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Temporal modelling using single-cell transcriptomics

Abstract

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

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Fig. 1: Overview of time-series scRNA-seq data analysis.
Fig. 2: Selecting time points to sample in scRNA-seq experiments.
Fig. 3: Trajectory inference methods for time-series scRNA-seq data.

Code availability

The code to reproduce the plots shown in Fig. 3 is available at http://dinglab.rimuhc.ca:8080/nrg/.

Data availability

The primary data for the analyses shown in Fig. 3 are from Treutlein et al.69, and the processed data presented in the figure are available at http://dinglab.rimuhc.ca:8080/nrg/.

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Acknowledgements

The work in this article is partially supported by National Institutes of Health (NIH) grants 1R01GM122096, OT2OD026682, 1U54AG075931 and 1U24CA268108 to Z.B.-J.

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Correspondence to Ziv Bar-Joseph.

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Glossary

Trajectory

A graph (often tree) structure that represents the states and their order during the biological process being studied. Cells are assigned to points on this graph.

Pseudo-time

Partial ordering of cells in single-cell RNA sequencing (scRNA-seq) data that represents predecessor and descendent cell state information.

Unique molecular identifiers

Sequence indices (often randomly generated) that are added to sequencing libraries before PCR amplification and enable the identification of PCR duplicates.

Dimensionality

In single-cell analyses, typically refers to the high versus low number of dimensions of the data. When working with large samples where each is composed of tens of thousands of features (for example, cells and their gene expression levels), the high dimension corresponds to the original values whereas the low dimension is a compact, although lossy, way to represent the data with many fewer values. Several low-dimension representation methods have been developed and they differ in the function they attempt to optimize (such as minimizing reconstruction loss, or minimizing differences in distance between the high-dimensional and low-dimensional spaces).

Auto-encoders

Neural networks whose goal is to reconstruct the input values. These networks are used for dimensionality reduction as they compress all input values through a small intermediate layer and then reconstruct them from the information in that layer.

Graphical models

Computational methods that are used to represent joint probability distributions in a compact manner. These include Bayesian networks, hidden Markov models (HMMs) and more.

Expectation–maximization

A widely used computational method that can be used to fill in missing data while simultaneously learning model parameters. The method iterates between the expectation step which determines expected values for missing data and the maximization step which infers parameters using the values obtained by the expectation step.

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Ding, J., Sharon, N. & Bar-Joseph, Z. Temporal modelling using single-cell transcriptomics. Nat Rev Genet 23, 355–368 (2022). https://doi.org/10.1038/s41576-021-00444-7

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