README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "figures",
  out.width = "100%"
)
```

# Differential Co-Localization Analysis

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Differential Co-Localization analysis (DiCoLo) is a computational framework designed for capturing condition-specific co-localized gene patterns within single-cell RNA sequencing data. The major workflow of DiCoLo comprises the following three main steps:

* Step1. Compute gene-gene OT distance for each condition
* Step2. Construct differential graph operator
* Step3. Identify differential co-localized genes by spectral analysis
* Downstream tasks
  * Assemble significant DiCoLo genes into co-localized gene modules.

```{r pressure, echo=FALSE, out.width="100%", out.height="auto"}
knitr::include_graphics("./man/figures/DiCoLo_workflow.png")
```


## Installation

DiCoLo can be installed in R as follows:

```{r, eval=FALSE}
install.packages("devtools")
devtools::install_github("KlugerLab/DiCoLo")

library("DiCoLo")
```


## Example tutorial

Please check [DiCoLo tutorial](https://klugerLab.github.io/DiCoLo/articles/).


## References

References of DiCoLo functions can be found [here](https://klugerLab.github.io/DiCoLo/reference/index.html).

For additional information, please reference our pre-print:

**DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data**
Ruiqi Li, Junchen Yang, Pei-Chun Su, Ariel Jaffe, Ofir Lindenbaum, Yuval Kluger
*bioRxiv* 2025 November 26. doi: https://doi.org/10.1101/2025.11.23.689932