Manolis Kellis
@manoliskellis1·24.9K subscribers·255 videos
MIT CompBio Lecture 21 - Single-cell genomics (Fall 2019)
Posted
November 19, 2019
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5,898
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November 19, 2019
5,898
92
3
1.61%
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1:25:30Now PlayingMIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
Full playlist with all videos in order is here
All slides from Fall 2019 are here
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
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