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