Code for ptcRNAi data, and smoGFP quantifications

This file contains scripts for the phenotypic data for ptcRNAi, CG8177, Nhe2 Overexpression, as well as the code for generating the plots for the smoothend GFP quantifications.

setwd("~/Box Sync/Ulmschneider manuscript/final_plots_with_scripts")
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.1.3

library(plyr)

## Warning: package 'plyr' was built under R version 3.1.3

library(reshape2)

## Warning: package 'reshape2' was built under R version 3.1.2

source("commonPlots.R")


norm1= read.csv("CG8177_UASNhe2/160417_normpercentincrease_328rm.csv")

norm2= read.csv("CG8177_UASNhe2/160416_normpercentincrease.csv")
norm1$genotype = factor(norm1$genotype,levels(norm1$genotype)[c(6,2,1,4,5,3)])
norm2$genotype = factor(norm2$genotype,levels(norm2$genotype)[c(6,2,1,4,5,3)])

Making 2 versions of the CG8177 + Nhe2OE data

p1  = ggplot(norm1, aes(x=genotype, y=raw ))  
p2 = ggplot(norm2, aes(genotype, y = raw ))
errorBarCrossHatch(p1)

errorBarCrossHatch(p2)

Making Figure for Nhe2 eya+ or eya-

nhe2=read.csv("Nhe2KD/160418_Nhe2KD_all.csv")
nhe2$name = factor(nhe2$name,levels(nhe2$name)[c(6,3,1,5,2,4)])

Making Nhe2 KD plot

p3 = ggplot(nhe2, aes(x= name, y = freq ))
errorBarCrossHatch(p3) + scale_y_continuous(breaks = seq(from = 0, to =1, by =.1), limits = c(-0.1,1))

Making ptcRNAi with Nhe2overexpression plot

ptc = read.csv("ptcRNAi/ptc_28795_withOE.csv")
ptc$name = factor(ptc$name, levels(ptc$name)[c(1,3,2)])

p4 = ggplot(ptc, aes(x = name, y = freqDouble ))
p4 = p4 + stat_summary(fun.data=mean_sdl, mult = 1, geom= "errorbar", color="black", size = 2, width = .6) + 
    stat_summary(fun.y = mean, geom = "point", colour = "black", pch = "_", size = 25) + 
    geom_dotplot(binaxis='y', stackdir='center', fill = "grey50", colour = "grey50") 
addtheme(p4)

This is for plotting the weak ptc RNAi data

ptcN2 = read.csv("ptcRNAi/ptc28795_withNhe2RNAi.csv")

p5 = ggplot(ptcN2, aes(x= genotype, y = freqPhen)) + stat_summary(fun.data=mean_sdl, mult = 1, geom= "errorbar", color="black", size = 2, width = .3) + 
    stat_summary(fun.y = mean, geom = "point", colour = "black", pch = "_", size = 25) + 
    geom_dotplot(binaxis='y', stackdir='center', fill = "grey50", colour = "grey50")
addtheme(p5)

This is for the chi-squared test to test differences between the weak ptc RNAi line and weak ptcRNAi + Nhe2 knockdown with TRIP RNAi

ptcW = c(82, 165)
ptcN2 = c(74, 131)
tbl = rbind(ptcW, ptcN2)

chisq.test(tbl)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  tbl
## X-squared = 0.2982, df = 1, p-value = 0.585

fisher.test(tbl)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  tbl
## p-value = 0.5516
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.5853005 1.3236238
## sample estimates:
## odds ratio 
##  0.8800226

Making a plot that includes the Nhe2RNAi data

ptcAll = read.csv("ptcRNAi/ptc_28795_withOEandNhe2RNAi.csv")
ptcAll$name = factor(ptcAll$name, levels(ptcAll$name)[c(1,4,3,2)]) 

p5 = ggplot(ptcAll, aes(x= name, y = freqDouble)) 
errorBarCrossHatch(p5)

Making a plot for ptc(strong data )

ptcst = read.csv("ptcRNAi/ptc55685_all.csv")

ptcst$genotype = factor(ptcst$genotype, levels(ptcst$genotype)[c(1,3,4,2)]) 
pSt = ggplot(ptcst, aes(x= genotype, y = totPhen))
errorBarCrossHatch(pSt)

UASsmoGFP data - boxplots Boxplot for UASsmoGFP region quants

d2 = read.csv("smoGFP/160325_region_quants.csv") 
p2 = ggplot(d2, aes(x= region, y = GFP))

p2 + geom_boxplot(aes(fill = region), width = 0.7, lwd = 1.5, fatten = .9, colour = "black") + theme_classic() + theme(axis.text = element_text(size = 15), line = element_line(size = 2, colour = "black")) + scale_fill_grey(start = 1, end = 0.4) + scale_y_continuous(breaks = c(0, 25, 50, 75, 100), limits = c(0,100))

Reading in data for UASsmoGFP by region data

d = read.csv("smoGFP/uasSmoGFP.csv")
d$genotype = factor(d$genotype,levels(d$genotype)[c(1,3,2)])
p = ggplot(d, aes(x = genotype, y = Mean ))

Boxplot for UASsmoGFP

p + geom_boxplot(aes(fill = genotype), width = 0.8, lwd = 1.5, fatten = .9, colour = "black") + theme_classic() + theme(axis.text = element_text(size = 15), line = element_line(size = 2, colour = "black")) + scale_fill_grey(start = 1, end = 0.4) + scale_y_continuous(breaks= c(0, 25, 50, 75, 100), limits = c(0, 100))

## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_boxplot).