README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# HDStIM <img src='man/figures/sticker.png' align="right" height = 200 />


<!-- badges: start -->
[![Actions Status](https://github.com/niaid/HDStIM/workflows/R-CMD-check/badge.svg)](https://github.com/niaid/HDStIM/actions?query=workflow%3AR-CMD-check)
<!-- badges: end -->

The goal of this package is to identify response to a stimulant in CyTOF/Flow cytometry stimulation assays by labeling cells as responded or not based on an unsupervised high dimensional approach. Starting from the annotated cell populations either through automated clustering such as FlowSOM or traditional cell gating, the primary function `HDStIM()` follows a heuristic approach to label cells as responding or non-responding. 

For a combination of cell population and stimulation type (e.g., CD127+ T-helper cells and interferon-alpha), `HDStIM()` starts by performing k-means clustering on the combined set of cells from stimulated and unstimulated samples. K-means clustering is performed on expression data of all the state markers combined. Upon clustering using a contingency table, a Fisher's exact test determines the effect size and the statistical significance of partitioning. Cells form the combinations that pass the Fisher's exact test are labelled as responding. 

## Installation

You can install the released version of stimcellselector from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("HDStIM")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("niaid/HDStIM")
```

## Contact
Rohit Farmer: [rohit.farmer@nih.gov](mailto:rohit.farmer@nih.gov), [rohit.farmer@gmail.com](mailto:rohit.farmer@gmail.com)