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
How to cite
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).