Gene regulatory network inference in single-cell biology

Current Opinion in Systems Biology - Tập 26 - Trang 87-97 - 2021
Kyle Akers1, T.M. Murali2
1Interdisciplinary Ph.D. Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, VA 24061, USA
2Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA

Tài liệu tham khảo

Wagner, 2016, Revealing the vectors of cellular identity with single-cell genomics, Nat Biotechnol, 34, 1145, 10.1038/nbt.3711 Marbach, 2012, Wisdom of crowds for robust gene network inference, Nat Methods, 9, 796, 10.1038/nmeth.2016 Tang, 2009, mRNA-Seq whole-transcriptome analysis of a single cell, Nat Methods, 6, 377, 10.1038/nmeth.1315 Hu, 2020, Integration of single-cell multi-omics for gene regulatory network inference, Comput Struct Biotechnol J, 18, 1925, 10.1016/j.csbj.2020.06.033 Trapnell, 2014, The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells, Nat Biotechnol, 32, 381, 10.1038/nbt.2859 Kim, 2015, Ppcor: an R package for a fast calculation to semi-partial correlation coefficients, Commun Stat Appl Methods, 22, 665 Specht, 2017, LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering, Bioinformatics, 33, 764, 10.1093/bioinformatics/btw729 Chan, 2017, Gene regulatory network inference from single-cell data using multivariate information measures, Cell Syst, 5, 251, 10.1016/j.cels.2017.08.014 Qiu, 2020, Inferring causal gene regulatory networks from coupled single-cell expression dynamics using Scribe, Cell Syst, 10, 265, 10.1016/j.cels.2020.02.003 Huynh-Thu, 2010, Inferring regulatory networks from expression data using tree-based methods, PloS One, 5, 10.1371/journal.pone.0012776 Moerman, 2019, GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks, Bioinformatics, 35, 2159, 10.1093/bioinformatics/bty916 Papili Gao, 2018, SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles, Bioinformatics, 34, 258, 10.1093/bioinformatics/btx575 Sanchez-Castillo, 2018, A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data, Bioinformatics, 34, 964, 10.1093/bioinformatics/btx605 Sekula, 2020, A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data, BMC Bioinf, 21, 361, 10.1186/s12859-020-03707-y Woodhouse, 2018, SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data, BMC Syst Biol, 12, 59, 10.1186/s12918-018-0581-y Malekpour, 2020, LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks, BMC Bioinf, 21, 318, 10.1186/s12859-020-03651-x Matsumoto, 2017, SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation, Bioinformatics, 33, 2314, 10.1093/bioinformatics/btx194 Aubin-Frankowski, 2020, Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference, Bioinformatics, 36, 4774, 10.1093/bioinformatics/btaa576 Huynh-Thu, 2018, dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data, Sci Rep, 8, 3384, 10.1038/s41598-018-21715-0 Herbach, 2017, Inferring gene regulatory networks from single-cell data: a mechanistic approach, BMC Syst Biol, 11, 105, 10.1186/s12918-017-0487-0 Bonnaffoux, 2019, WASABI: a dynamic iterative framework for gene regulatory network inference, BMC Bioinf, 20, 220, 10.1186/s12859-019-2798-1 Aibar, 2017, SCENIC: single-cell regulatory network inference and clustering, Nat Methods, 14, 1083, 10.1038/nmeth.4463 Van de Sande, 2020, A scalable SCENIC workflow for single-cell gene regulatory network analysis, Nat Protoc, 15, 2247, 10.1038/s41596-020-0336-2 Dai, 2019, Cell-specific network constructed by single-cell RNA sequencing data, Nucleic Acids Res, 47, e62, 10.1093/nar/gkz172 Wu, 2020, Joint learning of multiple gene networks from single-cell gene expression data, Comput Struct Biotechnol J, 18, 2583, 10.1016/j.csbj.2020.09.004 Buenrostro, 2015, Single-cell chromatin accessibility reveals principles of regulatory variation, Nature, 523, 486, 10.1038/nature14590 Rotem, 2015, Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state, Nat Biotechnol, 33, 1165, 10.1038/nbt.3383 Ku, 2019, Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification, Nat Methods, 16, 323, 10.1038/s41592-019-0361-7 Han, 2017, Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells, Nucleic Acids Res, 45, e77 Clark, 2017, Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq), Nat Protoc, 12, 534, 10.1038/nprot.2016.187 Budnik, 2018, SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation, Genome Biol, 19, 161, 10.1186/s13059-018-1547-5 Stuart, 2019, Comprehensive integration of single-cell data, Cell, 177, 1888, 10.1016/j.cell.2019.05.031 Wang, 2020, Integrative analyses of single-cell transcriptome and regulome using MAESTRO, Genome Biol, 21, 198, 10.1186/s13059-020-02116-x Welch, 2019, Single-cell multi-omic integration compares and contrasts features of brain cell identity, Cell, 177, 1873, 10.1016/j.cell.2019.05.006 Duren, 2018, Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations, Proc Natl Acad Sci U S A, 115, 7723, 10.1073/pnas.1805681115 Chen, 2018, Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data, BMC Bioinf, 19, 232, 10.1186/s12859-018-2217-z Pratapa, 2020, Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data, Nat Methods, 17, 147, 10.1038/s41592-019-0690-6 Han, 2018, TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions, Nucleic Acids Res, 46, D380, 10.1093/nar/gkx1013 Garcia-Alonso, 2019, Benchmark and integration of resources for the estimation of human transcription factor activities, Genome Res, 29, 1363, 10.1101/gr.240663.118 Yuan, 2019, Deep learning for inferring gene relationships from single-cell expression data, Proc Natl Acad Sci U S A, 116, 27151, 10.1073/pnas.1911536116 Schaffter, 2011, GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods, Bioinformatics, 27, 2263, 10.1093/bioinformatics/btr373 H. Nguyen, D. Tran, B. Tran, B. Pehlivan, T. Nguyen, A comprehensive survey of regulatory network inference methods using single-cell RNA sequencing data, Brief Bioinform, 2020, https://doi.org/10.1093/bib/bbaa190. Zappia, 2017, Splatter: simulation of single-cell RNA sequencing data, Genome Biol, 18, 174, 10.1186/s13059-017-1305-0 Dibaeinia, 2020, SERGIO: a single-cell expression simulator guided by gene regulatory networks, Cell Syst, 11, 252, 10.1016/j.cels.2020.08.003 Saito, 2015, The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PloS One, 10, 10.1371/journal.pone.0118432 Clark, 2018, scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells, Nat Commun, 9, 781, 10.1038/s41467-018-03149-4 Chen, 2019, High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell, Nat Biotechnol, 37, 1452, 10.1038/s41587-019-0290-0 Cao, 2018, Joint profiling of chromatin accessibility and gene expression in thousands of single cells, Science, 361, 1380, 10.1126/science.aau0730 Hu, 2016, Simultaneous profiling of transcriptome and DNA methylome from a single cell, Genome Biol, 17, 88, 10.1186/s13059-016-0950-z Angermueller, 2016, Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity, Nat Methods, 13, 229, 10.1038/nmeth.3728 Gerlach, 2019, Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells, Sci Rep, 9, 1469, 10.1038/s41598-018-37977-7 Stoeckius, 2017, Simultaneous epitope and transcriptome measurement in single cells, Nat Methods, 14, 865, 10.1038/nmeth.4380 Peterson, 2017, Multiplexed quantification of proteins and transcripts in single cells, Nat Biotechnol, 35, 936, 10.1038/nbt.3973 Macaulay, 2015, G&T-seq: parallel sequencing of single-cell genomes and transcriptomes, Nat Methods, 12, 519, 10.1038/nmeth.3370 Dey, 2015, Integrated genome and transcriptome sequencing of the same cell, Nat Biotechnol, 33, 285, 10.1038/nbt.3129 Han, 2018, SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells, Genome Res, 28, 75, 10.1101/gr.223263.117 Rodriguez-Meira, 2019, Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing, Mol Cell, 73, 1292, 10.1016/j.molcel.2019.01.009 Qiu, 2020, Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq, Nat Methods, 17, 991, 10.1038/s41592-020-0935-4 Jaitin, 2016, Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq, Cell, 167, 1883, 10.1016/j.cell.2016.11.039 Dixit, 2016, Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens, Cell, 167, 1853, 10.1016/j.cell.2016.11.038 Datlinger, 2017, Pooled CRISPR screening with single-cell transcriptome readout, Nat Methods, 14, 297, 10.1038/nmeth.4177 Rubin, 2019, Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks, Cell, 176, 361, 10.1016/j.cell.2018.11.022 Yang, 2020, scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens, Genome Biol, 21, 19, 10.1186/s13059-020-1928-4 Mimitou, 2019, Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells, Nat Methods, 16, 409, 10.1038/s41592-019-0392-0 Turki, 2020, SCGRNs: novel supervised inference of single-cell gene regulatory networks of complex diseases, Comput Biol Med, 118, 103656, 10.1016/j.compbiomed.2020.103656 Yuan, 2020, GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data, Genome Biol, 21, 300, 10.1186/s13059-020-02214-w