LINEAGE ESTIMATION FROM SINGLE-CELL RNASEQ TIME-SERIES

Background:

Fabian Theis’s group is one of the 2-3 leading teams in single cell analysis. They developed Scanpy https://scanpy.readthedocs.io/en/latest/, a fast and comprehensive analysis package.

Abstract:

Single-cell technologies have gained popularity in developmental and stem cell biology because they allow resolving potential heterogeneities due to asynchronicity of differentiating cells. With
technologies slowly becoming mature and cost-efficient, single cell profiles across multiple conditions e.g. time points and replicates are being generated.

In this talk I will first show that by modeling the high-dimensional single cell state space as a diffusion process, we can visualize cell differentiation and estimate lineage formation using pseudotemporal ordering. By including information across multiple time points and if available replicates, we can then setup a model motivated by population dynamics but with continuous states that explains cell lineage transitions in real time beyond pseudotime. I will finish by
briefly discussing computational challenges in upscaling to “big data” scRNAseq, and recent approaches based on deep learning.

Host:

Maximilian Haeussler, BME, School of Engineering