This is the repository for the Analysis and Applications of Genome-Scale Data course (BMI8130 on Autumn 2021)
The goal of this course is to introduce trainees to the fundamental algorithms needed to understand and analyze genome-scale expression data sets. Special focus will be on single-cell data analysis. The course will cover three major categories:
- fundamental and advanced big-data analysis in solving real biological problems
- behind the scene: classic and cutting-edge algorithms/methods for tool development
- in-hand practice of using tools and data analysis. The course will include an introduction to, and hands-on experience with, the R statistical software environment and the use of R packages that can be applied to these kinds of problems
- Introduction to OSC
- Introduction to R programming language & R markdown
- RNA-seq data analysis (DESeq2)
- Single-cell RNA-seq single sample analysis (Seurat)
- Single-cell RNA-seq multi sample analysis (Seurat)
- ChIP-seq data analysis (ChIPseeker)
- Single-cell ATAC-seq general analysis (Seurat & Signac)
- Single-cell ATAC-seq general analysis (ArchR)
- Single-cell ATAC-seq cis-regulatory networks & co-accessibility analysis (cicero)
- Single-cell ATAC-seq cis-regulatory topics * cell states analysis (cisTopic)
- Single-cell multi-omic data analysis (Seurat)
- Spatial transcriptomics data analysis (Giotto)
- Spatial transcriptomics clustering analysis (BayesSpace)
- Spatial transcriptomics deconvolution analysis (Spotlight)
- Single-cell immune profiling data analysis (scRepertoire)
- Single-cell RNA-seq cell-cell communication analysis (CellChat)
- Single-cell RNA-seq data integration (Seurat)
Maintainer: Cankun Wang
Authors:
Repository material are inspired by the following courses or workshops:
- Bioinformatics Training at the Harvard Chan Bioinformatics Core
- UC Davis Bioinformatics core workshop
- SciLifeLab Analysis of RNA-Seq Data
- Seurat vignettes
- Signac vignettes
Contact us: [email protected]