### Installing sc-SHC Package - R

Source: https://github.com/igrabski/sc-shc/blob/main/README.md

This snippet demonstrates how to install the `sc-SHC` R package directly from GitHub. It requires the `devtools` package to be installed first, which facilitates the installation of packages from development repositories.

```R
# install.packages("devtools")
devtools::install_github("igrabski/sc-SHC")
```

--------------------------------

### Running Stand-alone sc-SHC Clustering Pipeline - R

Source: https://github.com/igrabski/sc-shc/blob/main/README.md

This code shows how to use the `scSHC` function, the stand-alone clustering pipeline, to generate clusters from single-cell RNA-sequencing data. The `data` parameter expects a (possibly sparse) matrix with genes as rows and cells as columns. Optional parameters like `batch`, `alpha`, `num_features`, `num_PCs`, `parallel`, and `cores` can be adjusted to control the clustering process and error rates.

```R
library(scSHC)
clusters <- scSHC(data)
```

--------------------------------

### Evaluating Significance of Provided Clusters with testClusters - R

Source: https://github.com/igrabski/sc-shc/blob/main/README.md

This snippet illustrates how to use the `testClusters` function to evaluate the statistical significance of an already existing set of clusters. It requires the original `data` matrix and a character vector of `clusters` labels corresponding to the cells. Similar optional parameters as `scSHC` can be used, and a specific set of variable genes can be provided via `var.genes`.

```R
library(scSHC)
new_clusters <- testClusters(data, as.character(clusters))
```

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