Presentation on theme: "My first 100 Tb of data STATISTICAL METHODS FOR NEW TECHNOLOGY WORKING GROUP Ciprian M. Crainiceanu Johns Hopkins University"— Presentation transcript:
My first 100 Tb of data STATISTICAL METHODS FOR NEW TECHNOLOGY WORKING GROUP Ciprian M. Crainiceanu Johns Hopkins University http://www.biostat.jhsph.edu/smnt
Members of the group Key personnel C.M. Crainiceanu, B.S. Caffo, A.-M. Staicu, S. Greven, D. Ruppert, C.-Z. Di Senior Students V. Zipunnikov, J.-A. Goldsmith Other statisticians (>20) Scientific collaborators Direct collaboration Solving important scientific problems Diverse scientific applications
Scientific Collaborators Susan Bassett – fMRI, Alzheimers Danny Reich – DTI, DCE-MRI, MS Brian Schwartz – lead exposure, VBM, DTI, white matter imaging Stewart Mostofsky – fMRI, rsfcMRI, Autism, ADHD, Turrets Naresh Punjabi – EEG, sleep, sleep diseases Dzung Pham / Pilou Bazin – Cortical shape, thickness, lesion detection, MS Dean Wong – PET, fMRI substance abuse Susan Resnick – BLSA Jerry Prince – BLSA, ADNI Jim Pekar, Peter Van Zijl – 7T MRI, fMRI, rsfcMRI preprocessing, scanner physics Christos Davatzikos- RAVENS Susumu Mori – DTI, tractography Dana Boatman – ECOG, EEG, epilepsy Graham Redgrave – fMRI, DTI, Huntingtons, anorexia/bulimia Tudor Badea, Bruno Jednyak – Neuron classification, morphometry, 3D structure and shape Tom Glass – Gizmos Merck – EEG, neuroimaging Pfizer – imaging biomarkers?
A simple regression formula Data compression via longitudinal PCA MoM estimators of covariance matrices, smoothing Need: all covariance operators Solution: regress Y ij (d)Y ik (d) on 1, T ik, T ij, T ik T ij, jk
Functional regression No paper on longitudinal functional regression No paper published with this data structure Longitudinal extensions are not simple Technical details are hard without the correct recipe for known and published ingredients No available method that scales up Goldsmith, Feder, Crainiceanu, Caffo, Reich, 2010. PFR, JCGS, to appear Goldsmith, Crainiceanu, Caffo, Reich, 2010. LPFR, to appear?
PVD Y i = P V i D + E i P is T*A D is B*F V i is A*B A << T, B << F
Singular Value Decomposition (SVD) summarizes variance Subject-specific Data Eigenvariates Eigenfrequencies Diagonal Matrix Frequency. Frequency Time One subject
Caffo BS, Crainiceanu CM, Verduzco G, Joel SE, Mostofsky SH, Bassett SS, Pekar JJ. Two-Stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimers disease risk. NeuroImage (In Press). Default PVD Subject-specific Data Low rank approximation Eigenvariates Eigenfrequencies... Stacked across subjects Population decomposition Projecting original data onto population bases (Start here) SVD … Subject-specific Data
Main message, backed by 100Tb of data Eventually, good tech makes into observational and clinical trials Longitudinal/Multilevel FDA is the natural next step in FDA Data is changing the way we do business: availability, size, complexity Likely: funding will be based much more on relevance than on technical ability