Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape

Abstract

Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration.Therefore, efficient computational tools for combining diverse datasets are crucial for biology in the single cell genomics era. A number of methods have been developed to assist data integration by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration method. We illustrate the power of BBKNN for dimensionalityreduced visualisation and clustering in multiple biological scenarios, including a massive integrative study over several murine atlases. BBKNN successfully connects cell populations across experimentally heterogeneous mouse scRNA-Seq datasets, which reveals global markers of cell type and organspecificity and provides the foundation for inferring the underlying transcription factor network. BBKNN is available at https://github.com/Teichlab/bbknn.

Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation

“…To achieve good clustering by cell types, number of counts, mitochondrial percentage and donor effects were regressed out. PCA was carried out on highly variable genes, the donor effect was reduced by BBKNN tool (43) . Leiden clustering (44) and B_cell (B_follicular, B_Hypermutation, B_mantle) in Spleen.…”

Current best practices in single‐cell RNA‐seq analysis: a tutorial

“…This approach will confound the batch effect with biological differences between cell types or states that are not shared among datasets. Data integration methods such as Canonical Correlation Analysis (CCA; Butler et al , ), Mutual Nearest Neighbours (MNN; Haghverdi et al , ), Scanorama (preprint: Hie et al , ), RISC (preprint: Liu et al , ), scGen (preprint: Lotfollahi et al , ), LIGER (preprint: Welch et al , ), BBKNN (preprint: Park et al , ), and Harmony (preprint: Korsunsky et al , ) have been developed to overcome this issue. While data integration methods can also be applied to simple batch correction problems, we recommend to be wary of over‐correction given the increased degrees of freedom of non‐linear data integration approaches.…”

Dissecting the human immune system with single cell RNA sequencing technology

“…In contrast, when analyzing and integrating samples of multiple conditions (i.e., disease vs. health), batch effect removal must be performed with caution. Several approaches have been proposed to reduce batch effect including mutual nearest neighbors (MNN) correct, seurat3, ResNet, Harmony, Scanorama, BBKNN, scGen, and so on. MNN Correct is used to identify the connections shared by two datasets.…”

“…The second is how to remove batch effect 90 ResNet, 92 Harmony, 93 Scanorama, 94 BBKNN, 95 scGen, 96 and so on. MNN Correct is used to identify the connections shared by two datasets.…”