Release 1.0.0, 12/23/2017
© Copyright 2017, ShenLab at Indiana University School of Medicine
Acknowledgements: NIH R01 LM011360 and NSF IIS-1117335.
Contact: Lei Du (email@example.com) and/or Li Shen (firstname.lastname@example.org)
Question or bug reporting: The HDBIG team (email@example.com)
Recent advances in brain imaging and high throughput genotyping and sequencing techniques enable new approaches to study the influence of genetic variation on brain structure and function. HDBIG is a collection of software tools for high dimensional brain imaging genomics. These tools are designed to perform comprehensive joint analysis of heterogeneous imaging genomics data. HDBIG-SCCA-TLP is an HDBIG toolkit focusing on Sparse Canonical Correlation Analysis (SCCA). The current version includes matlab implementation of the SCCA Model with a Truncated L1-norm Penalty. It can be applied to examine the association between genetic variations and imaging phenotypes. See below for the relevant paper.
· Du L, Liu K, Zhang T, Yao X, Yan J, Risacher SL, Han J, Guo L, Saykin AJ, Shen L, for the Alzheimer’s Disease Neuroimaging Initiative. (2017) A novel SCCA approach via truncated l1-norm and its application to brain imaging genetics. Bioinformatics, 2017 Sep 18. doi: 10.1093/bioinformatics/btx594. [Epub ahead of print]
HDBIG-SCCA-TLP uses GNU General Public License (GPL). The license description is included in the software package. Please review and accept the license before installing HDBIG-SCCA-TLP via any source.
· Available at http://www.iu.edu/~hdbig/SCCA-TLP/
The package “HDBIG-SCCA-TLP-v1.0.0.zip” consists of two subfolders.
· 01_software: Matlab scripts and test data
· 99_license: The license description.