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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
## singleCellHaystack
<!-- badges: start -->
[](https://github.com/alexisvdb/singleCellHaystack/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=singleCellHaystack)
[](https://cran.r-project.org/package=singleCellHaystack)
[](https://cran.r-project.org/package=singleCellHaystack)
<!-- badges: end -->
:warning: We updated `singleCellHaystack` in late 2022. The master branch on GitHub is now the updated version 1.0, described [here](https://doi.org/10.1038/s41598-023-38965-2). The version on CRAN is also this updated version. For the older version described [here](https://doi.org/10.1038/s41467-020-17900-3), please use branch "binary". :warning:
`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](https://doi.org/10.1038/s41467-020-17900-3)), we have updated the method and made it more generally applicable (see [Sci Rep](https://doi.org/10.1038/s41598-023-38965-2)). 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](https://github.com/ddiez/singleCellHaystack-py).
## Citations
- Our manuscript describing the updated, more generally applicable version of `singleCellHaystack` has been published in [Scientific Reports](https://doi.org/10.1038/s41598-023-38965-2).
- Our manuscript describing the original implementation of `singleCellHaystack` ([version 0.3.4](https://github.com/alexisvdb/singleCellHaystack/tree/binary)) has been published in [Nature Communications](https://doi.org/10.1038/s41467-020-17900-3).
If you use `singleCellHaystack` in your research please cite our work using:
```{r echo=FALSE, results='asis', comment=""}
cit <- citation("singleCellHaystack")
print(cit, style = "html")
```
## Documentation and Demo
:warning: We updated this documentation to reflect the new version 1.0 :warning:
Our [documentation](https://alexisvdb.github.io/singleCellHaystack/) includes a few example applications showing how to use our package:
- [Toy example](https://alexisvdb.github.io/singleCellHaystack/articles/a01_toy_example.html)
- [Single-cell RNA-seq](https://alexisvdb.github.io/singleCellHaystack/articles/examples/a02_example_scRNAseq.html)
- [Spatial transcriptomics using Visium](https://alexisvdb.github.io/singleCellHaystack/articles/examples/a03_example_spatial_visium.html)
- [Spatial transcriptomics using Slide-seq V2](https://alexisvdb.github.io/singleCellHaystack/articles/examples/a04_example_spatial_slideseqV2.html)
- [MOCA 100k cells](https://alexisvdb.github.io/singleCellHaystack/articles/examples/a05_moca_100k.html)
- [Predicting DEGs along a trajectory](https://alexisvdb.github.io/singleCellHaystack/articles/examples/a06_pseudotime.html)
- [Analysis of gene set activities](https://alexisvdb.github.io/singleCellHaystack/articles/examples/a07_gene_sets.html)
- Anything else to add? Please let us know!
## Installation
You can install `singleCellHaystack` from [CRAN](https://CRAN.R-project.org/package=singleCellHaystack) with:
``` r
install.packages("singleCellHaystack")
```
Or, you can install `singleCellHaystack` from the GitHub repository as shown below. Typical installation times should be less than 1 minute.
``` r
require(remotes)
remotes::install_github("alexisvdb/singleCellHaystack")
```
For the old binary version of `singleCellHaystack` as described [here](https://doi.org/10.1038/s41467-020-17900-3), you can use the binary branch on GitHub:
``` r
require(remotes)
remotes::install_github("alexisvdb/singleCellHaystack@binary")
```
## 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 & 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).