Softwares
Sigma-P Method for Rare-Variant Analysis
SigmaP is a rare-variant method for detecting disease associations in case-control sequencing studies. The Sigma-P statistic aggregates the effects of multiple variant sites by computing a weighted sum of the log p-values per site. Each site is weighted by the inverse of its expected standard deviation (denoted by sigma) of the number of variants in controls. The method is robust against signal noise introduced by a large number of neutral variants and is effective for handling variants with opposite effects. Download: R Scripts and Sample Data |
Penalized Conditional/Unconditional Logistic Regression - pclogit R package
pclogit is an R package for penalized conditional/unconditional logistic regression using a network-based peanlty for matched/unmatched case-control data with grouped or graph-constrained variables. The algorithm is efficient for fitting the regularization path and for providing selection probabilities of each predictor for the anaylsis of high-dimensional matched/unmatched case-control data. It uses cyclical coordinate descent in a pathwise fashion. Downloads: Manual, pclogit.tar.gz (for Linux/Unix only) |
Rare variants selection - rvsel R package
rvsel is an R package for rare variants selection with sequence data. The most outome-related rare variants are selected within a gene or a genetic region. The selection procedure is based on the power set of the subset of the rare variants. Downloads: Manual, rvsel_0.1.tar.gz (for Linux/Unix only) |
A Network-assisted algorithm for Epigenetic studies - NEpiC R package
We present a network-assisted algorithm, NEpiC, that combines both mean and variance signals in searching for differentially methylated sub-networks using the protein-protein interaction (PPI) network. Download: R Scripts and Sample Data |
A penalized Exponential Tilt Model for Epigenetic studies - pETM R package
We present a penalized Exponential Tilt Model (pETM) using network-based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. Download: R package and manual |
Differentially Methylated Regions (DMRs) detection
Most existing methods developed to identify differentially methylated loci (DML) use mean signals only, and only a few methods were developed to identify DML using both mean and variance signals, while all existing methods to detect differentially methylated regions (DMRs) focus on mean signals only. This R code is for the new DMR detection algorithm we proposed that uses mean and variance combined signals. Download: R Scripts and Sample Data |
The MuTing Algorithm
MutIng algorithm integrates somatic mutation and DNA methylation data of multiple cancers and identifies methylation driver genes (MDGs) that, when mutated, have strong associations with specific methylation changes across cancer types. Download: R Scripts and Sample Data |
The MiAge Calculator
To estimate the number of cell divisions (mitotic age) of a given tissue type between individuals is of great interest as that allows their stratification of prospective cancer risk. Here we introduce the MiAge Calculator, a DNA methylation-based mitotic clock calculator based on a novel statistical method MiAge, designed to quantitatively estimate mitotic age of a tissue of an individual. This R code is for the new DMR detection algorithm we proposed that uses mean and variance combined signals. Download: R Scripts and Sample Data |