MLG: Multilayer Graph Clustering for Multi-Condition ScRNA-Seq

• Activity Number: 55 - Statistical methods for data from single cell technologies
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Shan Lu* and Daniel Conn and Shuyang Chen and Kirby Johnson and Emery Bresnick and Sunduz Keles
• Companies: University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin, Madison
• Keywords: single cell RNA-sequencing; dimension reduction; clustering; stochastic block models; data integration

Abstract

Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present Multi Layer Graph Clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale benchmarking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.