit(x)))
}
truemean <- function(x) {
return(mean(x, na.rm = TRUE))
}
percentgrowth <- function(initial, final){
return(100 * (truemean(final) - truemean(initial)) / truemean(initial))
}
pcramplificationrate <- function(initial, final){
return(2^(truemean(initial) - truemean(final)))
}
hview <- function(htmllines) {
htmlFile <- tempfile(fileext = ".html")
writeLines(htmllines, htmlFile)
rstudio::viewer(htmlFile)
}
rescaledtovehicleasunity <- function(x){
#this function expects a two-column data frame
#one column must be "treatments", and include some "V"
#the other column will be rescaled by multiplication so that the average of V becomes 1
y <- x
y[,2] <- y[,2] / mean(subset(y, treatment == "V")[,2], na.rm = TRUE)
return(y)
}
rescaledtovehicleaszero <- function(x){
#this function expects a two-column data frame
#one column must be "treatments", and include some "V"
#the other column will be rescaled by addition so that the average of V becomes 0
y <- x
y[,2] <- y[,2] - mean(subset(y, treatment == "V")[,2], na.rm = TRUE)
return(y)
}
#GENERIC PLOTTING
textSize <- 11
presentationTextSize <- 12
nicepalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
meaningfulpalette <- c("#444444", "#dd0000", "#00dd00", "#0000dd", "#888800", "#880088", "#008888", "#dddddd")
greypalette <- c("#ffffff", "#222222", "#999999", "#0000dd", "#00dd00", "#dd0000", "#008888", "#888800")
presentationcolors <- c("grey25", "red3", "green3")
blankgrob <- rectGrob(gp = gpar(col = "white"))
stdplottimecourse <- theme_bw() +
theme(text = element_text(size = textSize, color = "black", family = "Liberation Sans Narrow"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size = textSize),
axis.title.y = element_text(size = textSize),
axis.text.x = element_text(size = textSize),
axis.text.y = element_text(size = textSize),
legend.title = element_blank())
presentationplottimecourse <- theme_bw() +
theme(text = element_text(size = presentationTextSize, color = "black", family = "Cabin"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size = presentationTextSize),
axis.title.y = element_text(size = presentationTextSize),
axis.text.x = element_text(size = presentationTextSize),
axis.text.y = element_text(size = presentationTextSize),
legend.title = element_blank(),
legend.background = element_rect(fill = alpha('white', 1)))
stdbarplot <-
theme_bw() +
theme(text = element_text(size = textSize, color = "black", family = "Liberation Sans Narrow"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(color = "black"),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x= element_blank(),
axis.title.y = element_text(size = textSize))
presentationbarplot <-
theme_bw() +
theme(text = element_text(size = presentationTextSize, color = "black", family = "Cabin"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(color = "black"),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.title.y = element_text(size = presentationTextSize * 1.2),
axis.text.x = element_text(size = presentationTextSize * .7),
axis.text.y = element_text(size = presentationTextSize * .7))
annotationastitle <- function(text, x, y) {
return(annotate(geom = "text", label = text, x = x, y = y,
vjust = 1, hjust = 0.5,
size = .45 * textSize,
family = "Liberation Sans Narrow"))
}
annotationastitlepresentation <- function(text, x, y) {
return(annotate(geom = "text", label = text, x = x, y = y,
vjust = 1, hjust = 0.5,
size = .3 * presentationTextSize,
family = "Cabin"))
}
annotationasaxis <- function(texts, xs, y) {
return(
annotate(geom = "text", label = texts[[1]], x = xs[[1]], y = y,
vjust = 1, hjust = 0.5,
size = .2 * textSize,
family = "Liberation Sans Narrow") +
annotate(geom = "text", label = texts[[2]], x = xs[[2]], y = y,
vjust = 1, hjust = 0.5,
size = .2 * textSize,
family = "Liberation Sans Narrow") +
annotate(geom = "text", label = texts[[3]], x = xs[[3]], y = y,
vjust = 1, hjust = 0.5,
size = .2 * textSize,
family = "Liberation Sans Narrow"))
}
threecolumnplot <- function(skinnydataset, ylabel, ylimit, statstrings){
#this function expects a dataframe with two columns, first designating the treatments
completecasesdataset <- skinnydataset[complete.cases(skinnydataset),]
return(
ggplot(completecasesdataset) +
aes_string(x = colnames(completecasesdataset)[1],
y = colnames(completecasesdataset)[2],
fill = colnames(completecasesdataset)[1]) +
stat_summary(fun.y = mean,
geom = "bar",
colour = "black",
show_guide = FALSE) +
stat_summary(geom = 'errorbar',
fun.data = 'semInterval',
width = 0.1) +
stat_summary(geom = "text",
size = textSize * .4,
aes(family = "Liberation Sans Narrow"),
fun.y = statstringyoverbar,
hjust = .5,
vjust = -.6,
label = statstrings) +
ylab(ylabel) +
coord_cartesian(ylim = ylimit) +
scale_fill_manual(values = greypalette) +
stdbarplot)
}
threecolumnplotpresentation <- function(skinnydataset, ylabel, ylimit, statstrings){
#this function expects a dataframe with two columns, first designating the treatments
completecasesdataset <- skinnydataset[complete.cases(skinnydataset),]
return(
ggplot(completecasesdataset) +
aes_string(x = colnames(completecasesdataset)[1],
y = colnames(completecasesdataset)[2],
fill = colnames(completecasesdataset)[1]) +
stat_summary(fun.y = mean,
geom = "bar",
colour = "black",
show_guide = FALSE) +
stat_summary(geom = 'errorbar',
fun.data = 'semInterval',
width = 0.1) +
stat_summary(geom = "text",
size = presentationTextSize * .4,
aes(family = "Cabin"),
fun.y = statstringyoverbar,
hjust = .5,
vjust = -.6,
label = statstrings) +
ylab(ylabel) +
coord_cartesian(ylim = ylimit) +
scale_fill_manual(values = presentationcolors) +
presentationbarplot)
}
threegeneplot <- function(skinnydataset, ylabel, ylimit, statstrings){
# this function expects a dataframe with two columns, first designating the treatments
# second column described Ct(GOI)-Ct(housekeeping gene)
completecasesdataset <- skinnydataset[complete.cases(skinnydataset),]
completecasesdataset[,2] <- completecasesdataset[,2] * (-1)
return(
ggplot(completecasesdataset) +
aes_string(x = colnames(completecasesdataset)[1],
y = colnames(completecasesdataset)[2],
group = colnames(completecasesdataset)[1]) +
stat_summary(geom = "point",
size = 3,
colour = "black",
fun.y = mean,
show_guide = FALSE,
aes_string(shape = colnames(completecasesdataset)[1])) +
stat_summary(geom = "text",
size = textSize * .4,
aes(family = "Liberation Sans Narrow"),
fun.y = statstringyoverbar,
hjust = .5,
vjust = -.6,
label = statstrings) +
stat_summary(geom = 'errorbar',
fun.data = 'semInterval',
width = 0.1,
show_guide = FALSE,
position=position_dodge(.05)) +
ylab(ylabel) +
scale_y_continuous(breaks = (-10:10), labels = prettyNum(2^(-10:10))) +
coord_cartesian(ylim = ylimit) +
scale_shape_manual(values = c(16, 4, 1), labels = conditionsVDC, guide = FALSE) +
stdbarplot)
}
threegeneplotpresentation <- function(skinnydataset, ylabel, ylimit, statstrings){
# this function expects a dataframe with two columns, first designating the treatments
# second column described Ct(GOI)-Ct(housekeeping gene)
completecasesdataset <- skinnydataset[complete.cases(skinnydataset),]
completecasesdataset[,2] <- completecasesdataset[,2] * (-1)
return(
ggplot(completecasesdataset) +
aes_string(x = colnames(completecasesdataset)[1],
y = colnames(completecasesdataset)[2],
group = colnames(completecasesdataset)[1]) +
stat_summary(geom = "point",
size = 3,
fun.y = mean,
show_guide = FALSE,
aes_string(shape = colnames(completecasesdataset)[1],
color = colnames(completecasesdataset)[1])) +
geom_errorbar(stat = "hline",
yintercept = "mean",
width=0.8,
aes(ymax=..y.., ymin=..y.., color = treatment)) +
stat_summary(geom = "text",
size = presentationTextSize * .4,
aes(family = "Cabin"),
fun.y = statstringyoverbar,
hjust = .5,
vjust = -.6,
label = statstrings) +
stat_summary(geom = 'errorbar',
fun.data = 'semInterval',
width = 0.1,
show_guide = FALSE,
position = position_dodge(.05),
aes_string(color = colnames(completecasesdataset)[1])) +
ylab(ylabel) +
scale_y_continuous(breaks = (-10:10), labels = prettyNum(2^(-10:10))) +
coord_cartesian(ylim = ylimit) +
scale_shape_manual(values = c(16, 4, 1), labels = conditionsVDC, guide = FALSE) +
scale_color_manual(values = presentationcolors, labels = conditionsVDC, guide = FALSE) +
presentationbarplot)
}
plotthreetimecourses <- function(skinnydataset, ylimit, ylabel, statsstring){
# This function expects a dataframe with three columns
# - first column designates the treatments
# - second column describes the time at which it was measured
# - the name of the second column will become X axis label
# - third column describes the actual numbers to plot.
# statsstring can be a long vector of strings, showing:
# time1treatment1, time1treatment2, time1treatemnt3, time2treatement1 etc.
completecasesdataset <- skinnydataset[complete.cases(skinnydataset),]
return(
ggplot(completecasesdataset) +
aes_string(x = colnames(completecasesdataset)[2],
y = colnames(completecasesdataset)[3],
group = colnames(completecasesdataset)[1]) +
stat_summary(geom = "point",
size = 3,
fun.y = truemean,
position = position_dodge(.05),
aes_string(shape = colnames(completecasesdataset)[1])) +
stat_summary(geom = "line",
size = .5,
fun.y = truemean,
position = position_dodge(.05)) +
stat_summary(geom = "text",
size = textSize * .4,
aes(family = "Liberation Sans Narrow"),
fun.y = statstringyunderbar,
hjust = -.2,
vjust = .25,
label = statsstring) +
stat_summary(geom = 'errorbar',
fun.data = 'semInterval',
width = 0.2,
show_guide = FALSE,
position=position_dodge(.05)) +
coord_cartesian(ylim = ylimit) +
ylab(ylabel) +
scale_shape_manual(values = c(16, 4, 1), labels = conditionsVDC) +
stdplottimecourse)
}
plotfourtimecourses <- function(skinnydataset, ylimit, ylabel, categories, statsstring){
# This function expects a dataframe with three columns
# - first column designates the treatments
# - second column describes the time at which it was measured
# - the name of the second column will become X axis label
# - third column describes the actual numbers to plot.
# statsstring can be a long vector of strings, showing:
# time1treatment1, time1treatment2, time1treatemnt3, time2treatement1 etc.
completecasesdataset <- skinnydataset[complete.cases(skinnydataset),]
return(
ggplot(completecasesdataset) +
aes_string(x = colnames(completecasesdataset)[2],
y = colnames(completecasesdataset)[3],
group = colnames(completecasesdataset)[1]) +
stat_summary(geom = "point",
size = 3,
fun.y = truemean,
position = position_dodge(.1),
aes_string(shape = "treatment", show_guide = FALSE)) +
stat_summary(geom = "line",
size = .5,
fun.y = truemean,
position = position_dodge(.1), show_guide = FALSE) +
# stat_summary(geom = "text",
# size = textSize * .4,
# aes(family = "Liberation Sans Narrow"),
# fun.y = statstringyunderbar,
# hjust = -.2,
# vjust = .25,
# label = statsstring) +
stat_summary(geom = 'errorbar',
fun.data = 'semInterval',
width = 0.2,
show_guide = FALSE,
position=position_dodge(.1)) +
coord_cartesian(ylim = ylimit) +
ylab(ylabel) +
scale_x_continuous(labels = c("1", "2", "3", "4")) +
xlab("day") +
stdplottimecourse)
}
#PMID12519877
loadPMID12519877 <- function(datadirpath){
df1 <- read.csv(file.path(datadirpath, "PMID12519877", "proteasomeactivity.csv"), header = FALSE, colClasses = c(NA, "NULL", NA))
row.names(df1) <- df1$V1
return(df1)
}
PMID12519877data <- loadPMID12519877(datadir)
#MASS-REPORTING of IN VIVO DATA
reportstats <- function(invivodata, invivocolnames){
myoutput <- ""
invivodatasubset <- invivodata[invivodata$treatment %in% condsVDC, ]
invivodatasubset$treatment <- factor(invivodatasubset$treatment,
levels = condsVDC)
for (i in 1:length(invivocolnames)) {
if (invivocolnames[[i]] %in% c("animal", "TreatmentLong", "treatment")) next
myoutput <- paste0(myoutput,
"# ", invivocolnames[[i]])
myoutput <- paste(myoutput,
pandoc.table.return(setNames(aggregate(invivodata[,i], list(invivodata$treatment), longdescription),
c("Treatment", "Average (SD; n)")),
style = "rmarkdown"))
kwfour <- kruskal.test(as.formula(paste(colnames(invivodata)[i], "~treatment")), data = invivodata)
myoutput <- paste(myoutput,
paste0("Kruskal-Wallis p value for the four-way comparison is ", signif(kwfour$p.value, digits = 3)))
dunnsfour <- dunn.test(invivodata[,i], invivodata$treatment, method = "bonferroni")
dunnsreport <- data.frame(contrastsfour, dunnsfour$P.adjusted)[c(VvsDfourways, DvsCfourways, VvsTfourways), ]
myoutput <- paste(myoutput,
pandoc.table.return(dunnsreport, style = "rmarkdown"))
kwthree <- kruskal.test(as.formula(paste(colnames(invivodata)[i], "~treatment")), data = invivodatasubset)
myoutput <- paste(myoutput,
paste0("Kruskal-Wallis p value for the three-way comparison is ", signif(kwthree$p.value, digits = 3)))
dunnsthree <- dunn.test(invivodatasubset[,i], invivodatasubset$treatment, method = "bonferroni")
dunnsreport <- data.frame(contraststhree, dunnsthree$P.adjusted)[c(VvsDthreeways, DvsCthreeways, VvsCthreeways), ]
meanV <- truemean(subset(invivodata, treatment == "V")[,i])
meanD <- truemean(subset(invivodata, treatment == "D")[,i])
meanC <- truemean(subset(invivodata, treatment == "C")[,i])
dunnsreport <- setNames(cbind(dunnsreport, c(ifelse(meanV > meanD, 'V > D' , 'V < D'),
ifelse(meanD > meanC, 'D > DT' , 'D < DT'),
ifelse(meanV > meanC, 'V > DT' , 'V < DT'))),
c("Comparison", "P value", "Direction"))
myoutput <- paste(myoutput,
pandoc.table.return(dunnsreport, style = "rmarkdown"))
}
return(myoutput)
}
#FIRST PLOT - body weights at sacrifice
plotbodyweightsatsacrifice <- function(){
ylabel <- "body weight (g)"
ylimit <- c(0, 32)
onedayweightstat <- c("a", "b", "a,b")
threedayweightstat <- c("a,b", "a", "b")
sevendayweightstat <- threeidenticalgroups
return(
grid.arrange(
threecolumnplot(InvivoOnedayCVD[, colnames(InvivoOnedayCVD) %in% c("treatment", "day.2.body.weight..g.")],
ylabel,
ylimit,
onedayweightstat) +
annotationastitle("one day", 2, ylimit[[2]]) +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC),
threecolumnplot(InvivoThreedayCVD[, colnames(InvivoThreedayCVD) %in% c("treatment", "day.4.body.weight..g.")],
ylabel,
ylimit,
threedayweightstat) +
annotationastitle("three days", 2, ylimit[[2]]) +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = threeemptystrings),
threecolumnplot(InvivoSevendayCVD[, colnames(InvivoSevendayCVD) %in% c("treatment", "day.8.body.weight..g.")],
ylabel,
ylimit,
sevendayweightstat) +
annotationastitle("seven days", 2, ylimit[[2]]) +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = threeemptystrings),
ncol=3))
}
#SECOND PLOT - body weight time courses
plotbodyweightcourse <- function(){
timeseriescolumns <- c("body.weight.gain.after.1.days..percent.",
"body.weight.gain.after.2.days..percent.",
"body.weight.gain.after.3.days..percent.",
"body.weight.gain.after.4.days..percent.",
"body.weight.gain.after.5.days..percent.",
"body.weight.gain.after.6.days..percent.",
"body.weight.gain.after.7.days..percent.",
"body.weight.gain.after.8.days..percent.")
shortdf <- InvivoSevendayCVD[, colnames(InvivoSevendayCVD) %in% c("treatment", timeseriescolumns)]
body.weight.gain.after.0.days..percent. <- rep(0, dim(shortdf)[1])
statsstars <- c("", "", "",
"", paste0(unidagger, unistar),"",
"", unidagger,"",
"", unidagger,"",
"", unidagger,"",
"", unidagger,"",
"", unidagger,"",
"", paste0(unidagger, unistar),"")
shortdf <- cbind(body.weight.gain.after.0.days..percent., shortdf)
shortdf <- melt(shortdf, id = c("treatment"), value.name = "bodyweight")
colnames(shortdf)[2] <- "day"
setattr(shortdf$day, "levels", 1:8)
topplot <- plotthreetimecourses(shortdf,
c(-7, 10),
"body weight gain (% of pre-treatment)",
statsstars)
shortdf <- InvivoThreedayCVD[, colnames(InvivoThreedayCVD) %in% c("treatment", timeseriescolumns[1:4])]
body.weight.gain.after.0.days..percent. <- rep(0, dim(shortdf)[1])
shortdf <- cbind(body.weight.gain.after.0.days..percent., shortdf)
shortdf <- melt(shortdf, id = c("treatment"), value.name = "bodyweight")
colnames(shortdf)[2] <- "day"
setattr(shortdf$day, "levels", 1:4)
bottomleftplot <- plotthreetimecourses(shortdf,
c(-7, 10),
"body weight gain (% of pre-treatment)",
rep("", 12))
shortdf <- InvivoOnedayCVD[, colnames(InvivoOnedayCVD) %in% c("treatment", timeseriescolumns[1:1])]
body.weight.gain.after.0.days..percent. <- rep(0, dim(shortdf)[1])
shortdf <- cbind(body.weight.gain.after.0.days..percent., shortdf)
shortdf <- melt(shortdf, id = c("treatment"), value.name = "bodyweight")
colnames(shortdf)[2] <- "day"
setattr(shortdf$day, "levels", 1:2)
bottomrightplot <- plotthreetimecourses(shortdf,
c(-7, 10),
"body weight gain (% of pre-treatment)",
rep("", 6))
topplot$layers[[1]]$show_guide = FALSE
bottomleftplot$layers[[1]]$show_guide = FALSE
return(grid.arrange(topplot, arrangeGrob(bottomleftplot, bottomrightplot, ncol=2, widths = c(1.1,1)),
ncol=1))
}
# THIRD PLOT - body mass composition
plotleanfat <- function(){
#alphabetical order
columnnames <- c("fat.mass.gain..g.", "lean.mass.gain..g.", "total.water.gain..g.")
ylabels <- c("fat mass gain (g)", "lean mass loss (g)", "water loss (g)")
ylims <- list(c(0, 4), c(0, 4.5), c(0, 5.2))
statstrings <- list(
#fat1, 3, 7:
threeidenticalgroups,
c("a", "a,b", "b"),
c("a", "a,b", "b"),
#lean1, 3, 7:
threeidenticalgroups,
c("a", "b", "a,b"),
c("a", "b", "a,b"),
#water1, 3, 7:
threeidenticalgroups,
threeidenticalgroups,
threeidenticalgroups)
plotslist <- list()
for (i in 1:3) {
shortdf1 <- InvivoOnedayCVD[, colnames(InvivoOnedayCVD) %in% c("treatment", columnnames[[i]])]
shortdf3 <- InvivoThreedayCVD[, colnames(InvivoThreedayCVD) %in% c("treatment", columnnames[[i]])]
shortdf7 <- InvivoSevendayCVD[, colnames(InvivoSevendayCVD) %in% c("treatment", columnnames[[i]])]
if (columnnames[[i]] != "fat.mass.gain..g.") {
shortdf1[,2] <- shortdf1[, 2] * (-1)
shortdf3[,2] <- shortdf3[, 2] * (-1)
shortdf7[,2] <- shortdf7[, 2] * (-1)
}
plotslist[[i*3-2]] <- threecolumnplot(shortdf1, ylabels[[i]], ylims[[i]], statstrings[[(i*3-2)]])
plotslist[[i*3-1]] <- threecolumnplot(shortdf3, ylabels[[i]], ylims[[i]], statstrings[[(i*3-1)]])
plotslist[[i*3]] <- threecolumnplot(shortdf7, ylabels[[i]], ylims[[i]], statstrings[[(i*3)]])
if (columnnames[[i]] == "total.water.gain..g.") {
plotslist[[i*3-2]] <- plotslist[[i*3-2]] +
annotationastitle("one day", 2, ylims[[i]][[2]])
plotslist[[i*3-1]] <- plotslist[[i*3-1]] +
annotationastitle("three days", 2, ylims[[i]][[2]])
plotslist[[i*3]] <- plotslist[[i*3]] +
annotationastitle("seven days", 2, ylims[[i]][[2]])
}
if (columnnames[[i]] == "lean.mass.gain..g.") {
plotslist[[i*3-2]] <- plotslist[[i*3-2]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
plotslist[[i*3-1]] <- plotslist[[i*3-1]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
plotslist[[i*3]] <- plotslist[[i*3]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
}
}
return(grid.arrange(
plotslist[[7]], plotslist[[8]], plotslist[[9]], # water
plotslist[[1]], plotslist[[2]], plotslist[[3]], # fat
plotslist[[4]], plotslist[[5]], plotslist[[6]], # lean
ncol = 3))
}
# FOURTH PLOT SHOWS five muscles across three time points
plotmuscleweights <- function(){
#alphabetical order
columnnames <- c("gastrocnemius..mg.",
"levator..mg.",
"quadriceps..mg.",
"tibialis..mg.",
"triceps..mg.")
ylabels <- c("gastrocnemius (mg)",
"levator ani (mg)",
"quadriceps (mg)",
"tibialis ant. (mg)",
"triceps br. (mg)")
ylims <- list(c(0, 180),
c(0, 100),
c(0, 225),
c(0, 75),
c(0, 150))
statstrings <- list(
#gastrocnemia1,3,7:
threeidenticalgroups,
c("a", "b", "a,b"),
c("a", "b", "a,b"),
#levators1,3,7:
threeidenticalgroups,
threeidenticalgroups,
c("a", "b", "a,b"),
#quadriceps:
threeidenticalgroups,
threeidenticalgroups,
c("a", "b", "a,b"),
#tibialis:
threeidenticalgroups,
threeidenticalgroups,
threeidenticalgroups,
#triceps:
threeidenticalgroups,
threeidenticalgroups,
c("a", "b", "b")
)
plotslist <- list()
for (i in 1:5){
shortdf1 <- InvivoOnedayCVD[, colnames(InvivoOnedayCVD) %in% c("treatment", columnnames[[i]])]
shortdf3 <- InvivoThreedayCVD[, colnames(InvivoThreedayCVD) %in% c("treatment", columnnames[[i]])]
shortdf7 <- InvivoSevendayCVD[, colnames(InvivoSevendayCVD) %in% c("treatment", columnnames[[i]])]
plotslist[[i*3-2]] <- threecolumnplot(shortdf1, ylabels[[i]], ylims[[i]], statstrings[[(i*3-2)]])
plotslist[[i*3-1]] <- threecolumnplot(shortdf3, ylabels[[i]], ylims[[i]], statstrings[[(i*3-1)]])
plotslist[[i*3]] <- threecolumnplot(shortdf7, ylabels[[i]], ylims[[i]], statstrings[[(i*3)]])
if (columnnames[[i]] == "levator..mg.") {
plotslist[[i*3-2]] <- plotslist[[i*3-2]] +
annotationastitle("one day", 2, ylims[[i]][[2]])
plotslist[[i*3-1]] <- plotslist[[i*3-1]] +
annotationastitle("three days", 2, ylims[[i]][[2]])
plotslist[[i*3]] <- plotslist[[i*3]] +
annotationastitle("seven days", 2, ylims[[i]][[2]])
}
if (columnnames[[i]] == "tibialis..mg.") {
plotslist[[i*3-2]] <- plotslist[[i*3-2]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
plotslist[[i*3-1]] <- plotslist[[i*3-1]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
plotslist[[i*3]] <- plotslist[[i*3]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
}
}
return(grid.arrange(
plotslist[[4]], plotslist[[5]], plotslist[[6]], # levator
plotslist[[7]], plotslist[[8]], plotslist[[9]], # quadriceps
plotslist[[1]], plotslist[[2]], plotslist[[3]], # gastrocnemius
plotslist[[13]], plotslist[[14]], plotslist[[15]], # triceps
plotslist[[10]], plotslist[[11]], plotslist[[12]], # tibialis
ncol = 3))
}
# FIFTH plot shows proteasome activity in three muscles at three time points
plotproteasomeactivity <- function(){
#alphabetical order
columnnames <- c("gastrocnemius.proteasome.activity..rel.u..",
"quadriceps.proteasome.activity..rel.u..",
"triceps.proteasome.activity..rel.u..")
ylabels <- c("gastrocnemius proteasome\nactivity (rel.u.)",
"quadriceps proteasome\nactivity (rel.u.)",
"triceps proteasome\nactivity (rel.u.)")
ylims <- list(c(0, 2),
c(0, 1.8),
c(0, 3))
statstrings <- list(
#gastrocnemius 1, 3, 7:
c("a,b", "a", "b"),
c("a", "b", "b"),
threeidenticalgroups,
#quadriceps:
threeidenticalgroups,
threeidenticalgroups,
threeidenticalgroups,
#triceps:
threeidenticalgroups,
threeidenticalgroups,
threeidenticalgroups)
plotslist <- list()
for (i in 1:3){
shortdf1 <- rescaledtovehicleasunity(
InvivoOnedayCVD[, colnames(InvivoOnedayCVD) %in% c("treatment", columnnames[[i]])])
shortdf3 <- rescaledtovehicleasunity(
InvivoThreedayCVD[, colnames(InvivoThreedayCVD) %in% c("treatment", columnnames[[i]])])
shortdf7 <- rescaledtovehicleasunity(
InvivoSevendayCVD[, colnames(InvivoSevendayCVD) %in% c("treatment", columnnames[[i]])])
plotslist[[i*3-2]] <- threecolumnplot(shortdf1, ylabels[[i]], ylims[[i]], statstrings[[(i*3-2)]])
plotslist[[i*3-1]] <- threecolumnplot(shortdf3, ylabels[[i]], ylims[[i]], statstrings[[(i*3-1)]])
plotslist[[i*3]] <- threecolumnplot(shortdf7, ylabels[[i]], ylims[[i]], statstrings[[(i*3)]])
if (columnnames[[i]] == "quadriceps.proteasome.activity..rel.u..") {
plotslist[[i*3-2]] <- plotslist[[i*3-2]] +
annotationastitle("one day", 2, ylims[[i]][[2]])
plotslist[[i*3-1]] <- plotslist[[i*3-1]] +
annotationastitle("three days", 2, ylims[[i]][[2]])
plotslist[[i*3]] <- plotslist[[i*3]] +
annotationastitle("seven days", 2, ylims[[i]][[2]])
}
if (columnnames[[i]] == "triceps.proteasome.activity..rel.u..") {
plotslist[[i*3-2]] <- plotslist[[i*3-2]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
plotslist[[i*3-1]] <- plotslist[[i*3-1]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
plotslist[[i*3]] <- plotslist[[i*3]] +
theme(axis.text.x = element_text(color = "black")) +
scale_x_discrete(labels = conditionsVDC)
}
}
return(grid.arrange(
#plotslist[[1]], plotslist[[2]], plotslist[[3]], # gastrocnemius
plotslist[[4]], plotslist[[5]], plotslist[[6]], # quadriceps
plotslist[[7]], plotslist[[8]], plotslist[[9]], # triceps
ncol = 3))
}
plotmurf <- function(){
shortdf <- rescaledtovehicleasunity(
InvivoThreedayCVD[, colnames(InvivoThreedayCVD) %in% c("treatment", "quadriceps.MuRF1.protein..normalized.to.GAPDH.")])
murfplot <- threecolumnplot(shortdf, "MuRF-1 protein\n(normalized to GAPDH)", c(0,1.5), c("a", "a", "a"))
return(murfplot)
}
# SIXTH plot shows atrogenes in two muscles at three time points
plotatrogenes <- function(){
#alphabetical order
columnnames <- c(
"gastrocnemius.Ct.Fbxo32....Ct.Gapdh.",
"quadriceps.Ct.Fbxo32....Ct.Gapdh.",
"gastrocnemius.Ct.Trim63....Ct.Gapdh.",
"quadriceps.Ct.Trim63....Ct.Gapdh.")
ylabels <- c(
"gastrocnemius\nFbxo32 mRNA",
"quadriceps\nFbxo32 mRNA",
"gastrocnemius\nTrim63 mRNA",
"quadriceps\nTrim63 mRNA")
ylims <- list(
# gastrocnemius - Fbxo32 / Mafbx: 1, 3, 7:
c(-1, 2.5), c(-1.5, 5.4), c(-2, 2),
# quadriceps - Fbxo32 / Mafbx: 1, 3, 7:
c(-1, 8.5), c(-1.5, 5.4), c(-2, 2),
# gastrocnemius - Trim63 / Murf1: 1, 3, 7: