CellScopes.jl

KO | EN

gitlite — search

#python #hacktoberfest #open-source #rust #gemini #hacktoberfest-accepted #ai #llm #hacktoberfest2024 #tailwindcss #vite #docker

★ 46 Open GitHub ↗

A Julia package for single cell and spatial data analysis

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# CellScopes.jl (version 2) <img src="https://github.com/HaojiaWu/CellScopes.jl/blob/main/data/logo.png" width="60" height="60">

For version 1 (as published in [Wu et al., 2024](https://www.nature.com/articles/s41467-024-45752-8)), see the [archived tag](https://github.com/HaojiaWu/CellScopes.jl/tree/archived).

![Version](https://img.shields.io/badge/version-2.0.0-blue)
![Modality](https://img.shields.io/badge/multimodal-supported-brightgreen)

```CellScopes.jl``` is a toolkit built using the Julia programming language, designed for the analysis of single-cell and spatial transcriptomic data. [***Version 1***](https://github.com/HaojiaWu/CellScopes.jl/tree/archived) provides core functionalities including data normalization, dimensionality reduction, cell clustering, and visualization, tailored to diverse data types generated by platforms such as **scRNA-seq**, **scATAC-seq**, **Visium**, **Xenium**, **Slide-seq**, **MERFISH**, **seqFISH**, **STARmap**, and **Cartana**.

In ***version 2***, CellScopes expands its capabilities to support joint analysis of spatial datasets generated by different technologies, such as **Xenium** and **VisiumHD**, and introduces methods for integrating adjacent tissue sections as well as performing same-slide multimodal analysis. With these additions, ```CellScopes.jl``` can help researchers analyze spatial datasets with complex experimental designs and interpret biological signals more effectively. The current version of ```CellScopes.jl``` is structured as follows:

## 1. Installation
#### 1.1. Install Julia 1.10.4
To install ```CellScopes.jl```, you will need to have Julia 1.6 or higher installed. It is recommended to use Julia 1.10.4 or higher to avoid issues with dependencies. Here we will show how to install Julia in the Linux server.

Here is how to install ```Julia 1.10.4```.

```bash
cd /path/to/your/home/dir/
wget https://julialang-s3.julialang.org/bin/linux/x64/1.10/julia-1.10.4-linux-x86_64.tar.gz
tar xvf julia-1.10.4-linux-x86_64.tar.gz
```
Then add Julia to PATH by appending the following code to the end of the ```~/.bashrc``` file.

```bash
export PATH=/path/to/your/home/dir/julia-1.10.4/bin:$PATH
```

To implement your changes, either open a new login session or reload the .bashrc via

```
source ~/.bashrc
```

#### 1.2. Install CellScopes and dependencies
To install all of the necessary dependencies, run the following command line in ```Julia```. Note that this will not install the unregisterd package ```Leiden.jl```, which you may need to install manually from the GitHub repository first.

```julia
using Pkg;
Pkg.add(url="https://github.com/bicycle1885/Leiden.jl") # Install the unregistered dependency Leiden.jl
Pkg.add(url="https://github.com/HaojiaWu/CellScopes.jl") # Install CellScopes.jl
```

## 2. Tutorials
```CellScopes``` supports analysis for single-cell RNA sequencing (scRNA-seq), single-cell ATAC-seq (scATAC-seq), Visium, Slide-seq, Cartana, MERFISH, seqFISH, STARmap and Xenium datasets. For more information, please refer to the tutorials provided below.

### 2.1. Multimodal analysis
***a. Joint analysis of multimodal datasets within same slice***: *Coming soon!* 
***b. Joint analysis of multimodal datasets between adjacent slices***: *Coming soon!* 
***c. Joint analysis of multimodal datasets across different slices***: *Coming soon!*

### 2.2. Standalone analysis

***a. dRNA HybISS by Cartana***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/cartana_tutorial
 
***b. scRNA-seq***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/scRNA_tutorial
 
***c. scATAC-seq***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/scATAC_tutorial
 
***d. 10x Visium***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/visium_tutorial
 
***e. 10x Xenium***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/xenium_tutorial
 
***f. MERFISH***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/MERFISH_tutorial
 
***g. Slide-seq***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/SlideSeq_tutorial
 
***h. seqFISH***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/seqfish_tutorial
 
***i. STARmap***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/starmap_tutorial
 
***j. Visium HD***: https://github.com/HaojiaWu/CellScopes.jl/blob/main/docs/VisiumHD_tutorial

### 2.3. Interaction with other tools
In addition to these standalone CellScopes analyses, we also provide tutorials how CellScopes can interact with other popular tools such as Seurat, Scanpy and tools for gene imputation and spot deconvolution.
 
***a. Conversion of Scanpy AnnData to CellScopes Objects***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/scanpy_conversion
 
***b. Conversion of Seurat Objects to CellScopes Objects***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/seurat_conversion
 
***c. Gene imputation using SpaGE***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/gene_imputation

### 2.4. Incorporation of custom high-res images
We also provide tutorials for incorporating high-resolution H&E and nuclei staining images for Visium and Xenium data visualization.
 
***a. Visium data visualization with a high-resolution H&E image***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/Visium_more_viz
 
***b. Xenium data visualization with a high-resolution H&E/DAPI image***: https://github.com/HaojiaWu/CellScopes.jl/tree/main/docs/Xenium_more_viz

## 3. Citation
If the tool is helpful to your study, please consider citing our papers: 
### Citing CellScopes V2
TBD

### Citing CellScopes V1
https://www.nature.com/articles/s41467-024-45752-8 
**Nature Communications.** 2024 Feb 15;15(1):1396. 
Wu H, Dixon EE, Xuanyuan Q, Guo J, Yoshimura Y, Debashish C, Niesnerova A, Xu H, Rouault M, Humphreys BD. 
**High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing.**

## 4. Contact
For more information, please contact <a href="https://humphreyslab.com/">The Humphreys Lab</a> or follow our Twitter account <a href="https://twitter.com/humphreyslab?lang=en">@HumphreysLab</a>

CellScopes.jl

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README