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). | [GitHub link] | 
Neuroimaging Informatics Tools and Resources Clearinghouse
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. | [GitHub link]
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. | [GitHub link]
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. | [GitHub link]
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. | [GitHub link]
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). | [GitHub link]
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. | [GitHub link]
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. | [GitHub link] | Neuroimaging Informatics Tools and Resources Clearinghouse
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. FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics ( FDRcorrectedSCCA). | [GitHub link] | Neuroimaging Informatics Tools and Resources Clearinghouse
Details can be found at:
Alexej Gossmann, Pascal Zille, V. D. Calhoun and Y.P. Wang, FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics, Date of Publication: 13 March 2018, IEEE Trans. Medical Imaging, DOI: 10.1109/TMI.2018.2815583
10. Greedy projected distance correlation (GPDC). | [GitHub link] | Neuroimaging Informatics Tools and Resources Clearinghouse
Details can be found at:
Jian Fang, Chao Xu, Pascal Zille, Dongdong Lin, Hong-Wen Deng, Vince D. Calhoun, and Yu-Ping Wang, Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation, IEEE Transactions on Medical Imaging, Dec.14, 2017, DOI: 10.1109/TMI.2017.2783244
11.Group sorted L1 penalized estimation (grpSLOPE) for feature selection. | [GitHub link]
Details can be found at:
Alexej Gossmann, Shaolong Cao, Damian Brzyski, Lan-Juan Zhao, Hong-Wen Deng, Yu-Ping Wang, A sparse regression method for group-wise feature selection with false discovery rate control. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, Online ISSN: 1557-9964, Digital Object Identifier: 10.1109/TCBB.2017.2780106
12. Influence Function and Robust Variant of Kernel Canonical Correlation Analysis (RKUM). | [GitHub link]
Details can be found at:
Ashad Alam, Kenji Fukumizu, Yu-Ping Wang, Influence Function and Robust Variant of Kernel Canonical Correlation Analysis, Neurocomputing, Volume 304, 23 August 2018, Pages 12-29, https://doi.org/10.1016/j.neucom.2018.04.008
Ashad Alam, Vince D. Calhoun, Yu-Ping Wang, Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics, Computational Statistics & Data Analysis, September 2018, Volume 125, Pages 70-85, DOI: https://doi.org/10.1016/j.csda.2018.03.013
13. Estimating Dynamic Functional Brain Connectivity with a Sparse Hidden Markov Model (SpHMM). | [GitHub link] | Neuroimaging Informatics Tools and Resources Clearinghouse
Details can be found at:
Gemeng Zhang, Biao Cai, Aiying Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang, Estimating Dynamic Functional Brain Connectivity with a Sparse Hidden Markov Model, IEEE Transactions on Medical Imaging, Date of Publication: 19 July 2019; DOI: 10.1109/TMI.2019.2929959
14. A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics (LGCM). | [GitHub link] | Neuroimaging Informatics Tools and Resources Clearinghouse
Details can be found at:
Aiying Zhang, Jian Fang, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang, A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Date of Publication: 01 November 2019; DOI: 10.1109/TCBB.2019.2950904
15. Deep collaborative learning ((DCL)). | [GitHub link]
Details can be found at:
Wenxing Hu, Biao Cai, Aiying Zhang, Vince D. Calhoun, Yu-Ping Wang, Deep collaborative learning with application to the study of multimodal brain development, IEEE Transactions on Biomedical Engineering, Date of Publication: 13 March 2019; DOI: 10.1109/TBME.2019.2904301
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.