title: Disentangling multidimensional spatio-temporal data into their common and aberrant responses.
abstract: With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
authors.forename: Young Hwan
authors.initials: YH
authors.lastname: Chang
authors.affiliation: Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA.
authors.forename: James
authors.initials: J
authors.lastname: Korkola
authors.affiliation: Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
authors.forename: Dhara N
authors.initials: DN
authors.lastname: Amin
authors.affiliation: Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
authors.forename: Mark M
authors.initials: MM
authors.lastname: Moasser
authors.affiliation: Department of Electrical Engineering and Computer Sciences, Helen Wills Neuroscience Institute, University of California, Berkeley and UCB/UCSF Graduate Program in Bioengineering, CA, USA.
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
authors.affiliation: Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA.
authors.forename: Joe W
authors.initials: JW
authors.lastname: Gray
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Faculty Scientist, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
authors.forename: Claire J
authors.initials: CJ
authors.lastname: Tomlin
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects*; D053263:Gene Regulatory Networks; D006801:Humans; D000077341:Lapatinib; D009154:Mutation / Q000235:genetics; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D040081:Protein Array Analysis; D051057:Proto-Oncogene Proteins c-akt / Q000235:genetics; D011799:Quinazolines / Q000494:pharmacology
chemical_list: D014408:Biomarkers, Tumor; D011799:Quinazolines; D000077341:Lapatinib; D051057:Proto-Oncogene Proteins c-akt