Removing unwanted variation and quantifying cellular aging in single cell experiments

BIODS 260B
Nancy Zhang
Professor of Statistics and Data Science
Vice Dean of Wharton Doctoral Programs
3/16/2023
1:30 pm-2:50 pm
MSOB X303 (SEE ZOOM DETAILS BELOW)

Title: Removing unwanted variation and quantifying cellular aging in single cell experiments

Abstract: I will discuss two common problems in single cell experiments. The first is the pervasive issue of unwanted variation (batch effects).  These can be technical noise but can also be biological variation that is not of interest to a study. I will show that current paradigms for batch integration (also called “cell alignment/integration”) are overly aggressive, removing biologically meaningful variation.  I will describe a novel statistical procedure, cellanova, which makes use of a “pool-of-controls” design that is feasible in both clinical and laboratory settings, to separate unwanted variation from biological variation of interest.  This new framework is validated across diverse settings.  I will summarize the validations and illustrate using a few examples.

Next, I will shift gears and discuss another type of “noise” in single cell data: the intrinsic transcriptional  “noise” of cells, as defined by the classic experiments by Elowitz, Levine, Siggia and Swain (2002).  Commonly ignored in current analysis pipelines,  we show that intrinsic transcriptional noise is not only estimable from single cell data but also uniquely informative about cellular senescence.  Through analysis of data from in vitro cell irradiation experiments, from the Aging Mouse Atlas, and from a system of telomerase dysfunction-induced cellular aging, we show that while aging has varying effects across cell types, intrinsic transcriptional noise can serve as a universal and sensitive marker of cellular senescence.

The first part of this talk is joint work with Zhaojun Zhang and Zongming Ma, while the second part of this talk is joint work with Paul Hess and Bradley Johnson.

Website: https://statistics.wharton.upenn.edu/profile/nzh/

Zoom link: https://stanford.zoom.us/j/92124459914?pwd=cFpJYXVLOExUVjMzZkNsYXA0b0RxUT09&from=addon

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