MIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
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:
stellar.mit.edu/S/course/6/fa...