LORS – LOw-Rank Representation and Sparse Regression for eQTL mapping

LORS

:: DESCRIPTION

LORS is a LOw-Rank representation and Sparse regression for eQTL mapping. This algorithm accounts for confounding factors such as unobserved covariates, experimental artifacts, and unknown environmental perturbations

::DEVELOPER

Hongyu Zhao’s Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/ Linux/MacOsX
MatLab

:: DOWNLOAD

LORS

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Feb 17.
Accounting for Non-Genetic Factors by Low-Rank Representation and Sparse Regression for eQTL Mapping.
Yang C, Wang L, Zhang S, Zhao H.

PLA – Piecewise-constant and Low-rank Approximation for Multi-sample aCGH Data Analysis

PLA

:: DESCRIPTION

PLA – Piecewise-constant and Low-rank Approximation for Multi-sample aCGH Data Analysis

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux
MatLab

:: DOWNLOAD

PLA

:: MORE INFORMATION

Citation:

Bioinformatics. 2014 Mar 31. [Epub ahead of print]
Piecewise-constant and low-rank approximation for identification of recurrent copy number variations.
Zhou X1, Liu J, Wan X, Yu W.

LRAcluster 1.0 – Low Rank Approximation based Multi-omics Data Clustering

LRAcluster 1.0

:: DESCRIPTION

LRAcluster is a new method to discover molecular subtypes by detecting the low-dimensional intrinsic space of high-dimensional cancer multi-omics data.

::DEVELOPER

Bioinformatics & Intelligent Information Processing Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R

:: DOWNLOAD

LRAcluster

:: MORE INFORMATION

Citation

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
Wu D, Wang D, Zhang MQ, Gu J.
BMC Genomics. 2015 Dec 1;16(1):1022. doi: 10.1186/s12864-015-2223-8.