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