Iterative point set registration for aligning scRNA-seq data

Amir Alavi and Ziv Bar-Joseph

PLOS Computational Biology, 2020, vol. 16, issue 10, 1-21

Abstract: Several studies profile similar single cell RNA-Seq (scRNA-Seq) data using different technologies and platforms. A number of alignment methods have been developed to enable the integration and comparison of scRNA-Seq data from such studies. While each performs well on some of the datasets, to date no method was able to both perform the alignment using the original expression space and generalize to new data. To enable such analysis we developed Single Cell Iterative Point set Registration (SCIPR) which extends methods that were successfully applied to align image data to scRNA-Seq. We discuss the required changes needed, the resulting optimization function, and algorithms for learning a transformation function for aligning data. We tested SCIPR on several scRNA-Seq datasets. As we show it successfully aligns data from several different cell types, improving upon prior methods proposed for this task. In addition, we show the parameters learned by SCIPR can be used to align data not used in the training and to identify key cell type-specific genes.Author Summary: Integrating single cell expression data (scRNA-Seq) across labs, platforms, and technologies is a major challenge. Current methods for addressing this problem attempt to align cells in one study to match cells in another. While successful, current methods are unable to learn a general alignment in gene space that can be used to process new or additional data not used in the learning. Here we show that the scRNA-Seq alignment problem resembles a well known problem in the field of computer vision and robotics: point-cloud registration. We next extend traditional iterative rigid-object alignment methods for scRNA-seq while satisfying a set of unique constraints that distinguishes our solution from past methods. Analysis of transcriptomics data demonstrates that our method can accurately align scRNA-seq data, can generalize to unseen datasets, and can provide useful insights about genes active in the cells being studied.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007939

DOI: 10.1371/journal.pcbi.1007939

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