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:    • Machine Learning in Genomics - Fall 2019   All slides from Fall 2019 are here: https://stellar.mit.edu/S/course/6/fa... 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