SPREG 2.0 – Regression Analysis of Secondary Phenotype Data in Case-Control Association Studies

SPREG 2.0

:: DESCRIPTION

SPREG is a computer program for performing regression analysis of secondary phenotype data in case-control association studies. Secondary phenotypes are quantitative or qualitative traits other than the case-control status. Because the case-control sample is not a random sample of the general population, standard statistical analysis of secondary phenotype data can yield very misleading results.

::DEVELOPER

Danyu Lin

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux

:: DOWNLOAD

SPREG

:: MORE INFORMATION

Citation

Lin DY, Zeng D. 2009,
Proper analysis of secondary phenotype data in case-control association studies,
Genetic Epidemiology, 33:256-265.

RACER – Regression Analysis of Combinatorial Expression Regulation

RACER

:: DESCRIPTION

RACER fits the mRNA expression as response using as explanatory variables the TF binding signals (TFBS) from ENCODE, CNV, DM, miRNA expression signals from TCGA. Briefly, RACER infers the sample-specific TF/miRNA regulator activities, which are then used as inputs to infer specific TF/miRNA-gene interactions. The two-stage regression circumvents the problem with integrating the non-sample-specific ENCODE TFBS with the sample-specific TCGA measurements.

::DEVELOPER

Yue Li ,The Zhang Lab, University of Toronto

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux
R

:: DOWNLOAD

RACER

:: MORE INFORMATION

Citation:

Li Y, Liang M, Zhang Z (2014)
Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia.
PLoS Comput Biol 10(10): e1003908. doi:10.1371/journal.pcbi.1003908

SuperPC 1.05 – Survival and Regression Analysis for Microarrays

SuperPC 1.05

:: DESCRIPTION

SuperPC, written in the R language, does prediction for a censored survival outcome, or a regression outcome, using the “supervised principal component” approach. It is especially useful when the number of features p is >> n, the number of samples, for example in microarray studies.

::DEVELOPER

Rob Tibshirani

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
R package

:: DOWNLOAD

SuperPC

:: MORE INFORMATION

Citation

PLoS Biol. 2004 Apr;2(4):E108. Epub 2004 Apr 13.
Semi-supervised methods to predict patient survival from gene expression data.
Bair E, Tibshirani R.