Garrido_Maraver_MERCS

Garrido-Maraver et al. 2019

General information

This is a data analysis pipeline linked to a manuscript in review in Biology Open, entitled “Forcing contacts between mitochondria and the endoplasmic reticulum extends lifespan in a Drosophila model of Alzheimer’s disease”. This pipeline relates to data included in Figure 3.

The input files for this analysis pipeline were deposited in ArrayExpress:

Experiment ArrayExpress accession: E-MTAB-8468

Title: mRNA transcription profiling by array of adult flies expressing a artificial construct designed to bring mitochondria and the endoplasmic reticulum in close proximity

The other relevant files are in this GitHub repository, following this link

Load CEL files into R using the oligo package since the old affy package does not work with ST arrays Make sure you run the commands inside the CEL Files folder

Microarray data analysis using R and Bioconductor

Load microarray data for analysis

#Load CEL files into R using the oligo package since the old affy package does not work with ST arrays
#Make sure you run the commands inside the CEL Files folder
library(oligo)
setwd("/Users/miguel/Dropbox/RStudio/Juan/Data/CELFiles")
list = list.files(full.names=TRUE)
data = oligo::read.celfiles(list)
## Reading in : ./Titan_0180_11614MM_A05.CEL
## Reading in : ./Titan_0180_11614MM_A09.CEL
## Reading in : ./Titan_0180_11614MM_B05.CEL
## Reading in : ./Titan_0180_11614MM_B09.CEL
## Reading in : ./Titan_0180_11614MM_C05.CEL
## Reading in : ./Titan_0180_11614MM_C09.CEL
## Reading in : ./Titan_0180_11614MM_D05.CEL
## Reading in : ./Titan_0180_11614MM_D09.CEL
## Reading in : ./Titan_0180_11614MM_E05.CEL
## Reading in : ./Titan_0180_11614MM_E07.CEL
## Reading in : ./Titan_0180_11614MM_F05.CEL
## Reading in : ./Titan_0180_11614MM_F07.CEL
## Reading in : ./Titan_0180_11614MM_G05.CEL
## Reading in : ./Titan_0180_11614MM_G07.CEL
## Reading in : ./Titan_0180_11614MM_H05.CEL
## Reading in : ./Titan_0180_11614MM_H07.CEL
ph = data@phenoData
feat = data@featureData

Load the sample information

#Merge sample metadata from Edinburgh Genomics with Juan's sample information
#Load required libraries
library(readr)
library(tidyr)
library(dplyr)
setwd("/Users/miguel/Dropbox/RStudio/Juan")
metadata_juan <- read.csv("data/11614MM_SampleData_BioOpen.csv")
metadata_edinburghG <- read.csv("data/11614MM_HybSetup_BioOpen.csv")
metadata <- inner_join (metadata_juan,metadata_edinburghG)

ph@data[ ,1] = c("Control_3days_01","Linker_30days_01",
                 "Control_3days_02","Linker_30days_02",
                 "Control_3days_03","Linker_30days_03",
                 "Control_3days_04","Linker_30days_04",
                 "Linker_3days_01","Control_30days_01",
                 "Linker_3days_02","Control_30days_02",
                 "Linker_3days_03","Control_30days_03",
                 "Linker_3days_04","Control_30days_04")

Pset = fitProbeLevelModel(data)

Quality control of microarray data.

Histograms of raw data

#Create histograms of microarray data, using ggplot
library(ggplot2)
pmexp = pm(data)
sampleNames = vector()
logs =  vector()
for (i in 1:16)
{
  sampleNames = c(sampleNames,rep(ph@data[i,1],dim(pmexp)[1]))
  logs = c(logs,log2(pmexp[,i]))
}
logData = data.frame(logInt=logs,sampleName=sampleNames)
dataHist2 = ggplot(logData, aes(logInt, colour = sampleName))
dataHist2 + geom_density()