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Abstract BackgroundCurrent methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. ResultsHere, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. ConclusionsThis work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.
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SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data
Abstract MotivationIntegrative analysis of large-scale single-cell data collected from diverse cell populations promises an improved understanding of complex biological systems. While several algorithms have been developed for single-cell RNA-sequencing data integration, many lack the scalability to handle large numbers of datasets and/or millions of cells due to their memory and run time requirements. The few tools that can handle large data do so by reducing the computational burden through strategies such as subsampling of the data or selecting a reference dataset to improve computational efficiency and scalability. Such shortcuts, however, hamper the accuracy of downstream analyses, especially those requiring quantitative gene expression information. ResultsWe present SCEMENT, a SCalablE and Memory-Efficient iNTegration method, to overcome these limitations. Our new parallel algorithm builds upon and extends the linear regression model previously applied in ComBat to an unsupervised sparse matrix setting to enable accurate integration of diverse and large collections of single-cell RNA-sequencing data. Using tens to hundreds of real single-cell RNA-seq datasets, we show that SCEMENT outperforms ComBat as well as FastIntegration and Scanorama in runtime (upto 214× faster) and memory usage (upto 17.5× less). It not only performs batch correction and integration of millions of cells in under 25 min, but also facilitates the discovery of new rare cell types and more robust reconstruction of gene regulatory networks with full quantitative gene expression information. Availability and implementationSource code freely available for download at https://github.com/AluruLab/scement, implemented in C++ and supported on Linux.
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- Award ID(s):
- 2233887
- PAR ID:
- 10575189
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 41
- Issue:
- 2
- ISSN:
- 1367-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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