Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

By Brian L. Hie, Bryan D. Bryson, Bonnie Berger. Nature Biotechnology, 2019

798 citations recorded by the source corpus.

Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We present Scanorama, an algorithm that identifies and merges the shared cell types among all pairs of datasets and accurately integrates heterogeneous collections of scRNA-seq data. We applied Scanorama to integrate and remove batch effects across 105,476 cells from 26 diverse scRNA-seq experiments representing 9 different technologies. Scanorama is sensitive to subtle temporal changes within the same cell lineage, successfully integrating functionally similar cells across time series data of CD14+ monocytes at different stages of differentiation into macrophages. Finally, we show that Scanorama is orders of magnitude faster than existing techniques and can integrate a collection of 1,095,538 cells in just ~9 h. Scanorama integrates single-cell RNA-seq datasets from different tissues, different labs, different experiments or different technologies.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b8a08bc2499ffeae8c618901b2a6ccbdf2f974f1

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Citations in the corpus

Computational principles and challenges in single-cell data integration (2021, neutral): “22 ), LIGER 23 , Harmony 24 , BBKNN 25 , scVI 26 , conos 27 , scmap 28 , Scanorama 29 and scAlign 30 , among others.”
Best practices for single-cell analysis across modalities (2023, methodology): “…on the complexity of the integration tasks, such as atlas integration, deep-learning approaches such as scANVI 40 , scVI 41 and scGen 42 as well as linear-embedding models such as Scanorama 43 performed best, whereas for less complex integration tasks, Harmony 37 is the preferred method 35 .”
DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, methodology): “Integrating different spatial platform datasets, we compared DeepST with a variety of methods that have been widely used in single-cell data integration, including Harmony (17) and Scanorama (18).”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, positive): “We compare scVI to several methods, including MNN (Haghverdi et al , 2018), Seurat Alignment (Butler et al , 2018), ComBat (Johnson et al , 2007), Harmony (Korsunsky et al , 2019), Scanorama (Hie et al , 2019) and principal component analysis (PCA).”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “Scanorama also seeks to correct for batch effects through similar cells identified across batches [9].”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, positive): “For example, Seurat, Harmony, LIGER, scVI, Scanorama, fastMNN, scVI and scArches all learn a shared low-dimensional space that jointly represents the datasets and aligns cells in a matched biological state together.”
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans (2021, background): “S1 to S16 Tables S1 to S9 References (40-95)”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, methodology): “For Scanorama, we define Z1 and Z2 to be a 100-dimensional matrix produced with PCA, which is the same dimension reduction used in the integration method.”