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ABSTRACT Echinoderms represent a broad phylum with many tractable features to test evolutionary changes and constraints. Here, we present a single-cell RNA-sequencing analysis of early development in the sea star Patiria miniata, to complement the recent analysis of two sea urchin species. We identified 20 cell states across six developmental stages from 8 hpf to mid-gastrula stage, using the analysis of 25,703 cells. The clusters were assigned cell states based on known marker gene expression and by in situ RNA hybridization. We found that early (morula, 8-14 hpf) and late (blastula-to-mid-gastrula) cell states are transcriptionally distinct. Cells surrounding the blastopore undergo rapid cell state changes that include endomesoderm diversification. Of particular import to understanding germ cell specification is that we never see Nodal pathway members within Nanos/Vasa-positive cells in the region known to give rise to the primordial germ cells (PGCs). The results from this work contrast the results of PGC specification in the sea urchin, and the dataset presented here enables deeper comparative studies in tractable developmental models for testing a variety of developmental mechanisms.
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Discovering a sparse set of pairwise discriminating features in high-dimensional data
Abstract Motivation Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. Results We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. Availability and implementation https://github.com/smelton/SMD. Supplementary information Supplementary data are available at Bioinformatics online.
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- Award ID(s):
- 1764269
- PAR ID:
- 10328966
- Editor(s):
- Luigi Martelli, Pier
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 37
- Issue:
- 2
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- 202 to 212
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT: Motivation Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Results Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data. Availability and implementation The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997. Supplementary information Supplementary data are available at Bioinformatics online.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btaa690
