Online Program Home
My Program

Abstract Details

Activity Number: 648 - Are Statistical Methods Developed for Bulk RNAseq Data Appropriate for Single Cell Data Sets?
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #306606 Presentation
Title: Characterization of Differential Correlation Across Single Cell Differentiation Trajectories with ScDCARS
Author(s): Shila Ghazanfar*
Companies: Cancer Research UK Cambridge Institute
Keywords: correlation; single cell RNA-Sequencing

Single cell RNA-seq data places us in an unprecedented position where we are able to examine patterns of variation and importantly co-variation of genes across cells along continuous differentiation trajectories. We recently presented Differential Correlation Across Ranked Samples (DCARS), a statistical method to identify differentially correlated gene pairs across a set of ranked samples, representing either discrete or continuous patterns of group identity. Here, we describe a new approach, scDCARS, a framework for which changes in correlation are examined across a differentiation trajectory. We demonstrate scDCARS with liver developmental data and find key cascading changes in coordination of gene subnetworks including those associated with cell cycle and lipoprotein metabolism. Furthermore, we present scDCARS as part of the DCARS package as well as an interactive Shiny application readily available for scientists’ interrogation with new data. This work provides a unique lens in which higher order interactions among genes can be unpicked and understand the landscape of cell type fate choice.

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

Back to the full JSM 2019 program