scRNA-seq-Tutorial.Rmd

---
title: VI. Tutorial Single Cell Data Analysis 
author: "Lieven Clement"
date: "[statOmics](https://statomics.github.io), Ghent University"
output:
    html_document:
      theme: flatly     
      code_download: false
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bibliography: msqrob2.bib

---

<a rel="license" href="https://creativecommons.org/licenses/by-nc-sa/4.0"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>

### Macosko et al. dataset

Perform 

- QC
- Feature selection
- Dimension reduction 
- Clustering

on the Macosko dataset. A script with the steps can be found on
[Macosko](./singleCell_MacoskoWorkflow_blank.html). 

The data can also be downloaded on the UFORA course website (content > scRNAseq Data). 

### Differential expression analysis 

Follow [chapter 4](http://bioconductor.org/books/3.16/OSCA.multisample/multi-sample-comparisons.html) of the OSCA book on multi-sample.

You can generate the input sce object using the [OSCA book chapter](http://bioconductor.org/books/3.16/OSCA.multisample/chimeric-mouse-embryo-10x-genomics.html#chimeric-mouse-embryo-10x-genomics) or you can download it from the UFORA course website (content > scRNAseq Data). 

### Kang et al. dataset

1. Do the filtering and QC on the Kang dataset 
2. Perform DE analysis for the stimulus effect in each cell type

Data can be loaded via 

```
library(ExperimentHub)
eh <- ExperimentHub()
sce <- eh[["EH2259"]]
```

or on the UFORA course website (content > scRNAseq Data).