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Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

E. Kelly Buchanan, Ian Kinsella, Ding Zhou, Rong Zhu, Pengcheng Zhou, Felipe Gerhard, John Ferrante, Ying Ma, Sharon Kim, Mohammed Shaik, Yajie Liang, Rongwen Lu, Jacob Reimer, Paul Fahey, Taliah Muhammad, Graham Dempsey, Elizabeth Hillman, Na Ji, Andreas Tolias, Liam Paninski
doi: https://doi.org/10.1101/334706
E. Kelly Buchanan
1 Columbia University;
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  • For correspondence: ekb2154@columbia.edu
Ian Kinsella
1 Columbia University;
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  • For correspondence: iak2119@columbia.edu
Ding Zhou
1 Columbia University;
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  • For correspondence: dz2336@columbia.edu
Rong Zhu
1 Columbia University;
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Pengcheng Zhou
1 Columbia University;
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Felipe Gerhard
2 Q-State Biosciences, Inc., Cambridge, MA;
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John Ferrante
2 Q-State Biosciences, Inc., Cambridge, MA;
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Ying Ma
1 Columbia University;
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Sharon Kim
1 Columbia University;
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Mohammed Shaik
1 Columbia University;
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Yajie Liang
3 UC Berkeley;
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Rongwen Lu
3 UC Berkeley;
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Jacob Reimer
4 Baylor College of Medicine
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Paul Fahey
4 Baylor College of Medicine
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Taliah Muhammad
4 Baylor College of Medicine
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Graham Dempsey
2 Q-State Biosciences, Inc., Cambridge, MA;
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Elizabeth Hillman
1 Columbia University;
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Na Ji
3 UC Berkeley;
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Andreas Tolias
4 Baylor College of Medicine
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Liam Paninski
1 Columbia University;
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  • For correspondence: liam@stat.columbia.edu
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Abstract

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail. Here we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

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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. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 21, 2019.
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Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
E. Kelly Buchanan, Ian Kinsella, Ding Zhou, Rong Zhu, Pengcheng Zhou, Felipe Gerhard, John Ferrante, Ying Ma, Sharon Kim, Mohammed Shaik, Yajie Liang, Rongwen Lu, Jacob Reimer, Paul Fahey, Taliah Muhammad, Graham Dempsey, Elizabeth Hillman, Na Ji, Andreas Tolias, Liam Paninski
bioRxiv 334706; doi: https://doi.org/10.1101/334706
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Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
E. Kelly Buchanan, Ian Kinsella, Ding Zhou, Rong Zhu, Pengcheng Zhou, Felipe Gerhard, John Ferrante, Ying Ma, Sharon Kim, Mohammed Shaik, Yajie Liang, Rongwen Lu, Jacob Reimer, Paul Fahey, Taliah Muhammad, Graham Dempsey, Elizabeth Hillman, Na Ji, Andreas Tolias, Liam Paninski
bioRxiv 334706; doi: https://doi.org/10.1101/334706

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