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https://github.com/bioinfo-tsukuba/CCPLS
https://github.com/bioinfo-tsukuba/CCPLS
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/bioinfo-tsukuba/CCPLS
- Owner: bioinfo-tsukuba
- Created: 2022-01-12T04:15:23.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-04T01:25:52.000Z (12 months ago)
- Last Synced: 2024-01-14T19:44:00.460Z (5 months ago)
- Language: R
- Size: 942 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Lists
- awesome-cell-cell-communication - CCPLS - [R]- CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling) is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. (Uncategorized / Uncategorized)
README
# CCPLS 
R package for estimating cell-cell communications from spatial transcriptome data with single-cell resolution.
Please see [the paper of CCPLS](https://academic.oup.com/bioinformatics/article/38/21/4868/6692423) for details.
## Getting started
### Install
dependence: R version >= 4.1.1.
```
# Install dependent packages
install.packages(c("cluster", "circlize", "dplyr", "pls", "purrr", "Seurat", "stringr"), dependencies = TRUE)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ComplexHeatmap")
# Install CCPLS
install.packages("devtools", dependencies = TRUE)
devtools::install_github("bioinfo-tsukuba/CCPLS")
```
### Quick example
#### 1. Prepare arguments for CCPLS
```
# Here is demonstraion by included dataset in CCPLS package.
# Please prepare dataset and output directory for your purpose.
load(system.file("extdata", "dataset.Rdata", package = "CCPLS"))
output_dir <- "~/CCPLS_test"
# Note that 400 cells (subcellular spots) were randomly extracted by Seq-Scope data (Cho et al., 2021), and this demonstration cannot be interpreted biologically.
```
```exp_mat```:
A matrix object containing expression values. The rows represent cells. The columns represent genes. Row names and column names correspond to cell IDs and gene symbols, respectively.
```coord_mat```:
A matrix object containing cell IDs, x and y coordinates. The rows represent cells. The first column contains cell IDs and the second and subsequent columns contain x and y coordinates.
```annot_mat```:
A matrix object containing cell IDs and cell type labels. The rows represent cells. The first column contains cell IDs and the second column contains cell type labels.
```output_dir```:
A string. Full path of directory for saving outputs generated by CCPLS.
```HVG_extract_num```:
A number. Option for specifying extraction number of HVGs. The value is set as 2,000 in the default setting.
#### 2. Run CCPLS
```
result_CCPLS <- CCPLS::cellCellReg(exp_mat, coord_mat, annot_mat, output_dir, HVG_extract_num = 2000)
```
#### 3. View reports by CCPLS
The outputs are stored in the ```output_dir```.
## Citation
If you use CCPLS please cite our paper:
Takaho Tsuchiya, Hiroki Hori, and Haruka Ozaki. 2022. “CCPLS Reveals Cell-Type-Specific Spatial Dependence of Transcriptomes in Single Cells.” Bioinformatics 38 (21): 4868–77. https://academic.oup.com/bioinformatics/article/38/21/4868/6692423
## License
Copyright (c) 2022 Takaho Tsuchiya and Bioinformatics Laboratory, Faculty of Medicne, University of Tsukuba released under the [Artistic License 2.0](http://www.perlfoundation.org/artistic_license_2_0).