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). singleCellHaystack
uses 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.
Our manuscript describing singleCellHaystack has been published in
Nature 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).
Our documentation includes a few example applications showing how to use our package:
- Application on toy example
- Application on multi-dimensional coordinates
- Application of the advanced mode on multi-dimensional coordinates
- Application on 2D t-SNE coordinates
- Application of the advanced mode on 2D t-SNE coordinates
- Application to spatial transcriptomics
You can install the released version of singleCellHaystack from
CRAN 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")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.
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).