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Activity Number: 70 - Novel Approaches for Omics and Multi-Omics Analysis
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #323080
Title: Tilted-CCA: Quantifying Common and Distinct Information in Jointly-Sequenced Multiomic Single-Cell Data
Author(s): Kevin Lin* and Nancy R Zhang
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: multiomic data; dimension reduction; CITE-seq; RNA; ATAC; Canonical correlation analysis
Abstract:

Jointly-sequenced multiomic single-cell datasets of two modalities (for example, RNA and protein) offer biologists insight on cell-type identification at single-cell resolution via existing dimension-reduction methods that aggregate information from both modalities, but do not quantify how much information is common to both modalities or distinct to each modality. We develop a new dimension-reduction method called Tilted-CCA to fill this gap, where we formalize information as geometric features based on the nearest-neighbor graphs and build upon the statistical foundation of Canonical Correlation Analysis (CCA). We demonstrate the utility of Tilted-CCA in two ways by analyzing PBMC cells, sequenced via CITE-seq (for RNA and protein) and 10x (for RNA and ATAC). First, we show that Tilted-CCA offers insight on designing the smallest antibody panel where the resulting protein expressions provide cell-type separations not reflected in RNA. Second, we show for this biological system, RNA and ATAC are tightly intertwined such that neither modality contains much distinct information, whereas RNA and protein each contains more distinct information not reflected in other modality.


Authors who are presenting talks have a * after their name.

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