README.md

In minzheguo/SLICE: Determining Cell Differentiation and Lineage based on Single Cell Entropy

SLICE

SLICE is an algorithm that utilizes single-cell RNA-seq (scRNA-seq) data to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories.

Developed by Minzhe Guo

Installation

• In R or RStudio, type the following command to install devtools

install.packages("devtools") library(devtools)

• Then, use devtools to install SINCERA from github

devtools::install_github("xu-lab/SLICE")

• Use library() to activate SINCERA

library(SLICE)

Demonstration

• A demonstration of using SLICE to reconstruct a two-branched lung fibroblast differentiation lineage from E16.5 mouse lung single cell data can be found at https://github.com/minzheguo/SLICE/blob/master/demo/FB.R.

Notes:

• In order to use Seurat functionality, you need to import Seurat in advance and it is tested with Seurat 4.3.0, SeuratObject 4.1.3. Using other versions of Seurat (for example v5) may cause unexpected behaviour.
• New versions of princurve library is not backwards compatible, hence we updated our package to work with newer versions of princurve (tested with 2.1.6). Make sure you import the right version to use related functionality.

Citation

• Minzhe Guo, Erik L. Bao, Michael Wagner, Jeffrey A. Whitsett, Yan Xu. 2016. SLICE: determing cell differentiation and lineage based on single cell entropy. Nucleic Acids Research. doi:10.1093/nar/gkw1278. (MG and ELB are co-first authors)

minzheguo/SLICE documentation built on June 5, 2023, 11:56 a.m.