Skip to content
/ REBET Public
forked from genemine/REBET

a Method to Determine the Number of Cell Subpopulations

Notifications You must be signed in to change notification settings

zy-fang/REBET

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

36 Commits
 
 
 
 
 
 

Repository files navigation

REBET

1. REBET

a Method to Determine the Number of Cell Clusters based on Batch Effect Removal.

1.1 Description

REBET, is a method for determining the number of cell clusters is based on gene expression profile.
Two inputs are required to run REBET: (1) gene expression data matrix with samples in rows and genes in columns. (2) maximum number of clusters.

1.2 Download

REBET is implemented as an R package, which is freely available for non-commercial use.

REBET_0.1.0.tar.gz

2. Install

Step 1: Download the above REBET package and install it in R (tested on version 4.0.3).
Step 2: Install the "sva","flexclust","SC3","SingleCellExperiment","cluster","infotheo","scater","foreach","doParallel" R package (tested on version 4.0.3), which is dependent of REBET.

3. Usage

Notes: REBET was tested on linux and windows.

Using REBET is very simple. Just follow the steps below:
Step 1: open your R or Rstudio.
Step 2: in the R command window, run the following command to load the R package.

library(REBET)

Step 3: in R command window, run the following command to see the help document for running REBET. Then, you should be able to see a help page.

?REBET

Step 4: At the end of the help page, there is an example code. Copy these codes to command to run as follows:

data(Ramskold)

This dataset consists of gene expression values of 21042 genes from 33 samples. The true number of clusters is 7.
Ramsköld, D. et al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol., 30(8), 777–782.

result = REBET(data, Kmax=10)

In general, we first set Kmax to 10, and if the estimated optimal number of clusters happens to be 10, we then increase the value of Kmax. The result is a value, which is the optimal number of cell clusters returned by REBET.
The result returned by this example is 7, indicating that REBET accurately estimated the number of cell clusters in the Ramskold dataset.

4. Contact

If any questions, please do not hesitate to contact us at: Hongdong Li, [email protected]

5. How to cite?

If you use this tool, please cite the following work.
ZhaoYu Fang, CuiXiang Lin, YunPei Xu, Hongdong Li, QingSong Xu, REBET: a Method to Determine the Number of Cell Clusters based on Batch Effect Removal, 2021, submitted

About

a Method to Determine the Number of Cell Subpopulations

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published