Free software and data for public use
The software and data listed here is partially under NSF and NIH support.
Please acknowledge our publications when using our software.

Available Software:

1. Data integration with Group sparse Canonical Correlation Analysis (gsCCA).

Details can be found at:

Dongdong Lin, Ji-Gang Zhang, Li, Vince D. Calhoun, Hong-Wen Deng, Yu-Ping Wang: Group sparse canonical correlation analysis for genomic data integration. BMC Bioinformatics 14 , highly accessed paper, 245 (2013)

2. Imaging and genomic data integration with sparse representation based method

Details can be found at:

H. Cao, J. Duan, D. Lin, YY Shugart, V. Calhoun, and Y. Wang, Sparse Representation Based Biomarker Seleletion for Schizophrenia with Integrated Analysis of fMRI and SNPs, NeuroImage (2014), http://dx.doi.org/10.1016j.neuroimage.2014.01.021

H. Cao, J. Duan, D. Lin, V. Calhoun, and Y. Wang, Integrating fMRI and SNP data for biomarker identification for Schizophrenia with a sparse representation based variable selection method, BMC Medical Genomics, Nov.2013, 6(3):S2, doi:10.1186/1755-8794-6-S3-S2.

3. Multi-color fluorescence in situ image (M-FISH) database

Details can be found at:

J. Li, D. Lin, H. Cao, and Y. Wang, An improved sparse representation model with structural information for Multicolour Fluorescence In-Situ Hybridization (M-FISH) image classification, BMC Systems Biology, 2013, 7(4):S5 doi:10.1186/1752-0509-7-S4-S5

H. Cao, H. Deng, Marilyn Li and, Y. Wang, Classification of Multicolor Fluorescence In-situ Hybridization (M-FISH) Images with Sparse Representation, IEEE Trans. Nanobioscience, 11(2):111-118, Jun. 2012.

4. Copy number variation detection from sequencing data using CNV-TV

Details can be found at:

J. Duan, J.-G. Zhang, H.-W. Deng, and Y.-P. Wang, "CNV-TV: A robust method to discover copy number variation from short sequencing reads," BMC Bioinformatics, highly accessed paper, vol. 14, pp. 1-12, 2013.

Featured at:

Genetic Engineering & Biotechnology News: "CNV Strategies Get a Rethink", Oct 1, 2013

Biocompare: "Never Miss a Variant Again with These Sequence-based CNV Detection Tools",February 25, 2014

5. Copy number variation (CNV) detection from exome sequencing data using a sparse model

Details can be found at:

J. Duan, M. Wan, H.-W. Deng, and Y.-P. Wang, "A sparse model based detection of copy number variations from exome sequencing data," IEEE Trans.

Biomedical Engineering, in review, 2015.

6. Variant identification from genome sequencing data (gsCCA).

Details can be found at:

Shaolong Cao, Huaizhen Qin, Hong-Wen Deng and Yu-Ping Wang, A unified sparse representation for sequence variant identification for complex traits.

Genetic epidemiology, 2014 Dec;38(8):671-9. doi: 10.1002/gepi.21849. Epub 2014 Sep 4.

7. Fine mapping and test for sequence association.

Details can be found at:

Shaolong Cao, Huaizhen Qin, Hong-Wen Deng and Yu-Ping Wang, A unified sparse representation for sequence variant identification for complex traits.

Genetic epidemiology, 2014 Dec;38(8):671-9. doi: 10.1002/gepi.21849. Epub 2014 Sep 4.

8. Joint CCA model for class-specific correlation analysis.

Details can be found at:

Jian Fang, Dongdong Lin, S. Charles Schulz, Zongben Xu, Vince D. Calhoun, Yu-Ping Wang. Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.

Bioinformatics (2016) 32 (22): 3480-3488. DOI: https://doi.org/10.1093/bioinformatics/btw485. Published: 27 July 2016. Article history

9. FDRcorrectedSCCA

Details can be found at:

Alexej Gossmann, Pascal Zille, Vince Calhoun, Yu-Ping Wang. FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics.

Our data is also listed

Neuroimaging Informatics Tools and Resources Clearinghouse is currently a free one-stop-shop collaboratory for science researchers that need resources such as neuroimaging analysis software, publicly available data sets, or computing power.