STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data.

Massimo Andreatta,Santiago J Carmona,Jan Gorodkin

doi:10.1093/bioinformatics/btaa755

Massimo Andreatta, Santiago J Carmona + Show 1 more

Open Access

https://doi.org/10.1093/bioinformatics/btaa755

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Journal: Bioinformatics (Oxford, England)	Publication Date: Aug 26, 2020
Citations: 32	License type: CC BY 4.0

Affiliation: University of Lausanne

Abstract

SummarySTACAS 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 implementationSource code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

Highlights

Parallel single-cell transcriptomics has emerged as a transformative technology that enables measuring molecular profiles at single-cell resolution
STACAS is a package for determining integration anchors between heterogeneous datasets, and it is designed to be incorporated into Seurat dataset integration pipelines
STACAS employs a reciprocal principal component analysis (PCA) procedure to calculate anchors, where each dataset in a pair is projected onto the reduced PCA space of the other dataset; mutual nearest neighbors are calculated in these reduced spaces

Summary

Introduction

Parallel single-cell transcriptomics (scRNA-seq) has emerged as a transformative technology that enables measuring molecular profiles at single-cell resolution. Despite the highly multiplexed technologies, single-cell data are produced separately for different tissues and organs and are affected by multiple batch effects, such as different sample processing and scRNA-seq protocols. Seurat [4] is currently one of the most popular and best performing algorithms for single-cell data integration, and can be effortlessly integrated into complex analysis pipelines [5].

Results

Conclusion