Trajectory Analysis-2: Tools & Algorithms

Duration: 40 Minutes

Lecture Material:

Quick Summary

Data pre-processing is the first step and perhaps one of the most critical in trajectory analysis. Normalizing raw cell counts ensures that the data from different cells can be compared accurately. Additionally, dimensionality reduction methods like PCA, tSNE, and UMAP are essential in parsing through the high-dimensional data.

Clustering methods like Leiden and Louvain are often employed to segregate the cells into distinct groups. These methods are based on optimizing a score known as Cluster Modularity, which helps in identifying stable cell states and branching events in a cellular trajectory. The importance of clustering is twofold: it enables both computational efficiency and nuanced biological interpretation.

Various trajectory inference algorithms like Slingshot, Monocle3, TSCAN, and Palantir are used to construct cellular trajectories. Each has its advantages and challenges. For example, Monocle3 uses PQ graphs, while Palantir employs Markov chains to build these trajectories. The concept of pseudotime is crucial, serving as a relative measure of how cells progress along a given pathway.

Understanding gene expression along these trajectories is another pivotal aspect. Techniques like Moran's I spatial autocorrelation are employed to understand how specific genes are expressed at various locations along the trajectory. Advanced statistical models like Generalized Additive Models (GAMs) are used to infer smooth functions that can depict gene expression measures along a pseudotime.

In conclusion, trajectory analysis is a multifaceted approach in single-cell biology, requiring a blend of advanced algorithms and statistical methods for practical interpretation. From data pre-processing to clustering and trajectory inference, each step is crucial and requires a nuanced understanding of both the computational methods and the biological questions at hand.