Unsupervised Inference of Developmental Directions for Single Cells Using VECTOR.

A key step in trajectory inference is the determination of starting cells, which is typically done by using manually selected marker genes. In this study, we find that the quantile polarization of a cell's principal-component values is strongly associated with their respective states in development hierarchy, and therefore provides an unsupervised solution for determining the starting cells. Based on this finding, we developed a tool named VECTOR that infers vectors of developmental directions for cells in Uniform Manifold Approximation and Projection (UMAP). In seven datasets of different developmental scenarios, VECTOR correctly identifies the starting cells and successfully infers the vectors of developmental directions. VECTOR is freely available for academic use at https://github.com/jumphone/Vector.

[1]  W. Liu,et al.  Identification of key factors conquering developmental arrest of somatic cell cloned embryos by combining embryo biopsy and single-cell sequencing , 2016, Cell Discovery.

[2]  Kerstin B. Meyer,et al.  BBKNN: fast batch alignment of single cell transcriptomes , 2019, Bioinform..

[3]  Hannah A. Pliner,et al.  Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.

[4]  Erik Sundström,et al.  RNA velocity of single cells , 2018, Nature.

[5]  J. Marioni,et al.  Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos , 2016, Cell.

[6]  William M. Mauck,et al.  A single cell transcriptional roadmap for cardiopharyngeal fate diversification , 2017, bioRxiv.

[7]  Russell B. Fletcher,et al.  Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics , 2017, BMC Genomics.

[8]  Igor Adameyko,et al.  Multipotent peripheral glial cells generate neuroendocrine cells of the adrenal medulla , 2017, Science.

[9]  B. Lahn,et al.  Nestin Is Required for the Proper Self‐Renewal of Neural Stem Cells , 2010, Stem cells.

[10]  M. Sofroniew,et al.  Astrocytes: biology and pathology , 2009, Acta Neuropathologica.

[11]  Fabian J Theis,et al.  PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2019, Genome Biology.

[12]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[13]  R. Lovell-Badge,et al.  SOX9 induces and maintains neural stem cells , 2010, Nature Neuroscience.

[14]  Yarden Katz,et al.  A single-cell survey of the small intestinal epithelium , 2017, Nature.

[15]  S. Linnarsson,et al.  Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing , 2018, Nature Neuroscience.

[16]  Blair R. Leavitt,et al.  Induction of neurogenesis in the neocortex of adult mice , 2000, Nature.

[17]  Viktor Petukhov,et al.  dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments , 2018, Genome Biology.

[18]  W. Macklin,et al.  Olig1 Function Is Required for Oligodendrocyte Differentiation in the Mouse Brain , 2015, The Journal of Neuroscience.

[19]  Paul J. Hoffman,et al.  Comprehensive Integration of Single-Cell Data , 2018, Cell.

[20]  J. Marioni,et al.  Using single‐cell genomics to understand developmental processes and cell fate decisions , 2018, Molecular systems biology.

[21]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[22]  S. Horvath,et al.  Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing , 2013, Nature.

[23]  Wei Xie,et al.  The landscape of accessible chromatin in mammalian preimplantation embryos , 2016, Nature.

[24]  Sarah A Teichmann,et al.  A test metric for assessing single-cell RNA-seq batch correction , 2018, Nature Methods.

[25]  Weidong Tian,et al.  A novel approach to remove the batch effect of single-cell data , 2019, Cell Discovery.

[26]  J. Nichols,et al.  Lineage-Specific Profiling Delineates the Emergence and Progression of Naive Pluripotency in Mammalian Embryogenesis , 2015, Developmental cell.

[27]  R. Sandberg,et al.  Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.

[28]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[29]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[30]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[31]  Hongshan Guo,et al.  Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos , 2015, Genome Biology.

[32]  Andrew J. Hill,et al.  The single cell transcriptional landscape of mammalian organogenesis , 2019, Nature.

[33]  I. Duncan,et al.  A mutation in the Tubb4a gene leads to microtubule accumulation with hypomyelination and demyelination , 2017, Annals of neurology.

[34]  Chika Yokota,et al.  Spatiotemporal structure of cell fate decisions in murine neural crest , 2019, Science.

[35]  Jens Hjerling-Leffler,et al.  Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system , 2016, Science.

[36]  Yvan Saeys,et al.  A comparison of single-cell trajectory inference methods , 2019, Nature Biotechnology.

[37]  F. Biase,et al.  Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing , 2014, Genome research.

[38]  R. Satija,et al.  Integrative single-cell analysis , 2019, Nature Reviews Genetics.