Awesome

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.
2. Exploratory Data Analysis (EDA): sample quality and QC measures.
3. Sample and gene filtering.
4. Normalization: how sample quality and batch effects affect the data and how to account for it.
5. Comparison of normalizations and selection of top method.
6. Cluster analysis with clusterExperiment.
7. Compare different clustering approaches (varying number of PCs, clustering algorithm, ...).
8. Combine multiple clustering into a consensus and visualization of "final" clusters.
9. Selection of cluster-specific marker genes.
10. Lineage inference and trajectory analysis with slingshot.
11. Lineage reconstruction.
12. Trajectory analysis and visualization.
13. Selection of genes that correlate with pseudotime.