We have received lots of comments and updated our package thoroughly. Please visit the new website (https://github.com/clwan/LTMG).

This repository contains the R codes which could reproduce the results of "LTMG: A statistical model of transcriptional regulatory states in single cell RNA-Seq data" by Changlin Wan, Wennan Chang, Yu Zhang, Fenil Shah, Sha Cao, Melissa L. Fishel, Qin Ma, and Chi Zhang.

LTMGSCA is a left truncated mixture Gaussian (LTMG) model-based single cell RNA-seq analysis pipeline that can accurately infer the modality and distribution of individual gene’s expression profile in scRNA-seq data, while decrease the impact from dropout and low expression value. Enabled by LTMG, a differential expression test and a gene co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed and incorporated within the framework.

We compared LTMG with other state-of-art scRNA-seq models on a comprehensive set of human scRNA-seq data. LTMG in general achieved best goodness of fitting among them (lower KS value means better fitting performance). More detailed illustration could be found at https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkz655/5542876.

To install the LTMGSCA demonstration package, you will need to install the release version of devtools from CRAN with

install.packages("devtools")

Then,

devtools::install_github("zy26/LTMGSCA")

To see the demonstration of LTMG - DEG track, please choose between .md file and .pdf file.

To see the demonstration of LTMG - GCR track, please choose between .md file and .pdf file.

Wan, Changlin, Wennan Chang, Yu Zhang, Fenil Shah, Xiaoyu Lu, Yong Zang, Anru Zhang et al. "LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data." Nucleic Acids Research (2019).