Data and software to accompany the paper: Applying compressed sensing to genome-wide association studies.

Dataset type: Software
Data released on June 06, 2014

Vattikuti S; Lee JJ; Chang CC; Hsu SDH; Chow CC (2014): Data and software to accompany the paper: Applying compressed sensing to genome-wide association studies. GigaScience. http://dx.doi.org/10.5524/100094

DOI10.5524/100094

The aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. Linear regression is employed for this purpose, and in recent years a signal-processing paradigm known as compressed sensing (CS) has coalesced around a particular class of regression techniques. CS is not a method in its own right, but rather a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. The paper shows the applicability of compressed sensing (CS) theory to genome-wide association studies (GWAS), where the purpose is to find trait-associated tagging markers (genetic variants). Analysis scripts are contained in the compressed CS file. Mock data and scripts are found in the compressed GD file. The example scripts found in the CS repository require the GD files to be unpacked in a separate folder. Please look at accompanying readme pdfs for both repositories and annotations in the example scripts before using.

Additional details

Read the peer-reviewed publication(s):


Additional information:

https://github.com/ShashaankV/CS

https://github.com/ShashaankV/GD





File NameSample IDData TypeFile FormatSizeRelease Date 
ScriptTAR114.32 KB2014-06-06
ReadmePDF106.91 KB2014-06-06
Mixed archiveTAR163.59 MB2014-06-06
ReadmePDF91.03 KB2014-06-06
ReadmeTEXT0.81 KB2014-06-06
Displaying 1-5 of 5 File(s).
Date Action
May 1, 2020 CS_readme.pdf : additional file attribute added
May 1, 2020 File size of CS_readme.pdf updated