Artikel
Dimension reduction for temporal patterns in time-series single-cell RNA-sequencing data
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Veröffentlicht: | 15. September 2023 |
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Generating single-cell RNA-sequencing (scRNA-seq) data at several time points, e.g., during a developmental process, promises insights into mechanisms controlling cellular differentiation at the level of individual cells. As there is no one-to-one correspondence between cells at different timepoints, a first step in a typical analysis workflow is to reduce dimensionality to visually inspect temporal patterns. Here, one implicitly assumes that the resulting low-dimensional manifold captures the central gene expression dynamics of interest. Yet, commonly used techniques are not specifically designed to do so and their representations do not necessarily coincide with the one that best reflects the actual underlying dynamics.
We thus investigate how visual representations of different temporal patterns in time-series scRNA-seq data depend on the choice of dimension reduction, considering principal component analysis (PCA), t-distributed stochastic neighbourhood embedding (t-SNE), uniform manifold approximation and projection (UMAP) and single-cell variational inference (scVI), a popular deep learning-based approach. We specifically focus on comparing visual representations of each dimension reduction technique as such visual inspection of temporal patterns is often a crucial first step that guides the choice of further downstream analyses.
To characterize the approaches in a controlled setting, we introduce an artificial time series in a real-world snapshot scRNA-seq dataset from one experimental time point by simulating an underlying low-dimensional developmental process and generating corresponding high-dimensional gene expression data. Specifically, we apply a specific dimension reduction approach (say, tSNE) to the snapshot data and transform the low-dimensional representation according to biologically meaningful temporal patterns, e.g., dividing cell clusters during a differentiation process. We train a deep learning model to generate synthetic high-dimensional gene expression profiles corresponding to the simulated pattern at each time point, and apply the different dimension reduction approaches on the high-dimensional time-series data to compare how well they reflect the underlying temporal pattern introduced in, e.g., t-SNE space. Subsequently, we vary the dimension reduction method used to introduce the temporal pattern, the temporal pattern itself, and the underlying snapshot dataset based on which the development is simulated, to generalize our findings.
We thus characterize the different perspective of each technique on a specific temporal pattern with respect to the dataset, the underlying representation in which the pattern was introduced and to the pattern itself. The results illustrate how the choice of the dimension reduction approach can dramatically alter, i.e, distort, temporal structure. We further demonstrate this effect on a real-world time-series scRNA-seq dataset. To alleviate such problems, we provide directions for designing dimension reduction techniques that explicitly respect temporal structure.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
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