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Update README file.
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romanhaa committed Sep 25, 2020
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R package upon which the [Cerebro](https://github.com/romanhaa/Cerebro) is built.
Contains helper function that prepare single-cell RNA-seq data stored in a Seurat object for visualization in Cerebro.
Both Seurat v2 and Seurat v3 are supported.
Seurat v3 and `SCE`/`SingleCellExperiment` objects are supported.

Make sure to install the package using `BiocManager::install()` because `devtools::install_github()` will otherwise pull old versions of dependencies that can result in errors.

```r
BiocManager::install('romanhaa/cerebroApp')
```

For further details, please refer to the official [Cerebro](https://github.com/romanhaa/Cerebro) repository.

## How to use

### Export data stored in Seurat object

**Required meta data:**

* Experiment name.
* Organism, e.g. 'hg' (human) or 'mm' (mouse).
* Sample.
* Cluster.
* Number of transcripts (usually created by Seurat by default; `nUMI` / `nCounts_RNA` in Seurat v2 and v3).
* Number of expressed genes (usually created by Seurat by default; `nGene` / `nFeature_RNA` in Seurat v2 and v3).

**Note:** It is recommended to save sample information in a column called `sample` and cluster information in a column called `cluster`. Otherwise, the respective column names need to specified below.

Prepare data:

```r
library('cerebroApp')
exportFromSeurat(seurat, 'my_experiment.crb')
```

### Launch Cerebro

Launch Cerebro and load the `.crb` (or `.rds`) file you just exported from R.

```r
launchCerebro()
```

### Optional (but recommended) steps

To take full advantage of Cerebro, it is recommended to also run the commands below before exporting the data.

```r
seurat <- addPercentMtRibo(seurat)
seurat <- getMostExpressedGenes(seurat)
seurat <- getMarkerGenes(seurat)
seurat <- getEnrichedPathways(seurat)
seurat <- performGeneSetEnrichmentAnalysis(seurat, GMT_file = 'c2.all.v7.0.symbols.gmt')
```
For further details, please refer to the official [cerebroApp](https://romanhaa.github.io/cerebroApp/) website.

## Credit

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