MIT CompBio Lecture 21 - Single-Cell Genomics скачать в хорошем качестве

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...