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Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

Young Hwan Chang, Jim Korkola, Dhara N. Amin, Mark Moasser, Jose M. Carmena, Joe W. Gray, Claire J. Tomlin
doi: https://doi.org/10.1101/004259
Young Hwan Chang
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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Jim Korkola
2Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA
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Dhara N. Amin
3Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
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Mark Moasser
3Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
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Jose M. Carmena
4Department of Electrical Engineering and Computer Sciences, Helen Wills Neuroscience Institute, University of California, Berkeley and UCB/UCSF Graduate Program in Bioengineering
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Joe W. Gray
2Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA
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Claire J. Tomlin
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
5Faculty Scientist, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
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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 across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data.

Author Summary One of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. 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. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 28, 2014.
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Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses
Young Hwan Chang, Jim Korkola, Dhara N. Amin, Mark Moasser, Jose M. Carmena, Joe W. Gray, Claire J. Tomlin
bioRxiv 004259; doi: https://doi.org/10.1101/004259
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Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses
Young Hwan Chang, Jim Korkola, Dhara N. Amin, Mark Moasser, Jose M. Carmena, Joe W. Gray, Claire J. Tomlin
bioRxiv 004259; doi: https://doi.org/10.1101/004259

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