Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Szedlak, Anthony; Sims, Spencer; Smith, Nicholas H.; Paternostro, Giovanni; Piermarocchi, Carlo

doi:10.1371/journal.pcbi.1005849

Cited by 16 publications

(12 citation statements)

References 96 publications

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“…Moreover, the large-scale application of our method to all the genes in a dataset could provide a new method, able to predict the future gene dynamic but also to infer regulatory modules. This work is also different from previous works that used Hopfield neural networks with a symmetric connectivity matrix to model the GRN dynamics storing the observed RNA-seq data as stationary states [ 67 , 68 ], because we actually constructed the dynamical model in order to predict the actual dynamics of the system and we used this model to classify different network structures.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Monti

Fiorentino

Milanetti

et al. 2022

Entropy

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Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.

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Section: Discussionmentioning

confidence: 99%

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Monti

Fiorentino

Milanetti

et al. 2022

Entropy

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“…These cluster analyses suggest common transcriptional regulatory mechanisms. Accordingly, the group 1 genes AURKB [ 18 ], E2F1 [ 19 – 21 ], CDKN2D [ 22 ], LIF [ 23 , 24 ], and ACLY [ 25 ] participate in cell cycle regulation, whereas the group 2 genes EFEMP1 [ 26 , 27 ], CD74 [ 24 , 28 – 30 ], and TGM2 [ 31 , 32 ] are involved in the regulation of chondrogenesis.…”

Section: Discussionmentioning

confidence: 99%

Genetic Markers Can Predict Chondrogenic Differentiation Potential in Bone Marrow-Derived Mesenchymal Stromal Cells

Kanawa

Igarashi²,

Fujimoto

et al. 2018

Stem Cells International

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The precise predictions of the differentiation direction and potential of mesenchymal stromal cells (MSCs) are an important key to the success of regenerative medicine. The expression levels of fate-determining genes may provide tools for predicting differentiation potential. The expression levels of 95 candidate marker genes and glycosaminoglycan (GAG) contents after chondrogenic induction in 10 undifferentiated ilium and 5 jaw MSC cultures were determined, and their correlations were analyzed. The expression levels of eight genes before the induction of chondrogenic MSC differentiation were significantly correlated with the GAG levels after induction. Based on correlation patterns, the eight genes were classified into two groups: group 1 genes (AURKB, E2F1, CDKN2D, LIF, and ACLY), related to cell cycle regulation, and group 2 genes (CD74, EFEMP1, and TGM2), involved in chondrogenesis. The expression levels of the group 2 genes were significantly correlated with the ages of the cell donors. The expression levels of CDKN2D, CD74, and TGM2 were >10-fold higher in highly potent MSCs (ilium MSCs) than in MSCs with limited potential (jaw MSCs). Three-dimensional (3D) scatter plot analyses of the expression levels of these genes showed reduced variability between donors and confirmed predictive potential. These data suggest that group 2 genes are involved in age-dependent decreases in the chondrogenic differentiation potential of MSCs, and combined 3D analyses of the expression profiles of three genes, including two group 2 genes, were predictive of MSC differentiation potential.

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“…This landscape can be explicitly visualized by starting at the equilibrium points corresponding to the attractor configurations and adding noise to sample their basins of attraction. The points sampled can then be represented in a 2D plot using principal components projections ( Szedlak et al, 2017 ; Maetschke & Ragan, 2014 ; Fard et al, 2016 ; Taherian Fard & Ragan, 2017 ). An example of output from this DCS visualization tool is in Fig.…”

Section: Methodsmentioning

confidence: 99%

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

Domanskyi

Hakansson

Bertus

et al. 2021

PeerJ

Self Cite

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Motivation Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis. Results We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. Availability The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377. Supplementary information Supplemental Materials are available at PeerJ online.

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Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Cited by 16 publications

References 96 publications

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Genetic Markers Can Predict Chondrogenic Differentiation Potential in Bone Marrow-Derived Mesenchymal Stromal Cells

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

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