Your browser doesn't support javascript.
loading
STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data.
Andreatta, Massimo; Carmona, Santiago J.
Affiliation
  • Andreatta M; Ludwig Institute for Cancer Research Lausanne, University of Lausanne, CH-1066 Epalinges, Switzerland.
  • Carmona SJ; Department of Oncology, CHUV, UNIL CHUV, CH-1066 Epalinges, Lausanne, Switzerland.
Bioinformatics ; 37(6): 882-884, 2021 05 05.
Article in En | MEDLINE | ID: mdl-32845323
ABSTRACT

SUMMARY:

STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. AVAILABILITY AND IMPLEMENTATION Source code and R package available at https//github.com/carmonalab/STACAS; Docker image available at https//hub.docker.com/repository/docker/mandrea1/stacas_demo.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Single-Cell Analysis Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2021 Document type: Article Affiliation country: Switzerland Publication country: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Single-Cell Analysis Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2021 Document type: Article Affiliation country: Switzerland Publication country: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM