an unsupervised learning of single-cell transcriptomic data allows identification of individual cells making transition between all cell states, and inference of genes that mark transitions.
Download the source codes and unzip the MATLAB package. Change the current directory in MATLAB to the folder containing the scripts.
We provide real examples used in our mansucript with MATLAB live scripts to reproduce our results. Please see the /Examples folder and the following details.
In this Matlab live script (generated by Matlab R2018a), we provide an example workflow that outline the key steps and unique features of QuanTC.
Mouse skin squamous cell carcinoma (SCC dataset) (Walkthrough) : This dataset of 382 cells on skin tumors contains FACS_isolated epithelial YFP+Epcam+ tumor cells, which are relatively homogeneous, and mesenchymal-like YFP+Epcam- tumor cells, which are more heterogeneous
Mouse embryonic development (Intestine dataset) (Walkthrough).
Sha, Yutong, et al. "Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data." Nucleic acids research 48.17 (2020): 9505-9520. https://academic.oup.com/nar/article/48/17/9505/5900115