Bioconductor

Software is an effective way to disseminate statistical methodology. I try to develop R packages for the statistical ideas I publish. Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data. Because genomics is the focus of most of my work, the majority of my software is available through Bioconductor and you can find these software packages by visiting the projects portal where you will find packages for pretty much any genomics data analysis need. Examples of some of the packages my lab has contributed to are:

• alpine: modeling systematic bias to improve quantification from RNA-Seq
• CQN: removing GC-content bias from expression level RNA-Seq data
• rnaseqcomp: implementation of benchmarks for RNA-seq quantification pipelines.
• minfi: Preprocessing and analysis of Illumina methylation arrays
• bumphunter: Software for detecting differentially methylated regions (DMR)
• affy: preprocessing methods for Affymetrix expression arrays, e.g. RMA
• gcrma: sequence based background correction for
• frma: frozen RMA and gene expression barcode
• oligo: preprocessing methods for general applications of arrays
• crlmm: genotype calling and copy number analysis for SNP arrays
• affycomp: engine software for affycomp website

GitHub

Several of my projects, including this website, are developed on GitHub. This includes

• The code for the Introduction to Data Science book
• The code for DALS
• bumphunter
• The code for my Data Science Class
• This webpage
• rafalib

Introduction to Data Science code

All the code used to create the figures, tables and results in the Introduction to Data Science book Data Analysis for the Life Sciences_ book are hosted on GitHub.

Data Analysis for the Life Sciences (DALS) code

All the code used to create the figures, tables and results in the Data Analysis for the Life Sciences book are hosted on GitHub and served off this page,

rafalib

The rafalib package contains a series of shortcuts for routine tasks originally developed to facilitate data exploration. It is available on CRAN. The code is also hosted on GitHub.