Sign in
BioinformaticsBMC Medical Genomics
Peer-reviewed journal article

XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

Stefano CheloniiD, Roman HilljeiD, Lucilla LuziiD, Pier Giuseppe PelicciiD, Elena GattiiD

Abstract

Single-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence in the analysed samples of a mixture of cells arising from the host and the graft.\n\nWe have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes. We show its application on a single cell dataset composed by human and mouse cells.\n\nAvailability and implementationXenoCell is available for non-commercial use on GitLab: https://gitlab.com/XenoCell/XenoCell

PDFDOICode

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISJuppet '21Llorà-Batll '24Ni '19Aran '17Lin '20Megill '21
62% semantic matchSystems Biology
Quentin Juppet, Fabio De Martino, Elodie Marcandalli+5 more·Journal of Mammary Gland Biology and Neoplasia·May 17, 2021· 10 citations