CIDER: an interpretable meta-clustering framework for single-cell RNA-Seq data integration and evaluation
This repository hosts the code for the accepted version by Genome Biology.
The scripts are organised by the datasets.
The html reports generated by Rmarkdown can be viewed by https://htmlpreview.github.io.
Here we introduce a novel similarity metric based on Inter-group Differential ExpRession (IDER) and propose a workflow of Clustering by IDER (CIDER). The origin version was firstly proposed in our publication (Cancer Cell, 2020), in which we introduced the use of meta-clustering to partition scRNA-seq data from ovarian cancer fallopian tube epithelial cells confounded by structured batch effects and inter-patient variability. In this work, we present a scalable version of this methodology, and demonstrate its generalizable utility for wider application.
Zhiyuan Hu1,2,3, Ahmed A. Ahmed1,2, Christopher Yau4,5,6
1 Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
2 Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford OX3 9DU, UK
3 MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, OX3 9DS, UK
4 Division of Informatics, Imaging and Data Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
5 Alan Turing Institute, London NW1 2DB, UK
6 Health Data Research UK, Gibbs Building, 215 Euston Road, London, NW1 2BE