Single Cell Analysis in Computational Biology - Lecture 3

Dive into the world of single-cell analysis in this comprehensive lecture. Explore the differences between bulk and single-cell approaches, understanding the importance of studying individual cells. Learn about various scRNA-seq technologies and their applications in addressing biological questions. Follow the analysis of 84,000 cells from 48 individuals, covering data cleaning, clustering, and cell annotation techniques. Discover how to identify differentially expressed genes and track gene expression changes associated with phenotypes. Examine multi-region analysis, module analysis, and the advantages of using modules over single genes. Address questions about robustness, reproducibility, and discrepancies between phenotype and transcriptome. Gain insights into linked regions correlation and cell-projected phenotypes, equipping yourself with essential knowledge for conducting scRNA-seq analysis.

Intro
Bulk vs. Single-Cell
Why Single Cells
scRNA-seq Technologies
scRNA-seq Biological Questions
84k cells from 48 individuals
Cleaning up Data
Clustering and Cell Annotation
DEGs Gene Expression Changes with Phenotypes
Multi-Region Analysis
Module Analysis
Q1: Why Modules instead of single-genes
Q2: Difference from Bulk
Q3: Robustness and Reproducibility
Linked Regions Correlation
Discrepancies between Phenotype and Transcriptome
scRNA-seq Analysis Questions
Cell-Projected Phenotypes