1. Low Rank Gaussian Process Regression- Fit a Low Rank Gaussian Process Regression / Linear Mixed Model for large datasets. These models are widely used in statistical genetics as a test of association while correcting for the confounding effects of kinship and population structure.
2. CellMix- Resources for performing gene expression deconvolution in R, i.e. estimating cell proportions and/or cell-specific gene expression from global gene expression measured in heterogeneous samples.
Currently hosted packages: csSAM and CellMix packages.
3. GWAtoolbox- An R-package implemented for genome-wide association studies. The main feature is very vast quality check of data before meta-analysis. It provides an extensive list of quality statistics presented using easy-to-use DHTML and graphical outputs.
Development Status : 5 - Production/Stable [Filter]
4. Genome-wide analysis using MOSS- Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. Includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
7. casper- casper infers alternative splicing from high-throughput sequencing data both for known variants and de novo discovery. We use a Bayesian model with few assumptions, and modern model selection ideas with improved theoretical and computational properties.
8. Genotype Database Framework- GT.DB is a database framework for efficiently managing genome-wide genotype datasets (i.e. 10^6 or more biallelic markers, 1000s of samples), as well as underlying raw data and complex phenotype data, tightly integrated with R libraries for analysis.