1. BioConductor - R for Microarray Analysis
Claudio Lottaz
Computational Diagnostics Group Computational Molecular Biology Department Max Planck Institute for Molecular Genetics

2. Overview Introduction File Formats Data structures Analysis methods
16-Apr-17
Overview
Introduction
File Formats
Data structures
Analysis methods
Summary

3. The R-Project S/Splus: commercial statistics software package
Introduction
16-Apr-17
The R-Project
S/Splus: commercial statistics software package
origins in the academic community
now commercialized
serious effort in graphical user interface and the like
R: public domain statistics software package
based on the public roots of S
still compatible with the S language
command-line like user interface

4. BioConductor R is extendable through packages
Introduction
16-Apr-17
BioConductor
R is extendable through packages
packages may be written in R (language)
programming interface to C available
BioConductor is a collection of packages
various contributors
various methods on different types of data
heterogeneous usage

5. File Formats Importing red/green experiments data
16-Apr-17
File Formats
Importing red/green experiments data
intensities from image processing output .spot or .gpr files (Spot or GenePix packages)
Textual information on probes and targets .gal and .gdl files generated by GenePix
Importing Affymetrix data:
reads Affymetrix CEL-files
needs copyright protected CDF-files for interpretation
Exporting tab delimited ASCII-files

6. Red/Green Specific Data Structures
16-Apr-17
Red/Green Specific Data Structures
marrayLayout objects: contain information on
Probes and their locations
House-keeping genes
marrayRaw objects: intensities for a batch of arrays
red/green, Foreground/back ground
information on applied targets
marrayNorm objects: post normalization data
Average log intensities, normalized log ratios
Normalization factors

7. Affymetrix Specific Data Structures
16-Apr-17
Affymetrix Specific Data Structures
Cdf objects: chip description
Cel objects: contains probe data of one chip
Cel.container object: a set of Cel objects
PPSet object: all probes for a particular target
PPSet.container object: a set of PPSet objects
For convenience: Plobs (probe level objects)
contain a Cdf and a Cel-container object
Simple use, less flexible access

8. Common Data Structures
16-Apr-17
Common Data Structures
exprSet objects: hold expression data
matrix of expression data and standard errors
link to phenotype data and gene annotations
geneNames to identify the genes
phenoData objects: hold phenotype/patient data
list of variables for each phenotype
matrix of data: row per case, column per variable
Some packages use their own data structures

9. Utilities Utilities for resampling Aggregators Summary statistics
Data Structures
16-Apr-17
Utilities
Utilities for resampling
Aggregators
e.g. cumulate results in a cross-validation
Summary statistics
Convenient methods for graphical output
histograms, scatter plots, gene location, boxplots...
on various subsets of data

10. Red/Green Specific Analysis
Analysis Methods
16-Apr-17
Red/Green Specific Analysis
Diagnostical plots to find printing, hybridization or scanning artifacts
boxplots, scatter plots and spatial images
Foreground, background, log-ratio...
Normalization (Yang et al. 2001, 2002)
location normalization: local weighted regression, intensity dependent or 2D spatial
Scale normalization: median absolute deviation (MAD)

11. Affymetrix Specific Analysis
Analysis Methods
16-Apr-17
Affymetrix Specific Analysis
Exploring probe level data (package affy) probe names, perfect match/mismatch intensities,...
Normalization (on probe data)
MVA plots for Affymetrix data
Various methods, default is quantile normalization
Determining expression levels
Various methods: Affymetrix (1999), Li&Wong (2001), Irizarry (2002)
Standard errors are determine per expression value

12. Common Analysis Gene filtering: e.g.
Analysis Methods
16-Apr-17
Common Analysis
Gene filtering: e.g.
find high expressed genes
find differentially expressed genes (also more than 2 groups)
Find genes with similar expression patterns to given gene of interest
Receiver operating characteristic (ROC)
Annotation: chromosome location, gene ontology

13. Common Analysis (continued)
Analysis Methods
16-Apr-17
Common Analysis (continued)
Expression density diagnostics
gene-wise compare distributional shapes to find differences between groups
Multiple hypothesis testing
family-wise error rates, false discovery rate
minP and maxT procedures, step-up procedures
based on various statistic (t-, F-, Wilcoxon...)
adjusted p-values for genes declared differentially expressed, obtained through permutation

14. Summary Public domain software, reproducible methods
16-Apr-17
Summary
Public domain software, reproducible methods
Open source, references to publications
Sophisticated methods available
Rather specific input formats needed, license problem on Affymetrix chip description files
Some heterogeneity in implementation
Blurry definition of the R language