High Dimensional Stimulation Immune Mapping ('HDStIM')

A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.

HDStIM

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 with:

install.packages("HDStIM")

And the development version from GitHub with:

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

Contact

Rohit Farmer: [email protected], [email protected]