Dataset Information

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.

ABSTRACT: BACKGROUND:Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. RESULTS:We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. CONCLUSIONS:We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

SUBMITTER: Parca L

PROVIDER: S-EPMC7576925 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.

Parca Luca L Truglio Mauro M Biagini Tommaso T Castellana Stefano S Petrizzelli Francesco F Capocefalo Daniele D Jordán Ferenc F Carella Massimo M Mazza Tommaso T

GigaScience 20201001 10

<h4>Background</h4>Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task.<h4>Results</h4>We present Pyntacle, a h ...[more]

PMID: 33084878

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Project description:BackgroundSingle-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.ResultsIn order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing.ConclusionsTaken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.

Dataset Information

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.

Publications

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.

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