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CancerStemID

Published on by Andrew Teschendorff
The CancerStemID project consists of 1- an R package CancerStemID aimed at single-cell RNA-Seq (scRNA-Seq) studies that have profiled cells from all main stages in cancer development, including cells from normal or normal-adjacent tissues, cells from preneoplastic lesions and cells from invasive cancer. The R-package comes with a vignette and a scRNA-Seq data of esophageal cancer development in mouse (Yao J, Cui Q et al Nat Commun.2020). It is designed to illustrate the functionality of a number of R-functions for (i) estimating differentiation activity of tissue-specific transcription factors (TFs) in single-cells, for (ii) computing a transcription factor inactivation load (TFIL), a measure that we show correlates with cancer-risk, and (iii) the cancer-risk score itself. Task (i) is based on the SCIRA-algorithm (Teschendorff & Wang NPJ Genomic Medicine 2020) whereas tasks (ii)+(iii) are novel. In addition, we also illustrate how to estimate differentiation potency of single-cells, although details of this are also described elsewhere (Teschendorff & Enver, Nat Commun.2017). 2- a zip file "Analysis_Examples_CancerStemID.zip" that contains an executable R-markdown file and two human ESCC datasets (10X scRNA-Seq and 10X Visium) with extended analyses on these datasets to further illustrate the code and to demonstrate reproducibility of key results. 3- a number of R-scripts for analysing the human ESCC cohorts 1&2 10X scRNA-Seq, the 10X Visium data from ESCC cohort-1, the mouse ESCC 10X scRNA-Seq data, and for inferring and validating the esophageal specific regulatory network. Note: these scripts will not execute without access to the data-files which are available on request: andrew@picb.ac.cn

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