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https://github.com/kevinrue/hancock

Cell signatures, with confidence.
https://github.com/kevinrue/hancock

bioconductor-package classification-algorithm machine-learning single-cell-rna-seq

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Cell signatures, with confidence.

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# hancock

The goal of the [_hancock_](https://github.com/kevinrue/hancock) package is to provide a collection of methods for learning and applying gene signatures associated with cellular phenotypes and identities.
Particular focus is given to single-cell data stored in objects derived from the [`SummarizedExperiment`](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) class.

# Prerequisites

The [_hancock_](https://github.com/kevinrue/hancock) package supports classes of gene sets defined in multiple packages.
However, it uses `BaseSets` classes defined in the [_unisets_](https://github.com/kevinrue/unisets) package to return newly learned signatures with accompanying metadata.
This dependency may be installed as follows:

```
install.packages("devtools")
devtools::install_github("kevinrue/unisets", build_opts = c("--no-resave-data", "--no-manual"))
```

Several functions support the `tbl_geneset` class defined in the [_GeneSet_](https://github.com/Kayla-Morrell/GeneSet) package.
This package may be installed as follows:

```
devtools::install_github("Kayla-Morrell/GeneSet")
```

# Installation

The [_hancock_](https://github.com/kevinrue/hancock) package may be installed as follows:

```
install.packages("devtools")
devtools::install_github("kevinrue/hancock")
```

To build the vignette as well, please use the following code:

```
devtools::install_github("kevinrue/hancock", build_opts = c("--no-resave-data", "--no-manual"))
```

# Usage

Demonstration notebooks are available as vignettes and on the companion repository: https://github.com/kevinrue/hancock2018

# Contributing

Considerate contributions are very welcome!
Please refer to the [contributing guidelines](CONTRIBUTING.md) for more details.