ProjectSVR

ProjectSVR is a machine learning-based algorithm for mapping the query cells onto well-constructed reference atlas.

Related resources

Quick start dataset

The data of quick start for ProjectSVR is available at https://zenodo.org/record/8147304 or 百度云盘(https://pan.baidu.com/s/13qSpcaldyQ9MUVCIaYSUIQ) 提取码: psvr

Reference atlas

The reference cell atlases involved in ProjectSVR paper are available at https://zenodo.org/record/8350746 or 百度云盘(https://pan.baidu.com/s/1fNG5PcgqWiPZi3erkewA5w) 提取码: psvr

Query dataset

The query datasets involved in ProjectSVR paper are available at https://zenodo.org/record/8350748 or 百度云盘(https://pan.baidu.com/s/1yGdhcwBIxodinRpppPHkQw) 提取码: psvr

Pre-built reference model

You can download pre-build reference models from Zenodo or 百度云盘(https://pan.baidu.com/s/1yBWifQHimRNun1jgcYVEPg) 提取码: psvr

Name	Source	Version	Download
PBMC (DISCO)	https://www.immunesinglecell.org/atlas/blood	0.2	download
Mouse testicular cell atlas (mTCA)	This paper	0.2	download
Maternal-fetal interface atlas (Vento 2018)	https://doi.org/10.1038/s41586-018-0698-6	0.2	download
Pan cancer tumor infiltrated CD4+ T cell landscape (Zheng 2021)	https://doi.org/10.1126/science.abe6474	0.2	download
Pan cancer tumor infiltrated CD8+ T cell landscape (Zheng 2021)	https://doi.org/10.1126/science.abe6474	0.2	download

Tutorials

The ProjectSVR webpage with all the documentation and tutorials is here.

We have various examples, including:

A generic quick start tutorial on a demo PBMC scRNA-seq dataset.

• Quick start: mapping PBMC dataset onto pre-build PBMC reference.

Tutorials on how to build projection models for reference atlas.

• DISCO blood atlas

• Maternal-fetal interface atlas

• Pan-cancer T cell lanscape

• Mouse testicular cell atlas

Tutorials on how to project the query datasets onto reference atlas via pre-build models.

• Decidual immune microenvironment of RPL patients

• ICB responsiveness of BRCA

• Genetic perturbed germ cells (Zfp541-KO)

• Genetic perturbed germ cells (Ythdc2-KO)

• In vitro induced meiosis

A tutorial on how to train a model to predict pseudotime.

• Training the reference pseudotime model

A tutorial on how to train a multi-classifier for cell type auto annotation.

• Training an ensemble SVM model for cell type prediction

Installation

Install the development version from GitHub use:

install.packages("devtools")
devtools::install_github("JarningGau/ProjectSVR")

ProjectSVR has been successfully installed and test on ubuntu, centOS and wsl2.

Dependencies

• R >= 4.1

External packages

Install AUCell or UCell for signature score calculation.

## install UCell
# R = 4.3
BiocManager::install("UCell") # or
# R < 4.3
remotes::install_github("carmonalab/UCell", ref="v1.3")
## install AUCell
BiocManager::install("AUCell")

We provided a wrapper RunCNMF of python pacakge cnmf for feature selection. If you want to use it, you should install cnmf through reticulate.

install.packages("reticulate")
reticulate::install_miniconda()
## install sceasy for single cell data format transformation.
devtools::install_github("cellgeni/sceasy")
reticulate::py_install("anndata")
## install cnmf package via reticulate
reticulate::py_install("cnmf")

Benchmark results

Benchmark results of ProjectSVR and other reference mapping algorithms were listed at https://github.com/JarningGau/ProjectSVR-benchmark/

Code of Conduct

Please note that the ProjectSVR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.