Gene regulatory network inference in single-cell biology
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