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MIT CompBio Lecture 21 - Single-Cell Genomics

MIT Computational Biology: Genomes, Networks, Evolution, Health Prof. Manolis Kellis http://compbio.mit.edu/6.047/ Fall 2018 Lecture 21 - Single-cell Genomics 1. Single-cell profiling technologies - Traditional single-cell analyses - Single-cell RNA-seq - Dealing with noise in scRNA-seq data - Single-cell epigenomics (scATAC-Seq) 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 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 Slides for Lecture 21: https://stellar.mit.edu/S/course/6/fa...