singleCellHaystack in late 2022. The master
branch on GitHub is now the updated version 1.0, described
here. The version on CRAN
is also this updated version. For the older version described
here, please use branch
“binary”.
singleCellHaystack is a package for predicting differentially active
features (e.g. genes, proteins, chromatin accessibility) in single-cell
and spatial genomics data. While singleCellHaystack originally focused
on the prediction of differentially expressed genes (DEGs; see
here), we have updated the
method and made it more generally applicable (see Sci
Rep). It can now also be
used for finding differentially accessible genomic regions in
scATAC-seq, DEGs along a trajectory, spatial DEGs, or any other features
with non-random levels of activity inside any input space (1D, 2D, or
>2D). It does so without relying on clustering of samples into
arbitrary clusters. singleCellHaystack uses Kullback-Leibler
Divergence to find features that have patterns of activity in subsets of
samples that are non-randomly positioned inside any input space.
For the Python implementation, please see here.
-
Our manuscript describing the updated, more generally applicable version of
singleCellHaystackhas been published in Scientific Reports. -
Our manuscript describing the original implementation of
singleCellHaystack(version 0.3.4) 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.
Vandenbon A, Diez D (2023). “A universal tool for predicting differentially active features in single-cell and spatial genomics data.” Scientific Reports, 13(1), 11830. doi:10.1038/s41598-023-38965-2.
Our documentation includes a few example applications showing how to use our package:
- Toy example
- Single-cell RNA-seq
- Spatial transcriptomics using Visium
- Spatial transcriptomics using Slide-seq V2
- MOCA 100k cells
- Predicting DEGs along a trajectory
- Analysis of gene set activities
- Anything else to add? Please let us know!
You can install singleCellHaystack from
CRAN with:
install.packages("singleCellHaystack")Or, you can 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")For the old binary version of singleCellHaystack as described
here, you can use the
binary branch on GitHub:
require(remotes)
remotes::install_github("alexisvdb/singleCellHaystack@binary")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 & 11), macOS (Mojave 10.14.1 and Catalina 10.15.1), and Linux (CentOS 7.9 and Ubuntu 19.10).
singleCellHaystack depends on the following packages: Matrix (1.5-1),
splines (4.1.3), ggplot2 (3.3.6) and reshape2 (1.4.4).