MIT CompBio Lecture 21 - Single-cell genomics (Fall 2019)

MIT Computational Biology: Genomes, Networks, Evolution, Health http://compbio.mit.edu/6.047/ Prof. Manolis Kellis Full playlist with all videos in order is here: https://www.youtube.com/playlist?list=PLypiXJdtIca6U5uQOCHjP9Op3gpa177fK All slides from Fall 2019 are here: https://stellar.mit.edu/S/course/6/fa19/6.047/materials.html Outline for this lecture: 1. Single-cell profiling technologies - Traditional single-cell analyses - Single-cell RNA-seq - Dealing with noise in scRNA-seq data - Multiplexing: reduce batch effects, doublets, cost - Single-cell epigenomics (scATAC-Seq) - Single-cell multi-omics (PAIRED-seq, SNARE-seq, sci-CAR) 2. Extracting biological insights from single-cell data - Clustering similar cells - Clustering similar genes - Dimensionality reduction - Distinguishing different cell types - Trajectories through cell space - Dataset completion and missing data imputation - Multiresolution analysis - Comparison of multiple methods 3. Single-cell RNA-seq in disease: Focus on Brain Disorders - Why Brain: Cell type and function diversity - Initial maps of brain diversity across regions, development, organoids - Brain variation at the single-cell level in Alzheimer’s disease - Somatic mosaicism and clonality from scDNA-seq and scRNA-seq - Deconvolution of bulk data into single-cell profiles vs. phenotype vs. genotype - Deconvolution of eQTL effects at single-cell level and mediation analysis