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Activity Number: 151 - Novel Methods and Tools in the Era of Big Omics Data
Type: Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #322848
Title: PseudoGA: Cell Pseudotime Reconstruction Method Based on Genetic Algorithm Using Single Cell RNA-Seq Data
Author(s): Indranil Mukhopadhyay* and Pronoy Kanti Mondal and Udit Surya Saha
Companies: Indian Statistical Institute and Indian Statistical Institute and Indian Statistical Institute
Keywords: single cell; genetic algorithm; resampling; robust; pseudotime
Abstract:

Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis only detects average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq (scRNA-seq) presents an unprecedented opportunity that helps in placing cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory (pseudotime), may reveal the developmental pathway that provides information on dynamic transcriptomic changes. Existing approaches heavily depend on reducing huge dimension to very low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming gene expressions vary according to a smooth curve along the pseudotime trajectory. Our method shows higher accuracy in simulated and real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from scRNA-seq data. We use resampling technique while applying PseudoGA to a large scRNA-seq data. PseudoGA is adaptable to parallel computing.


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