mda14jx_Final Project.ipynb

Kernel: R (R-Project)

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 model

• Check 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 of limma

• 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 the prcomp() wants the genes in the columns

• For 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 command heatmap.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