README.md

In jinzhuangdou/SCCAT: Simultaneous Canonical Correlation Analysis on Two directions \

bindSC

bindSC (Bidirectional INtegration of Data from Single Cell multi-omics technologies) is an R package for single cell multi-omic integration analysis, developed and maintained by Ken chen's lab in MDACC. bindSC is developed to address the challenge of single-cell multi-omic data integration that consists of unpaired cells measured with unmatched features across modalities. Previous methods such as Seurat, Liger did not work on this case unless making features matched. The core algorithm implemented in bindSC package is BiCCA (Bidirectional Canonical Correlation Analysis), which utilizes a transition matrix Z (M features by L samples) to bridge matrix X (M features by K samples) with matrix Y (N features by L samples). The matrix Z is solved by maximizing correlation of pair (X, Z) and correlation of pair (Y, Z) simultaneously. Under estimated matrix Z, the cell/feature correspondence across datasets can be obtained by implementing standard CCA on pair (X, Z) and pair (Y, Z) respectively.

Once multiple datasets are integrated, bindSC provides functionality for further data exploration, analysis, and visualization. User can:

• Jointly defining cell types from multi-omic datasets
• Identifying more comprehensive molecular multi-view of biological processes in cell type level.

Improvements and new features will be added on a regular basis, please contact [e-mail] with any question.

Version History

v1.0.0 [7/7/2020]

• Release bindSC.

System Requirements

Hardware requirements

The bindSC package requires only a standard computer with enough RAM to support the in-memory operations. For minimal performance, please make sure that the computer has at least about 10 GB of RAM. For optimal performance, we recommend a computer with the following specs:

• RAM: 10+ GB
• CPU: 4+ cores, 2.3 GHz/core

Before setting up the bindSC package, users should have R version 3.6.0 or higher, and several packages set up from CRAN and other repositories. The user can check the dependencies in DESCRIPTION.

Installation

bindSC is written in R and can be installed by following R commands:

$ R
> install.packages('devtools')
> library(devtools)
> install_github('jinzhuangdou/bindSC')

Usage

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

• Testing unmatched feature integration using a toy example

• Jointly Defining Cell Types from scRNA-seq and scATAC-seq

Bug report

License

This project is covered under the GNU General Public License 3.0.

Citation

MS is in preparation.

jinzhuangdou/SCCAT documentation built on July 8, 2020, 2:36 p.m.