Scissor: Single-Cell Identification of Subpopulations with bulk Sample phenOtype coRrelation

Introduction

Scissor is a novel approach that utilizes the phenotypes, such as disease stage, tumor metastasis, treatment response, and survival outcomes, collected from bulk assays to identify the most highly phenotype-associated cell subpopulations from single-cell data. The workflow of Scissor is shown in the following Figure:

News

• May, 2021: Scissor version 2.1.0 is updated.
  • Add utilities for cell level evaludations including correlation check and bootstrap (function: evaluate.cell)
• Feb, 2021: Scissor version 2.0.0 is launched.
  • Optimize the inputs and outputs in Scissor main function
  • Add utilities for the reliability significance test (function: reliability.test)
• Jun, 2020: Scissor version 1.0.0 is launched.

Installation

• Prerequisites: Scissor is developed under R (version >= 3.6.1). The Seurat package (version >= 3.2.0) is used for loading data and preprocessing.

• Latest version: The latest developmental version of Scissor can be downloaded from GitHub and installed from source by devtools::install_github('sunduanchen/Scissor')

Manual

Please see https://sunduanchen.github.io/Scissor/vignettes/Scissor_Tutorial.html for details. In the R terminal, please use the command ?Scissor to access the help documents.

Examples

In our Scissor Tutorial, we use several applications on the Lung Adenocarcinoma (LUAD) scRNA-seq cancer cells as examples to show how to execute Scissor in real applications.

How to cite Scissor

Please cite the following manuscript:

Phenotype-guided subpopulation identification from single-cell sequencing data
Duanchen Sun and Zheng Xia

License

Scissor is licensed under the GNU General Public License v3.0.

Improvements and new features of Scissor will be updated on a regular basis. Please post on the GitHub discussion page with any questions.