Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session Z06: Genome Organization and Subnuclear Phenomena III: Measuring and Manipulating Genome Organization
11:30 AM–2:30 PM,
Friday, March 10, 2023
Room: Room 129
Sponsoring
Unit:
DBIO
Chair: Siyuan Wang, Princeton University
Abstract: Z06.00013 : Interpretable and tractable probabilistic models for single-cell RNA sequencing*
2:18 PM–2:30 PM
Presenter:
Gennady Gorin
(California Institute of Technology)
Authors:
Gennady Gorin
(California Institute of Technology)
Lior Pachter
(California Institute of Technology)
Meichen Fang
(California Institute of Technology)
John J Vastola
(Harvard Medical School)
We outline a class of models of technical and biological variability for single-cell RNA sequencing (scRNA-seq). This technology can quantify the number of RNA molecules in millions of cells at a genome-wide scale. Due to the volume of the data, typical scRNA-seq analyses are ad hoc and descriptive, typically relying on normalization, background subtraction, and data filtering. We argue that the descriptive worldview is insufficient, as it oversimplifies the intrinsic variability in the transcriptional and experimental processes.
Our approach emphasizes a class of processes that are particularly suited to the analysis of scRNA-seq data. To facilitate biological discovery, these processes are interpretable: each parameter encodes the rate of a biophysical phenomenon. To enable use with large datasets, they are also tractable, translating the convolution of noise sources into the composition of generating functions. We present computationally facile solutions to generic biological systems, which couple stochastic transcriptional driving to downstream RNA processing. In addition, we outline probabilistic strategies for phenomena particular to scRNA-seq, such as non-ergodicity in developmental trajectories, background contamination, and variability in capturing individual molecules.
*This work was partially funded by NIHÂ U19MH114830.
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