Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data
暂无分享,去创建一个
[1] G. Bain,et al. Characterization of primary human skeletal muscle cells from multiple commercial sources , 2013, In Vitro Cellular & Developmental Biology - Animal.
[2] Hongkai Ji,et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis , 2016, Nucleic acids research.
[3] Hannah A. Pliner,et al. Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.
[4] Xuelong Li,et al. A survey of graph edit distance , 2010, Pattern Analysis and Applications.
[5] J. Lee,et al. Single-cell RNA sequencing technologies and bioinformatics pipelines , 2018, Experimental & Molecular Medicine.
[6] Vanesa Nieto-Estévez,et al. IGF-I: A Key Growth Factor that Regulates Neurogenesis and Synaptogenesis from Embryonic to Adult Stages of the Brain , 2016, Front. Neurosci..
[7] Yvan Saeys,et al. A comparison of single-cell trajectory inference methods , 2019, Nature Biotechnology.
[8] Chris Wiggins,et al. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.
[9] J. Marioni,et al. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.
[10] Russell B. Fletcher,et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics , 2017, BMC Genomics.
[11] Yu Xin Wang,et al. Building muscle: molecular regulation of myogenesis. , 2012, Cold Spring Harbor perspectives in biology.
[12] Fabian J Theis,et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2019, Genome Biology.
[13] Olivier Pourquié,et al. Making muscle: skeletal myogenesis in vivo and in vitro , 2017, Development.
[14] Justin Guinney,et al. GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.
[15] B. Olwin,et al. Differentially expressed fibroblast growth factors regulate skeletal muscle development through autocrine and paracrine mechanisms , 1996, The Journal of cell biology.
[16] A. Martelli,et al. Expression of phospholipase C beta family isoenzymes in C2C12 myoblasts during terminal differentiation , 2004, Journal of cellular physiology.
[17] Lu Wen,et al. Single-Cell Transcriptome Analysis Maps the Developmental Track of the Human Heart. , 2019, Cell reports.
[18] M. Sheng,et al. Regulated Expression and Subcellular Localization of Syndecan Heparan Sulfate Proteoglycans and the Syndecan-Binding Protein CASK/LIN-2 during Rat Brain Development , 1999, The Journal of Neuroscience.
[19] Lu Wen,et al. Dissecting the Global Dynamic Molecular Profiles of Human Fetal Kidney Development by Single-Cell RNA Sequencing. , 2018, Cell reports.
[20] Marko Repic,et al. Proliferation control in neural stem and progenitor cells , 2015, Nature Reviews Neuroscience.
[21] Gary D Bader,et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap , 2019, Nature Protocols.
[22] J. L. Swain,et al. FGF receptor availability regulates skeletal myogenesis. , 1999, Experimental Cell Research.
[23] Zhisong He,et al. Identification and characterization of functional modules reflecting transcriptome transition during human neuron maturation , 2017, bioRxiv.
[24] Gary D Bader,et al. Developmental Emergence of Adult Neural Stem Cells as Revealed by Single-Cell Transcriptional Profiling. , 2017, Cell reports.
[25] Z. Bar-Joseph,et al. Reconstructing differentiation networks and their regulation from time series single-cell expression data , 2018, Genome research.
[26] Cole Trapnell,et al. Defining cell types and states with single-cell genomics , 2015, Genome research.
[27] Caleb Weinreb,et al. Fundamental limits on dynamic inference from single-cell snapshots , 2017, Proceedings of the National Academy of Sciences.
[28] Cole Trapnell,et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.
[29] François Guillemot,et al. Molecular control of neurogenesis: a view from the mammalian cerebral cortex. , 2012, Cold Spring Harbor perspectives in biology.
[30] I. Simon,et al. Studying and modelling dynamic biological processes using time-series gene expression data , 2012, Nature Reviews Genetics.
[31] M. Hemberg,et al. Challenges in unsupervised clustering of single-cell RNA-seq data , 2019, Nature Reviews Genetics.
[32] L. Haupt,et al. Exploiting Heparan Sulfate Proteoglycans in Human Neurogenesis—Controlling Lineage Specification and Fate , 2017, Front. Integr. Neurosci..
[33] D. Hartfiel,et al. Understanding , 2003 .
[34] Mitsuhiko Toda,et al. Methods for Visual Understanding of Hierarchical System Structures , 1981, IEEE Transactions on Systems, Man, and Cybernetics.
[35] Pavithra Kumar,et al. Understanding development and stem cells using single cell-based analyses of gene expression , 2017, Development.
[36] Gary D Bader,et al. scClustViz – Single-cell RNAseq cluster assessment and visualization , 2018, F1000Research.
[37] P. Jap,et al. Differentiation of human skeletal muscle cells in culture: maturation as indicated by titin and desmin striation , 1992, Cell and Tissue Research.
[38] M. Ashburner,et al. An ontology for cell types , 2005, Genome Biology.
[39] Mauro J. Muraro,et al. De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.
[40] H. T. Ghashghaei,et al. Neural Stem Cells to Cerebral Cortex: Emerging Mechanisms Regulating Progenitor Behavior and Productivity , 2016, The Journal of Neuroscience.