Faculty Opinions recommendation of SCENIC: single-cell regulatory network inference and clustering.

2017 ◽  
Author(s):  
Chao Cheng
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Network Inference

Single-cell regulatory network inference and clustering from high-dimensional sequencing data

2019 ◽  
Author(s):  
Aristidis G. Vrahatis ◽  
Georgios N. Dimitrakopoulos ◽  
Sotiris K. Tasoulis ◽  
Spiros V. Georgakopoulos ◽  
Vassilis P. Plagianakos
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
High Dimensional ◽  
Sequencing Data

scholarly journals Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

2020 ◽  
Vol 17 (2) ◽  
pp. 147-154 ◽  
Author(s):  
Aditya Pratapa ◽  
Amogh P. Jalihal ◽  
Jeffrey N. Law ◽  
Aditya Bharadwaj ◽  
T. M. Murali
Keyword(s):  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Transcriptomic Data ◽  
Gene Regulatory

scholarly journals SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

2017 ◽  
Vol 33 (15) ◽  
pp. 2314-2321 ◽  
Author(s):  
Hirotaka Matsumoto ◽  
Hisanori Kiryu ◽  
Chikara Furusawa ◽  
Minoru S H Ko ◽  
Shigeru B H Ko ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Inference Algorithm ◽  
Rna Seq

scholarly journals SCENIC: Single-cell regulatory network inference and clustering

2017 ◽  
Author(s):  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Thomas Moerman ◽  
Jasper Wouters ◽  
Vân Anh Huynh-Thu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Target Genes ◽  
Single Cells ◽  
State Transitions ◽  
Rna Seq ◽  
Cell State ◽  
Gene Regulatory

AbstractSingle-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

scholarly journals WASABI: a dynamic iterative framework for gene regulatory network inference

2018 ◽  
Author(s):  
Arnaud Bonnaffoux ◽  
Ulysse Herbach ◽  
Angélique Richard ◽  
Anissa Guillemin ◽  
Sandrine Giraud ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Molecular Mechanisms ◽  
Gene Regulatory Network Inference ◽  
Piecewise Deterministic Markov Processes ◽  
Gene Regulatory ◽  
Cell Data

AbstractInference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. In conclusion, WASABI is a versatile algorithm which should help biologists to fully exploit the power of time-stamped single-cell data.

scholarly journals A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data

2020 ◽  
Author(s):  
Hung Nguyen ◽  
Duc Tran ◽  
Bang Tran ◽  
Bahadir Pehlivan ◽  
Tin Nguyen
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Comprehensive Survey ◽  
Living Organisms ◽  
The Individual ◽  
Inference Techniques ◽  
Inference Methods

Abstract Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods’ performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.

scholarly journals Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

2021 ◽  
Author(s):  
Matthew Stone ◽  
Sunnie Grace McCalla ◽  
Alireza Fotuhi Siahpirani ◽  
Viswesh Periyasamy ◽  
Junha Shin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Cost Effective ◽  
Gene Regulatory Network Inference ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory ◽  
Inference Methods

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional pro- grams of different cellular states by measuring the transcriptome of thousands individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory net- works and a number of methods with different learning frameworks have been developed. Here we present a expanded benchmarking study of eleven recent network inference methods on six published single-cell RNA-sequencing datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that while no method is a universal winner and most methods have a modest recovery of experimentally derived interactions based on global metrics such as AUPR, methods are able to capture targets of regulators that are relevant to the system under study. Based on overall performance we grouped the methods into three main categories and found a combination of information-theoretic and regression-based methods to have a generally high perfor- mance. We also evaluate the utility of imputation for gene regulatory network inference and find that a small number of methods benefit from imputation, which further depends upon the dataset. Finally, comparisons to inferred networks for comparable bulk conditions showed that networks inferred from scRNA-seq datasets are often better or at par to those from bulk suggesting that scRNA-seq datasets can be a cost-effective way for gene regulatory network inference. Our analysis should be beneficial in selecting algorithms for performing network inference but also argues for improved methods and better gold standards for accurate assessment of regulatory network inference methods for mammalian systems.

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