Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM

Chen, Huidong; Albergante, Luca; Hsu, Jonathan Y.; Lareau, Caleb A.; Bosco, Giosuè Lo; Guan, Jihong; Zhou, Shuigeng; Gorban, Alexander N.; Bauer, Daniel E.; Aryee, Martin J.; Langenau, David M.; Zinovyev, Andreï; Buenrostro, Jason D.; Yuan, Guo‐Cheng; Pinello, Luca

doi:10.1038/s41467-019-09670-4

Cited by 221 publications

(225 citation statements)

References 53 publications

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“…A common practice for clustering and trajectory finding methods is to preprocess the data by selecting highly variable genes and performing dimension reduction using principal component analysis (PCA) 19 or t-Distributed Stochastic Neighbor Embedding (tSNE) 20 . Examples include Seurat 6,13,21 , pcaReduce 22 , MNN batch-effectcorrection 23 , RaceID3 24 , TSCAN 25 , STREAM 26 , and many others. There are also imputation methods designed to explicitly remove dropouts.…”

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confidence: 99%

Embracing the dropouts in single-cell RNA-seq analysis

2020

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One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction of the transcriptome of each cell. Almost all computational algorithms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation to address the dropouts. Here, an opposite view is explored. Instead of treating dropouts as a problem to be fixed, we embrace it as a useful signal. We represent the dropout pattern by binarizing single-cell RNA-seq count data, and present a cooccurrence clustering algorithm to cluster cells based on the dropout pattern. We demonstrate in multiple published datasets that the binary dropout pattern is as informative as the quantitative expression of highly variable genes for the purpose of identifying cell types. We expect that recognizing the utility of dropouts provides an alternative direction for developing computational algorithms for single-cell RNA-seq analysis.

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confidence: 99%

Embracing the dropouts in single-cell RNA-seq analysis

2020

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“…Indeed, the framework may also be applicable to, e.g., downstream analysis of chromatin accessibility trajectories in scATAC-seq datasets (e.g. Chen et al [2019]) or bulk RNA-seq time-course studies.…”

Section: Discussionmentioning

confidence: 99%

Trajectory-based differential expression analysis for single-cell sequencing data

Berge

Bézieux²,

Street

et al. 2019

Preprint

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Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type di↵erentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are di↵erentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess di↵erential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad di↵erences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage di↵erential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage di↵erential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from dropletbased and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

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“…At a proteomic level, researchers have identified proteins in the brain which are associated with the cognitive trajectory in the elderly (11). Finally, the evolution of single-cell sequencing has allowed the evaluation of these different layers in greater detail (12). The analysis of omics data has advanced the understanding of human diseases, but it is important to remember that these studies represent only one layer of a more complex system.…”

Section: Integrative Biology Approaches Applied To Human Diseases Intmentioning

confidence: 99%

“…Clustering analyses in scRNA-seq data can be very useful and informative, but they are not always able to describe dynamic biological processes involved in transitions between different states, such as cellular proliferation and maturation (12). Such events can be computationally modeled through the reconstruction of the cell trajectory and pseudotime estimation (53).…”

Section: Tools For the Analysis Of Single-layer High-throughput Datamentioning

confidence: 99%

Integrative Biology Approaches Applied to Human Diseases

Urbanski

Araújo

Creighton

et al. 2019

Computational Biology

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The study of multifactorial and complex interactions in human diseases has been transformed by the omics revolution. The speed and scale of omics analysis have increased exponentially in the past decades, and it is now easier and faster to generate large amounts of biological data. However, extracting meaningful information from this "sea of data" remains a major challenge. The field of integrative biology utilizes a holistic approach to integrate multilayer biological data. In this chapter, we introduce concepts and techniques for the analysis of single-layer omics data and for integrating multilayer omics datasets to extract meaningful and relevant biological insights. Integrative biology is a promising approach for the study of a wide range of human diseases. We also highlight some current challenges in the field, such as the need for more specialized and interpretable methods, while increasing the accessibility of integrative analysis for the scientific community.

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Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM

Cited by 221 publications

References 53 publications

Embracing the dropouts in single-cell RNA-seq analysis

Embracing the dropouts in single-cell RNA-seq analysis

Trajectory-based differential expression analysis for single-cell sequencing data

Integrative Biology Approaches Applied to Human Diseases

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