Step 1: Load packages with data from Bioconductor, library(affy) - mas5, rma, library(puma)
Step 2: Load and read data, create affybatch. Annotate with pData.
Step 3: Analysis of gene expression data with different methods and normalisation techniques.
Create eset
Extract gene expression
First diagnostic using density() and boxplot()
Normalisation by log2 if required
Step 4: Diagnostics of the data with plotting techniques
MAPlot
ggplot
boxplot
Step 5: Differential Expression Analysis
For
puma
, combine the data using an bayesian Hierarchical modelCheck the dimension and the
pData()
for the eset of the combined values. Calculate the FC and plot the data with a MA plot using the command ma.plot()MAPlot
use of
limma
for DE analysis. Remember the three core steps oflimma
Step 1: build the design contrast matrix
Step 2: fit the linear model
Step 3: calculate the p-values and FDRs with a empirical Bayes test
Step 6: Visualisation of Data with PCA
perform PCA in R using the command
prcomp()
It needs the traspose command
t()
since the input for theprcomp()
wants the genes in the columnsFor probabilistic PCA you can use
pumaPCA()
Step 7: Hierarchical clustering of DE (Differentially Expressed) genes
To perform this we need to activate a library called
gplots
. We will use the commandheatmap.2()
.We do clustering a the selected genes from our DE analysis this is to search for patterns in of differentially regulatend pathways.
Step 8: Functional/Pathway analysis of DE targets using PANTHER or DAVID