Literature DB >> 27179027

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

Zhicheng Ji1, Hongkai Ji2.   

Abstract

When analyzing single-cell RNA-seq data, constructing a pseudo-temporal path to order cells based on the gradual transition of their transcriptomes is a useful way to study gene expression dynamics in a heterogeneous cell population. Currently, a limited number of computational tools are available for this task, and quantitative methods for comparing different tools are lacking. Tools for Single Cell Analysis (TSCAN) is a software tool developed to better support in silico pseudo-Time reconstruction in Single-Cell RNA-seq ANalysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods. TSCAN is available at https://github.com/zji90/TSCAN and as a Bioconductor package.
© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Mesh:

Substances:

Year:  2016        PMID: 27179027      PMCID: PMC4994863          DOI: 10.1093/nar/gkw430

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  22 in total

1.  Mapping and quantifying mammalian transcriptomes by RNA-Seq.

Authors:  Ali Mortazavi; Brian A Williams; Kenneth McCue; Lorian Schaeffer; Barbara Wold
Journal:  Nat Methods       Date:  2008-05-30       Impact factor: 28.547

2.  Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development.

Authors:  Sean C Bendall; Kara L Davis; El-Ad David Amir; Michelle D Tadmor; Erin F Simonds; Tiffany J Chen; Daniel K Shenfeld; Garry P Nolan; Dana Pe'er
Journal:  Cell       Date:  2014-04-24       Impact factor: 41.582

3.  Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape.

Authors:  Eugenio Marco; Robert L Karp; Guoji Guo; Paul Robson; Adam H Hart; Lorenzo Trippa; Guo-Cheng Yuan
Journal:  Proc Natl Acad Sci U S A       Date:  2014-12-15       Impact factor: 11.205

4.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

5.  Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis.

Authors:  Fuchou Tang; Catalin Barbacioru; Siqin Bao; Caroline Lee; Ellen Nordman; Xiaohui Wang; Kaiqin Lao; M Azim Surani
Journal:  Cell Stem Cell       Date:  2010-05-07       Impact factor: 24.633

Review 6.  RNA-Seq: a revolutionary tool for transcriptomics.

Authors:  Zhong Wang; Mark Gerstein; Michael Snyder
Journal:  Nat Rev Genet       Date:  2009-01       Impact factor: 53.242

7.  Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.

Authors:  Diego Adhemar Jaitin; Ephraim Kenigsberg; Hadas Keren-Shaul; Naama Elefant; Franziska Paul; Irina Zaretsky; Alexander Mildner; Nadav Cohen; Steffen Jung; Amos Tanay; Ido Amit
Journal:  Science       Date:  2014-02-14       Impact factor: 47.728

8.  Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE.

Authors:  Peng Qiu; Erin F Simonds; Sean C Bendall; Kenneth D Gibbs; Robert V Bruggner; Michael D Linderman; Karen Sachs; Garry P Nolan; Sylvia K Plevritis
Journal:  Nat Biotechnol       Date:  2011-10-02       Impact factor: 54.908

9.  Bayesian approach to single-cell differential expression analysis.

Authors:  Peter V Kharchenko; Lev Silberstein; David T Scadden
Journal:  Nat Methods       Date:  2014-05-18       Impact factor: 28.547

10.  Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses.

Authors:  Ido Amit; Manuel Garber; Nicolas Chevrier; Ana Paula Leite; Yoni Donner; Thomas Eisenhaure; Mitchell Guttman; Jennifer K Grenier; Weibo Li; Or Zuk; Lisa A Schubert; Brian Birditt; Tal Shay; Alon Goren; Xiaolan Zhang; Zachary Smith; Raquel Deering; Rebecca C McDonald; Moran Cabili; Bradley E Bernstein; John L Rinn; Alex Meissner; David E Root; Nir Hacohen; Aviv Regev
Journal:  Science       Date:  2009-09-03       Impact factor: 47.728

View more
  177 in total

1.  Reconstructing complex lineage trees from scRNA-seq data using MERLoT.

Authors:  R Gonzalo Parra; Nikolaos Papadopoulos; Laura Ahumada-Arranz; Jakob El Kholtei; Noah Mottelson; Yehor Horokhovsky; Barbara Treutlein; Johannes Soeding
Journal:  Nucleic Acids Res       Date:  2019-09-26       Impact factor: 16.971

2.  Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq.

Authors:  Weiqiang Zhou; Zhicheng Ji; Weixiang Fang; Hongkai Ji
Journal:  Nucleic Acids Res       Date:  2019-11-04       Impact factor: 16.971

3.  TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data.

Authors:  Jiang Xie; Yiting Yin; Jiao Wang
Journal:  Interdiscip Sci       Date:  2021-06-09       Impact factor: 2.233

Review 4.  Understanding development and stem cells using single cell-based analyses of gene expression.

Authors:  Pavithra Kumar; Yuqi Tan; Patrick Cahan
Journal:  Development       Date:  2017-01-01       Impact factor: 6.868

Review 5.  Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics.

Authors:  Sabyasachi Dasgupta; Gary D Bader; Sidhartha Goyal
Journal:  Biophys J       Date:  2018-07-11       Impact factor: 4.033

6.  Cell lineage and communication network inference via optimization for single-cell transcriptomics.

Authors:  Shuxiong Wang; Matthew Karikomi; Adam L MacLean; Qing Nie
Journal:  Nucleic Acids Res       Date:  2019-06-20       Impact factor: 16.971

7.  scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes.

Authors:  Xiaoying Zheng; Suoqin Jin; Qing Nie; Xiufen Zou
Journal:  IEEE Trans Biomed Eng       Date:  2019-08-23       Impact factor: 4.538

Review 8.  Creating Lineage Trajectory Maps Via Integration of Single-Cell RNA-Sequencing and Lineage Tracing: Integrating transgenic lineage tracing and single-cell RNA-sequencing is a robust approach for mapping developmental lineage trajectories and cell fate changes.

Authors:  Russell B Fletcher; Diya Das; John Ngai
Journal:  Bioessays       Date:  2018-06-26       Impact factor: 4.345

9.  Single-Cell Transcriptomics in Medulloblastoma Reveals Tumor-Initiating Progenitors and Oncogenic Cascades during Tumorigenesis and Relapse.

Authors:  Liguo Zhang; Xuelian He; Xuezhao Liu; Feng Zhang; L Frank Huang; Andrew S Potter; Lingli Xu; Wenhao Zhou; Tao Zheng; Zaili Luo; Kalen P Berry; Allison Pribnow; Stephanie M Smith; Christine Fuller; Blaise V Jones; Maryam Fouladi; Rachid Drissi; Zeng-Jie Yang; W Clay Gustafson; Marc Remke; Scott L Pomeroy; Emily J Girard; James M Olson; A Sorana Morrissy; Maria C Vladoiu; Jiao Zhang; Weidong Tian; Mei Xin; Michael D Taylor; S Steven Potter; Martine F Roussel; William A Weiss; Q Richard Lu
Journal:  Cancer Cell       Date:  2019-08-29       Impact factor: 31.743

10.  Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

Authors:  Zheng Kuang; Zhicheng Ji; Jef D Boeke; Hongkai Ji
Journal:  Nucleic Acids Res       Date:  2018-01-09       Impact factor: 16.971

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.