annotate README.md @ 3:8f333f90d8d6 draft default tip

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 9961b5acbf9081f10e14bc272406b36854fa2924"

# Wrappers for Scater

This code wraps a number of [scater](https://bioconductor.org/packages/release/bioc/html/scater.html) and [scuttle](https://bioconductor.org/packages/3.13/bioc/html/scuttle.html) functions as Galaxy wrappers. Briefly, the `scater-create-qcmetric-ready-sce` tool takes a sample gene expression matrix (usually read-counts) and a cell annotation file, creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics` function (using other supplied files such as ERCC's and mitochondrial gene features).
Various filter scripts are provided, along with some plotting functions for QC.


## Typical workflow

1. Read in data with `scater-create-qcmetric-ready-sce`.
2. Visualise it.
   Take a look at the distribution of library sizes, expressed features and mitochondrial genes with `scater-plot-dist-scatter`.
   
3. Guided by the plots, filter the data with `scater-filter`.\
   You can either manually filter with user-defined parameters or use PCA to automatically removes outliers.
4. Visualise data again to see how the filtering performed using `scater-plot-dist-scatter`.\
   Decide if you're happy with the data. If not, try increasing or decreasing the filtering parameters.

6. Investigate other confounding factors.\
   Plot the data (using PCA) and display various annotated properties of the cells using `scater-plot-pca`.

## Command-line usage

The scripts require the installation of scater and few other R/BioConductor packages. An easy way to install them is to create a [conda](https://conda.io/) environment using the `environment.yml` file distributed together with these wrappers:

```
conda env create -f environment.yml
conda activate scater
```

For help with any of the following scripts, run:
 `<script-name> --help`

---

`scater-create-qcmetric-ready-sce.R`
Takes an expression matrix (usually read-counts) of samples (columns) and gene/transcript features (rows), along with other annotation information, such as cell metadata, control genes (mitochondrail genes, ERCC's), creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics`. Save the resulting SingleCellExperiment object in Loom format.


```
./scater-create-qcmetric-ready-sce.R -a test-data/counts.txt -c test-data/annotation.txt -f test-data/mt_controls.txt  -o test-data/scater_qcready.loom
```

---

`scater-plot-dist-scatter.R`
Takes SingleCellExperiment object (from Loom file) and plots a panel of read and feature graphs, including the distribution of library sizes, distribution of feature counts, a scatterplot of reads vs features, and % of mitochondrial genes in library.

```
./scater-plot-dist-scatter.R -i test-data/scater_qcready.loom -o test-data/scater_reads_genes_dist.pdf
```

---


`scater-pca-filter.R`
Takes SingleCellExperiment object (from Loom file) and automatically removes outliers from data using PCA. Save the filtered SingleCellExperiment object in Loom format.

```
./scater-pca-filter.R -i test-data/scater_qcready.loom -o test-data/scater_pca_filtered.loom
```

---

`scater-manual-filter.R`
Takes SingleCellExperiment object (from Loom file) and filters data using user-provided parameters. Save the filtered SingleCellExperiment object in Loom format.

```
./scater-manual-filter.R -i test-data/scater_qcready.loom -l 10000 -d 4 -m 33 -o test-data/scater_manual_filtered.loom
```

---

`scater-plot-pca.R`
PCA plot of a SingleCellExperiment object. The options `-c`, `-p`, and `-s` all refer to cell annotation features. These are the column headers of the `-c` option used in `scater-create-qcmetric-ready-sce.R`.

```
./scater-plot-pca.R -i test-data/scater_qcready.loom -c Treatment -p Mutation_Status -o test-data/scater_pca_plot.pdf
```

---

`scater-plot-tsne.R`
t-SNE plot of a SingleCellExperiment object. The options `-c`, `-p`, and `-s` all refer to cell annotation features. These are the column headers of the `-c` option used in `scater-create-qcmetric-ready-sce.R`.

```
./scater-plot-tsne.R -i test-data/scater_qcready.loom -c Treatment -p Mutation_Status -o test-data/scater_tsne_plot.pdf
```