26-ideal-scrnaseq-pipeline.Rmd

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output: html_document
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# "Ideal" scRNAseq pipeline (as of Mar 2017)

```{r, echo=FALSE}
library(knitr)
opts_chunk$set(fig.align = "center", echo=FALSE)
```

## Experimental Design

* Avoid confounding biological and batch effects (Figure \@ref(fig:pipeline-batches))
    * Multiple conditions should be captured on the same chip if possible
    * Perform multiple replicates of each condition where replicates of different conditions should be performed together if possible
    * Statistics cannot correct a completely confounded experiment!

* Unique molecular identifiers
    * Greatly reduce noise in data
    * May reduce gene detection rates (unclear if it is UMIs or other protocol differences)
    * Lose splicing information
    * Use longer UMIs (~10bp)
    * Correct for sequencing errors in UMIs using [UMI-tools](https://github.com/CGATOxford/UMI-tools)

* Spike-ins
    * Useful for quality control
    * May be useful for normalizing read counts
    * Can be used to approximate cell-size/RNA content (if relevant to biological question)
    * Often exhibit higher noise than endogenous genes (pipetting errors, mixture quality)
    * Requires more sequencing to get enough endogenous reads per cell

* Cell number vs Read depth
    * Gene detection plateaus starting from 1 million reads per cell
    * Transcription factor detection (regulatory networks) require high read depth and most sensitive protocols (i.e. Fluidigm C1)
    * Cell clustering & cell-type identification benefits from large number of cells and doesn't requireas high sequencing depth (~100,000 reads per cell).

```{r pipeline-batches, out.width = '90%', fig.cap="Appropriate approaches to batch effects in scRNASeq. Red arrows indicate batch effects which are (pale) or are not (vibrant) correctable through batch-correction."}
knitr::include_graphics("figures/Pipeline-batches.png")
```
## Processing Reads
* Read QC & Trimming
    * [FASTQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), [cutadapt](http://cutadapt.readthedocs.io/en/stable/index.html)
    
* Mapping
    * Small datasets or UMI datasets: align to genome/transcriptome using [STAR](https://github.com/alexdobin/STAR)
    * Large datasets: pseudo-alignment with [Salmon](http://salmon.readthedocs.io/en/latest/salmon.html)
  
* Quantification
    * Small dataset, no UMIs : [featureCounts](http://subread.sourceforge.net/)
    * Large datasets, no UMIs: [Salmon](http://salmon.readthedocs.io/en/latest/salmon.html)
    * UMI dataset : [UMI-tools'](https://github.com/CGATOxford/UMI-tools) + [featureCounts](http://subread.sourceforge.net/)

## Preparing Expression Matrix

* Cell QC
    * [scater](http://bioconductor.org/packages/scater)
    * consider: mtRNA, rRNA, spike-ins (if available), number of detected genes per cell, total reads/molecules per cell

* Library Size Normalization
    * [scran](http://bioconductor.org/packages/scran)

* Batch correction (if appropriate)
    * [RUVs](http://bioconductor.org/packages/RUVSeq)

## Biological Interpretation

* Feature Selection
    * [M3Drop](http://bioconductor.org/packages/M3Drop)

* Clustering and Marker Gene Identification
    * [SC3](http://bioconductor.org/packages/SC3)

* Pseudotime
    * distinct timepoints: [TSCAN](http://bioconductor.org/packages/TSCAN)
    * small dataset/unknown number of branches: [Monocle2](https://bioconductor.org/packages/release/bioc/html/monocle.html)
    * large continuous dataset: [destiny](http://bioconductor.org/packages/destiny)

* Differential Expression
    * Small number of cells and few groups : [scde](http://hms-dbmi.github.io/scde/)
    * Replicates with batch effects : mixture/linear models
    * Balanced batches: [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) or [MAST](https://bioconductor.org/packages/release/bioc/html/MAST.html)
    * Large datasets: Kruskal-Wallis test (all groups at once), or Wilcox-test (compare 2-groups at a time).