DNAMIX: Calculates likelihood ratios as they pertain to mixed DNA samples encountered in forensic science. Written by John Storey.
Eigen-R2: Eigen-R2 is a high-dimensional version of the classic R2 statistic. It can be applied when one wants to determine the aggregate R2 value for many related response variables according to a common set of independent variables. This is a generalization of simply taking the mean R2 values.
EDGE: A comprehensive software package for the significance analysis of DNA microarray experiments -- for both standard and time course experiments -- based on our new optimal discovery procedure and time course methodology. Written by Jeff Leek, Alan Dabney, Eva Monsen, and John Storey.
QVALUE: The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. This software takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values. Written by Alan Dabney and John Storey.
SNM: Supervised normalization of microarray data carried out according to the study design. The study-specific variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.
SVA: It has been shown that genome-wide expression may be affected by environmental, demographic, genetic and technical factors, creating what we call expression heterogeneity. Surrogate variable analysis (SVA) is designed to identify, estimate, and incorporate into an analysis the sources of expression heterogeneity that are not captured by variables included in the model. SVA has been shown to reduce dependence across genes, stablize false discovery rate estimates, and improve reproducibility of analyses. Written by Jeff Leek and John Storey.
TRIGGER: This package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). Includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest.
Undergraduate Guide to R: A very gentle introduction to the R programming language for beginners. This primer contains many examples as well as pointers to more advanced R manuals. It is popular among undergraduates and biologists here at Princeton. Written by Trevor Martin.