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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, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!--{width="100"}<!-- -->
<p align="center">
<img src="man/figures/README-logo.png" width="50%" />
</p>
# Canek
<!-- badges: start -->
[](https://github.com/MartinLoza/Canek/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=Canek)
[](https://cran.r-project.org/package=Canek)
[](https://cran.r-project.org/package=Canek)
<!-- badges: end -->
*Canek is an R package to correct batch effects from single-cell RNA-seq biological replicates*.
### Motivation to develop Canek
As single-cell genomics technologies become mainstream, more laboratories will perform experiments under different conditions with biological replicates obtained using a common technology. In this scenario, integration of datasets with minimal impact on cell phenotype is essential.
### The workflow
Canek leverages information from mutual nearest neighbors to combine local linear corrections with cell-specific non-linear corrections within a fuzzy logic framework.
<p align="center">
<img src="man/figures/README-workflow.png" width="100%"/>
</p>
<style type="text/css">
ol { list-style-type: upper-alpha; }
</style>
<font size="2">
> A. Canek starts with a reference batch and query batch, assuming a predominantly linear batch effect.
>
> B. Cell clusters are defined on the query batch and MNN pairs (arrows) are used to define batch effect observations.
>
> C. The MNN pairs from each cluster are used to estimate cluster specific correction vectors. These vectors can be used to correct the batch effect or, (D) a non-linear correction can be applied by calculating cell-specific correction vectors using fuzzy logic.
<font size="3">
### Results
Canek was the highest scored method in tests specifically designed to assess **over-correction**, where Canek corrected batch effects without distortion to the structures of cells as compared with a gold standard.
For more information about Canek check out our manuscript in [NAR Genomics and Bioinformatics](https://doi.org/10.1093/nargab/lqac022).
## Usage
You can use Canek directly with *normalized-count matrices*, *Seurat* objects or *SingleCellExperiment* objects. For more details, check out our GitHub page and vignettes:
- [Canek website](https://martinloza.github.io/Canek/index.html)
- [Run Canek on a toy example vignette](https://martinloza.github.io/Canek/articles/toy_example.html)
- [Run Canek on Seurat objects vignette](https://martinloza.github.io/Canek/articles/seurat.html)
- [Run Canek on SingleCellExperiment objects vignette](https://martinloza.github.io/Canek/articles/SingleCellExperiment.html)
- [Best practices on batch effects correction](https://martinloza.github.io/Canek/articles/Best_practices_thymus.html)
## Installation
You can install the release version of Canek from [CRAN](https://CRAN.R-project.org) with:
```
install.packages("Canek")
```
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("remotes")
remotes::install_github("MartinLoza/Canek")
```
## Citation
If you use Canek in your research please cite our work using:
```{r echo=FALSE, results='asis', comment=""}
cit <- citation("Canek")
print(cit, style = "text")
```