Lecture 3: Trajectory Analysis & Cell-Cell Communication in scRNA-seq Data

↩︎️ Lecture Material

Lecture Description

This last class focused on single-cell RNA-seq data analysis consist of trajectory analysis using Slingshot and cell-cell interaction analysis to detect the pairwise interactions between cell clusters. Moreover, Differential Expression Analysis and Gene set variation analysis between two subclusters are included in this lecture.

Own Preparation

1. Revisit Lecture 2 (required) Prior to attending the lecture, we highly recommend that participants spend time reviewing Lecture 2.

2. Prepare Your R Environment and input for this class (required) Please complete the Lecture 2 HW. It prepares your R environment and provides the input (integrated SeuratObject) for the cell-cell communication of the practical session of Lecture 3. As a continuation of the previous lecture, the outputs from Lecture 2 practical session: Myeloid cells_Reclustering_SO.rds &Initial_clustering.rds.zip from the ‘data/processed/’ directory serve as the inputs for trajectory and cell-cell communication analysis (respectively) of this lecture.

3. Review the Paper (recommended) The following paper serves as the foundation reference for the analysis conducted during the course: Single-cell landscape reveals active cell subtypes and their interaction in the tumor microenvironment of gastric cancer

4. Take advantage of free tutorials online on trajectory analysis and read up about studying cell-cell communication using single cell data. (recommended) It will also be beneficial for you to go through this tutorial on trajectory analysis via Slingshot provided as part of the library vignette. As for methods for cell-cell communication based on single cell data, here is a fitting article.