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. 2021 May 5;37(6):882-884.
doi: 10.1093/bioinformatics/btaa755.

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

Massimo Andreatta  1   2   3 , Santiago J Carmona  1   2   3
Affiliations

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

Massimo Andreatta et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
Anchor finding and dataset integration using STACAS. (A) Expression level (log [ normalized UMI counts + 1]) of Cd8a and Cd4 after integration with Seurat CCA (top) or STACAS (bottom); important biological differences between the samples are lost by data rescaling and sub-optimal anchoring by Seurat 3 CCA. (B) Anchor distance distribution between pairs of samples prior to anchor filtering by STACAS; poor anchors with distance higher than threshold (represented with a vertical dashed line) are filtered out by STACAS. (C–E) Low-dimensionality UMAP visualization of scRNA-seq data, colored by sample, without batch correction (C), using Seurat CCA anchors (D) and using STACAS anchors (E) for dataset alignment. (F–H) UMAP visualization of scRNA-seq data, colored by TILPRED state prediction, without batch correction (F), using Seurat CCA anchors (G) and using STACAS anchors (H) for dataset alignment
See this image and copyright information in PMC

References

    1. Andreatta M. et al. (2020) Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses. bioRxiv 2020.06.23.166546; doi: 10.1101/2020.06.23.166546 - DOI
    1. Carmona S.J. et al. (2020) Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq. OncoImmunology, 9, 1737369. - PMC - PubMed
    1. Eisenstein M. (2020) Single-cell RNA-seq analysis software providers scramble to offer solutions. Nat. Biotechnol., 38, 254–257. - PubMed
    1. Haghverdi L. et al. (2018) Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol., 36, 421–427. - PMC - PubMed
    1. Lähnemann D. et al. (2020) Eleven grand challenges in single-cell data science. Genome Biol., 21, 31. - PMC - PubMed

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