Revealing the vectors of cellular identity with single-cell genomics

By Allon Wagner, Aviv Regev, Nir Yosef. Nature Biotechnology, 2016

608 citations recorded by the source corpus.

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges—from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/694938f776e31d0162ff7fe163baf408e33af1b1

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Citations in the corpus

Polyploidy, the Nucleotype, and Novelty: The Impact of Genome Doubling on the Biology of the Cell (2019, background): “The ability to track the transcription of a single gene (Germier et al. 2017) or to explore the determinants of cell identity (Wagner et al. 2016) are clearly relevant to understanding how polyploidy works at the cellular level.”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “Background Simple integrated analysis work flows for single-cell transcriptomic data [1] have been enabled by frameworks such as SEURAT [2], MONOCLE [3], SCDE/PAGODA [4], MAST [5], CELL RANGER [6], SCATER [7], and SCRAN [8].”
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2019, unknown): “Current computational approaches attempt to achieve this usually in one of two ways [1].”
Information Constraints on Auto-Encoding Variational Bayes (2018, background): “Such data can reveal a cell’s type [29, 30], if we can cope with a high level of technical noise [31].”
Spatial components of molecular tissue biology (2022, unknown): “…techniques, usually by visualizing a limited number of molecular proteins or RNAs 13 , whereas single-cell profiling has characterized the molecular features of cell states and types and their underlying circuitry at unprecedented scale and molecular detail in dissociated samples 14–16 .”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, background): “A growing body of single-cell multi-omic techniques now offers the ability to further refine our definitions of cellular identity by providing multiple views of molecular state [4, 5].”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, background): “Interpreting scRNA-seq remains challenging, however, as the data is confounded by nuisance factors such as variation in capture efficiency and sequencing depth [5], amplification bias, batch effects [6] and transcriptional noise [7].”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, background): “Current computational approaches attempt to achieve this in only one of two ways [1].”
A deep generative model for gene expression profiles from single-cell RNA sequencing (2017, background): “While there is often little prior knowledge of single-cell heterogeneity generatingX , a reasonably general assumption is that X has been generated from a low-dimensional manifold of cellular states [1].”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, unknown): “…or disease models in order to increase statistical robustness (Wen & Tang, 2018), achieve consistency and reproducibility among studies (Haghverdi et al , 2018; Butler et al , 2018), and ultimately converge to a common ontology of cell states and types (Wagner et al , 2016; Regev et al , 2017).”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, methodology): “For all the scRNA-seq datasets, we used PCA (as a noise-reduction preprocessing step 1, 19 ) to project the cells into a 100-dimensional space and used the projected coordinates in the 100-dimensional spaces as inputs to scvis.”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, background): “Recently, scRNA-seq was developed to enable a wide 73 variety of transcriptomic analyses at the single cell level ([3], [4], [5], [6], [7], [8]).”