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MOSCATO: A Supervised Approach for Analyzing Multi-Omic Single-Cell Data

Lorin M Towle-Miller, Jeffrey C Miecznikowski
doi: https://doi.org/10.1101/2021.09.02.458781
Lorin M Towle-Miller
aUniversity at Buffalo
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  • For correspondence: lorinmil@buffalo.edu
Jeffrey C Miecznikowski
aUniversity at Buffalo
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Abstract

Advancements in genomic sequencing continually improve personalized medicine in complex diseases. Recent breakthroughs generate multiple types of signatures (or multi-omics) from each cell, producing different data ‘omic’ types per single-cell experiment. We introduce MOSCATO, a technique for selecting features across multi-omic single-cell datasets that relate to clinical outcomes. For example, we leverage penalization concepts often used in multi-omic network analytics to accommodate the high-dimensionality where multiple-testing is likely underpowered. We organize the data into multi-dimensional tensors where the dimensions correspond to the different ‘omic’ types. Using the outcome and the single-cell tensors, we perform regularized tensor regression to return a variable set for each ‘omic’ type that forms the clinically-associated network. Robustness is assessed over simulations based on available single-cell simulation methods. Real data comparing healthy subjects versus subjects with leukemia is also considered in order to identify genes associated with the disease. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments. This algorithm may identify clinically-relevant genetic patterns on a cellular-level that span multiple layers of sequencing data and ultimately inform highly precise therapeutic targets in complex diseases. Code to perform MOSCATO and replicate the real data application is publicly available on GitHub at https://github.com/lorinmil/MOSCATO and https://github.com/lorinmil/MOSCATOLeukemiaExample.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/lorinmil/MOSCATO

  • https://github.com/lorinmil/MOSCATOLeukemiaExample

Copyright 
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-NC-ND 4.0 International license.
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Posted September 04, 2021.
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MOSCATO: A Supervised Approach for Analyzing Multi-Omic Single-Cell Data
Lorin M Towle-Miller, Jeffrey C Miecznikowski
bioRxiv 2021.09.02.458781; doi: https://doi.org/10.1101/2021.09.02.458781
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MOSCATO: A Supervised Approach for Analyzing Multi-Omic Single-Cell Data
Lorin M Towle-Miller, Jeffrey C Miecznikowski
bioRxiv 2021.09.02.458781; doi: https://doi.org/10.1101/2021.09.02.458781

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