In WendyLiuLab/Hsieh2016PCR: PCR data analysis for Hsieh 2006

library(plyr)
library(dplyr)
library(gplots)
library(ggplot2)
library(readr)
library(reshape2)

devtools::load_all(".")

df = load_all_cqs() %>%
  fold_vs_gapdh() %>%
  filter(Timepoint == "4h")

by_mouse = df %>%
  group_by(Target, Timepoint, Condition, Mouse) %>%
  summarize(FoldVsGapdh=mean(FoldVsGapdh, na.rm=TRUE)) %>%
  filter(!is.na(FoldVsGapdh))

by_condition = by_mouse %>%
  group_by(Target, Timepoint, Condition) %>%
  summarize(FoldVsGapdh=mean(FoldVsGapdh, na.rm=TRUE))

g = ggplot(by_mouse, aes(Condition, FoldVsGapdh)) +
  facet_wrap(~Target, ncol=2, scales="free") +
  stat_summary(fun.data=mean_se) +
  theme_bw() +
  theme(axis.text.x=element_text(angle=40, hjust=1))
print(g)

g = ggplot(by_mouse, aes(Condition, FoldVsGapdh)) +
  facet_wrap(~Target, ncol=2, scales="free") +
  stat_summary(fun.data=mean_se, geom="bar", width=0.8) +
  stat_summary(fun.data=mean_se, geom="errorbar", width=0.5) +
  labs(y="Fold expression vs Gapdh") +
  theme_bw() +
  theme(axis.text.x=element_text(size=8))
print(g)

for(target in unique(by_mouse$Target)) {
  paste("\n****", target, "\n") %>% cat
  conditions = c("Control", "F", "Fg", "F+Fg")
  fit = aov(FoldVsGapdh~Condition, by_mouse, subset=by_mouse$Target == target & by_mouse$Condition %in% conditions)
  summary(fit) %>% print
  hsd = TukeyHSD(fit)
  print(hsd$Condition[hsd$Condition[,"p adj"] < 0.05,])
}

map_array = acast(by_mouse, Condition~Target, value.var="FoldVsGapdh", subset=.(Timepoint == "4h" & Condition != "L"), fun.aggregate=mean)
heatmap(map_array, scale="col")

my_pca = prcomp(map_array, scale.=TRUE)
biplot(my_pca)

WendyLiuLab/Hsieh2016PCR documentation built on May 9, 2019, 10:57 p.m.