• The Bioconductor project started in 2001

Goal: make it easier to conduct reproducible consistent analysis of data from new high-throughput biological technologies

• Core maintainers of the Bioconductor website located at Fred Hutchinson Cancer Research Center
• Updated version released biannually coinciding with the release of R
• Many contributed software packages

• Bioconductor software consists of R add-on packages.
• An R package is a structured collection of code (R, C, or other), documentation, and/or data for performing specific types of analyses.
• E.g. affy, limma, sva packages provide implementations of specialized statistical and graphical methods.

• Provide access to statistical and graphical tools for analysis of high-dimensional biological data

1. Microarray analysis
2. High-throughput 'omics' data

• Include comprehensive documentation describing and providing examples for packages
• Packages have associated vignettes that provide examples of how to use functions
• Have additional tools to work with publically available databases and other meta-data

• Bioconductor has adopted a new documentation paradigm, the vignette.
• A vignette is an executable document consisting of a collection of documentation text and code chunks.
• Vignettes form dynamic, integrated, and reproducible statistical documents that can be automatically updated if either data or analyses are changed.
• Vignettes can be generated using the sweave function (or, roxygen2 package)

Lets take a look at the website…

http://bioconductor.org/

Pre-normalization intensity data for a batch of arrays

Probe-level intensity data for a batch of arrays (same CDF)

Processed Affymetrix or spotted array data

Minimum Information About a Microarray Experiment (MIAME) http://fged.org/projects/miame/

The six most critical elements contributing towards MIAME are:

1 - The raw data for each hybridization (e.g., CEL or GPR files)

2 - The final processed (normalized) data for the set of hybridizations in the experiment (study) (e.g., the gene expression data matrix used to draw the conclusions from the study)

3 - The essential sample annotation including experimental factors and their values (e.g., compound and dose in a dose response experiment)

Minimum Information About a Microarray Experiment (MIAME) http://fged.org/projects/miame/

4 - The experimental design including sample data relationships (e.g., which raw data file relates to which sample, which hybridizations are technical, which are biological replicates)

5 - Sufficient annotation of the array (e.g., gene identifiers, genomic coordinates, probe oligonucleotide sequences or reference commercial array catalog number)

6 - The essential laboratory and data processing protocols (e.g., what normalization method has been used to obtain the final processed data)

1. marray: Spotted DNA microarrays.
2. affy: Affymetrix oligonucleotide chips.
3. limma: all, from spotted arrays to Affy to RNA-seq

• Reading in intensity data, diagnostic plots, normalization, computation of expression measures.

• The packages start with very different data structures, but produce similar objects of class ExpressionSet.

• One can then use other Bioconductor and CRAN packages for exploratory data analysis and visualization, differential expression detection