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MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data.

ABSTRACT:

Background

Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each 'omic' type.

Results

Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data.

Conclusions

MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments.

SUBMITTER: Towle-Miller LM

PROVIDER: S-EPMC9351124 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data.

Towle-Miller Lorin M LM Miecznikowski Jeffrey C JC

BMC genomics 20220804 1

<h4>Background</h4>Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each 'omic' type.<h4>Results</h4>Robustness was assessed over ...[more]

PMID: 35927608

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