Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches

Zinovyev, Andreï; M, Sadovsky; Calzone, Laurence; Fouché, Aziz; Groeneveld, Clarice S.; Barillot, Emmanuel; Gorban, Alexander N.

doi:10.1101/2021.06.14.448414

Cited by 4 publications

(5 citation statements)

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“…Some advanced methods of unsupervised learning, such as quantifying the data manifold curvature, require knowledge of data ID [64]. In mathematical modeling of biological and other complex systems, it is frequently important to estimate the effective dimensionality of the dynamical process, from the data or from simulations, in order to inform model reduction [17,65,66]. In medical applications and in the analysis of clinical data, knowledge of consensus data dimensionality was shown to be important to distinguish signal from noise and predict patient trajectories [16].…”

Section: Discussionmentioning

confidence: 99%

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Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Bac

Mirkes

Gorban

et al. 2021

Entropy

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Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.

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Section: Discussionmentioning

confidence: 99%

“…Scikit-dimension was applied in several recent studies for estimating the intrinsic dimensionality of real-life datasets [16,17].…”

Section: Introductionmentioning

confidence: 99%

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Bac

Mirkes

Gorban

et al. 2021

Entropy

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“…In a recent work [50] a motion separation approach based on independent component analysis was developed. Universal mathematical [51] and computational [52] models of cell cycle are useful for such separation. The developed methods for processing of single cell omics data open the door for detailed analysis of adaptation and stress at the single cell level.…”

Section: Extending Of Methods To New Phenomena and Problemsmentioning

confidence: 99%

It is useful to analyze correlation graphs

Gorban,

Tyukina,

Pokidysheva

et al. 2022

Preprint

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In 1987, we analyzed the changes in correlation graphs between various features of the organism during stress and adaptation. After 33 years of research of many authors, discoveries and rediscoveries, we can say with complete confidence: It is useful to analyze correlation graphs. In addition, we should add that the concept of adaptability ('adaptation energy') introduced by Selye is useful, especially if it is supplemented by 'adaptation entropy' and free energy, as well as an analysis of limiting factors. Our review of these topics, "Dynamic and Thermodynamic Adaptation Models" (Phys Life Rev, 2021, arXiv:2103.01959 [q-bio.OT]), attracted many comments from leading experts, with new ideas and new problems, from the dynamics of aging and the training of athletes to single-cell omics. Methodological backgrounds, like free energy analysis, were also discussed in depth. In this article, we provide an analytical overview of twelve commenting papers and some related publications.

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“…Gene expression modulation during cell cycle can be visualized and interpreted by looking at the so-called cell cycle plots. In these plots, each cell is reduced to a small set of coordinates (typically between 2 and 4 [23]), each of those corresponding to the average transcription activity of genes associated with a specific cell cycle signal (e.g. G1/S phase, G2/M phase, histones).…”

Section: Carrying Out Label Transfer To Predict Cell Cycle Phasesmentioning

confidence: 99%

transmorph: a unifying computational framework for single-cell data integration

Fouché

Chadoutaud

Delattre

et al. 2022

Preprint

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Data integration of single-cell data describes the task of embedding datasets obtained from different sources into a common space, so that cells with similar cell type or state end up close from one another in this representation independently from their dataset of origin. Data integration is a crucial early step in most data analysis pipelines involving multiple batches and allows informative data visualization, batch effect reduction, high resolution clustering, accurate label transfer and cell type inference. Many tools have been proposed over the last decade to tackle data integration, and some of them are routinely used today within data analysis workflows. Despite constant endeavors to conduct exhaustive benchmarking studies, a recent surge in the number of these methods has made it difficult to choose one objectively for a given use case. Furthermore, these tools are generally provided as rigid pieces of software allowing little to no user agency on their internal parameters and algorithms, which makes it hard to adapt them to a variety of use cases. In an attempt to address both of these issues at once we introduce transmorph, an ambitious unifying framework for data integration. It allows building complex data integration pipelines by combining existing and original algorithmic modules, and is supported by a rich software ecosystem to easily benchmark modules, analyze and report results. We demonstrate transmorph capabilities and the value of its expressiveness by solving a variety of practical single-cell applications including supervised and unsupervised joint datasets embedding, RNA-seq integration in gene space and label transfer of cell cycle phase within cell cycle genes space. We provide transmorph as a free, open source and computationally efficient python library, with a particular effort to make it compatible with the other state-of-the-art tools and workflows.

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Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches

Cited by 4 publications

References 52 publications

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

It is useful to analyze correlation graphs

transmorph: a unifying computational framework for single-cell data integration

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