HDBIG-S2CCA

HDBIG-S2CCA is an HDBIG toolkit focusing on Structured Sparse Canonical Correlation Analysis (S2CCA). The current version includes matlab implementations of the structure-aware SCCA model (S2CCA), the GraphNet SCCA model (GN-SCCA), the Graph OSCAR SCCA (GOSC-SCCA) model, and the Absolute value based GraphNet SCCA model (AGN-SCCA). It can be applied to examine the association between genetic variations and imaging phenotypes.

HDBIG-S2CCA v1.0.0 is released: Download, Documentation (PDF)

References

Du L*, Yan J*, Kim S, Risacher SL, Huang H, Inlow M, Moore JH, Saykin AJ, Shen L, for the ADNI (2014) A novel structure-aware sparse learning algorithm for brain imaging genetics. MICCAI’14: Med Image Comput Comput Assist Interv, Lecture Notes in Computer Science, 8675:329-336, Boston, MA, September 14-18, 2014. (*equal contribution)

Du L, Yan J, Kim S, Risacher SL, Huang H, Inlow M, Moore JH, Saykin AJ, Shen L, for the ADNI. (2015) GN-SCCA: GraphNet sparse canonical correlation analysis for brain imaging genetics. BIH 2015 Special Session on Neuroimaging Data Analysis and Applications, Lecture Notes in Artificial Intelligence, 9250: 275-284, London, UK, 30 August - 2 September 2015.

Du L, Huang H, Yan J, Kim S, Risacher SL, Inlow M, Moore JH, Saykin AJ, Shen L, for the Alzheimer's Disease Neuroimaging Initiative. (2016) Structured sparse CCA for brain imaging genetics via graph OSCAR. BMC Systems Biology. 10 Suppl 3:68.

Du L, Huang H, Yan J, Kim S, Risacher SL, Inlow M, Moore JH, Saykin AJ, Shen L, for the Alzheimer's Disease Neuroimaging Initiative. (2016) Structured Sparse Canonical Correlation Analysis for Brain Imaging Genetics: An Improved GraphNet Method. Bioinformatics. 32 (10):1544-1551. 10.1093/bioinformatics/btw033.