https://github.com/drisso/bioc2016singlecell

Bioconductor workshop: Analysis of single-cell RNA-seq data with R and Bioconductor
https://github.com/drisso/bioc2016singlecell

Last synced: 23 days ago
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Bioconductor workshop: Analysis of single-cell RNA-seq data with R and Bioconductor

• Host: GitHub
• URL: https://github.com/drisso/bioc2016singlecell
• Owner: drisso
• Created: 2016-06-07T18:28:52.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2016-11-07T07:09:32.000Z (over 7 years ago)
• Last Synced: 2024-02-23T17:35:10.412Z (4 months ago)
• Size: 20.1 MB
• Stars: 71
• Watchers: 8
• Forks: 19
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome_single_cell - Bioconductor2016 Single-cell-RNA-sequencing workshop by Sandrine Dudoit lab - [R] - SCONE, clusterExperiment, and slingshot tutorial. (Tutorials and workflows / Other applications)
• awesome-single-cell - Bioconductor2016 Single-cell-RNA-sequencing workshop by Sandrine Dudoit lab - [R] - SCONE, clusterExperiment, and slingshot tutorial. (Tutorials and workflows / Spatial transcriptomics)
• awesome-single-cell - Bioconductor2016 Single-cell-RNA-sequencing workshop by Sandrine Dudoit lab - [R] - SCONE, clusterExperiment, and slingshot tutorial. (Tutorials and workflows / Spatial transcriptomics)
• roryk-awesome-single-cell - Bioconductor2016 Single-cell-RNA-sequencing workshop by Sandrine Dudoit lab - [R] - SCONE, clusterExperiment, and slingshot tutorial. (Tutorials and workflows)

README

        
# BioC 2016 workshop

## Analysis of single-cell RNA-seq data with R and Bioconductor

__Davide Risso (@drisso), Michael Cole (@mbcole), and Kelly Street (@kstreet13)__

This repository contains the code and data needed for the workshop.

The workshop is divided in three parts:

1. Quality control (QC) and normalization with [scone](https://github.com/YosefLab/scone).

  1. Exploratory Data Analysis (EDA): sample quality and QC measures.

  2. Sample and gene filtering.

  3. Normalization: how sample quality and batch effects affect the data and how to account for it.

  4. Comparison of normalizations and selection of top method.

2. Cluster analysis with [clusterExperiment](https://github.com/epurdom/clusterExperiment).

  1. Compare different clustering approaches (varying number of PCs, clustering algorithm, ...).

  2. Combine multiple clustering into a consensus and visualization of "final" clusters.

  3. Selection of cluster-specific marker genes.

3. Lineage inference and trajectory analysis with [slingshot](https://github.com/kstreet13/slingshot).

  1. Lineage reconstruction.

  2. Trajectory analysis and visualization.

  3. Selection of genes that correlate with pseudotime.