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singleCellHaystack

singleCellHaystack is a package for predicting differentially expressed genes (DEGs) in single cell transcriptome data. It does so without relying on clustering of cells into arbitrary clusters! Single-cell RNA-seq (scRNA-seq) data is often processed to fewer dimensions using Principal Component Analysis (PCA) and represented in 2-dimensional plots (e.g. t-SNE or UMAP plots). singleCellHaystackuses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a these multi-dimensional spaces or 2D representations.

Citation

Our manuscript describing singleCellHaystack has been published inNature Communications.

If you use singleCellHaystack in your research please cite our work using:

Vandenbon A, Diez D (2020). “A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data.”Nature Communications, 11(1), 4318. doi: 10.1038/s41467-020-17900-3 (URL: https://doi.org/10.1038/s41467-020-17900-3).

Documentation and Demo

Our documentationincludes a few example applications showing how to use our package:

Installation

You can install the released version of singleCellHaystack fromCRAN with:

install.packages("singleCellHaystack")

You can also install singleCellHaystack from the GitHub repository as shown below. Typical installation times should be less than 1 minute.

require(remotes) remotes::install_github("alexisvdb/singleCellHaystack")

System Requirements

Hardware Requirements

singleCellHaystack requires only a standard computer with sufficient RAM to support running R or RStudio. Memory requirements depend on the size of the input dataset.

Software Requirements

This package has been tested on Windows (Windows 10), macOS (Mojave 10.14.1 and Catalina 10.15.1), and Linux (CentOS 6.9 and Ubuntu 19.10).

singleCellHaystack depends on the following packages: splines (3.6.0), ggplot2 (3.2.0), reshape2 (1.4.3).