High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.
High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.
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DOI：
10.1093/bioinformatics/btac117
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发表时间：
2022-04-28
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基因调控网络定义了生物系统中转录因子和靶基因之间的调控关系，重建它们对于理解细胞生长和功能至关重要。根据基因组数据推断和重建网络的方法在过去十年中迅速发展，以响应测序技术和机器学习的进步。数据收集的规模急剧增加;最大的全基因组基因表达数据集已经从数千个测量增长到数百万个单细胞，新技术即将增加到数千万个细胞及以上。在这项工作中，我们提出了Inferelator 3.0，它已进行了重大更新，以整合来自不同细胞类型的数据，以学习特定于上下文的调控网络，并将其聚合到一个共享的调控网络中，同时保留了以前版本的功能。Inferelator能够整合最大的单细胞数据集，并学习细胞类型特异性基因调控网络。与其他网络推理方法相比，Inferelator从单细胞基因表达数据中学习新的信息丰富的酿酒酵母网络，通过恢复已知的金标准来测量。我们通过从一个大型（130万）单细胞基因表达数据集（具有配对的单细胞染色质可及性数据）中学习E18发育中小家鼠大脑中多种不同神经元和神经胶质细胞类型的网络来展示其扩展能力。Inferrelator软件在GitHub（https：//github.com/flatironinstitute/inferrelator）上以MIT许可证提供，并已作为python软件包发布，附带相关文档（https：//inferrelator.readthedocs.io/）。 补充数据可在Bioinformatics在线获得。
Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). Supplementary data are available at Bioinformatics online.
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影响因子：
9.9
作者：
Arrieta-Ortiz ML;Hafemeister C;Bate AR;Chu T;Greenfield A;Shuster B;Barry SN;Gallitto M;Liu B;Kacmarczyk T;Santoriello F;Chen J;Rodrigues CD;Sato T;Rudner DZ;Driks A;Bonneau R;Eichenberger P
通讯作者：
Eichenberger P
影响因子：
12.3
作者：
Mehta TK;Koch C;Nash W;Knaack SA;Sudhakar P;Olbei M;Bastkowski S;Penso-Dolfin L;Korcsmaros T;Haerty W;Roy S;Di-Palma F
通讯作者：
Di-Palma F
影响因子：
3
作者：
Chen S;Mar JC
通讯作者：
Mar JC
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
64.5
作者：
Dixit, Atray;Pamas, Oren;Li, Biyu;Chen, Jenny;Fulco, Charles P.;Jerby-Amon, Livnat;Marjanovic, Nemanja D.;Dionne, Danielle;Burks, Tyler;Raychowdhury, Raktima;Adamson, Britt;Norman, Thomas M.;Lander, Eric S.;Weissman, Jonathan S.;Friedman, Nir;Regev, Aviv
通讯作者：
Regev, Aviv