In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed at the level of single cells. In dynamic biological systems, it may not be appropriate to assign cells to discrete groups, but rather a continuum of cell states may be observed, e.g. the differentiation of a stem cell population into mature cell types. This is often represented as a trajectory in a reduced dimension of the scRNA-seq dataset.
Many methods have been suggested for trajectory inference. However, in this setting, it is often unclear how one should handle multiple biological groups or conditions, e.g. constructing and comparing the differentiation trajectory of a wild type versus a knock-out stem cell population.
In this workshop, we will explore methods for comparing multiple conditions in a trajectory inference analysis. We will expand on our recent package condiments and present the workflow on two examples.
We will first present a single-lineage trajectory dataset with two conditions which we call TGFB, and conduct a full workflow analysis on that dataset, from integration to differential expression. You can find the compiled version of that vignette here.
The workshop will start with an introduction to the problem and the first dataset using presentation slides. Following this, we will have a lab session on the first dataset. Time permitting, we will then look at a second dataset.
|TGFB: Data Integration||5m|
|TGFB: Trajectory Inference and Differential Topology||10m|
|TGFB: Differential Progression||5m|
|TGFB: Differential Expression||10m|
|KRAS: Trajectory Inference and Differential Topology||10m|
|KRAS: Differential Progression and Differentiation||5m|
|Wrap-up and Conclusions||5m|
Participants will learn how to reason about trajectories in single-cell RNA-seq data and how they may be used for interpretation of complex scRNA-seq datasets. Participants can follow along in the following fashions: