https://github.com/niaid/hdstim
HDStIM: High Dimensional Stimulation Immune Mapping
https://github.com/niaid/hdstim
assay cytof cytometry cytometry-analysis-pipeline flowcytometry r-package stimulation
Last synced: 8 months ago
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HDStIM: High Dimensional Stimulation Immune Mapping
- Host: GitHub
- URL: https://github.com/niaid/hdstim
- Owner: niaid
- License: other
- Created: 2020-09-14T19:31:25.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-05T20:19:18.000Z (over 2 years ago)
- Last Synced: 2025-09-08T15:47:11.457Z (9 months ago)
- Topics: assay, cytof, cytometry, cytometry-analysis-pipeline, flowcytometry, r-package, stimulation
- Language: R
- Homepage: https://niaid.github.io/HDStIM/
- Size: 42.3 MB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# HDStIM 
[](https://github.com/niaid/HDStIM/actions?query=workflow%3AR-CMD-check)
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)