Literature DB >> 26877824

A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.

Zhixiang Lin1, Stephan J Sanders2, Mingfeng Li1, Nenad Sestan1, Matthew W State2, Hongyu Zhao1.   

Abstract

Human neurodevelopment is a highly regulated biological process. In this article, we study the dynamic changes of neurodevelopment through the analysis of human brain microarray data, sampled from 16 brain regions in 15 time periods of neurodevelopment. We develop a two-step inferential procedure to identify expressed and unexpressed genes and to detect differentially expressed genes between adjacent time periods. Markov Random Field (MRF) models are used to efficiently utilize the information embedded in brain region similarity and temporal dependency in our approach. We develop and implement a Monte Carlo expectation-maximization (MCEM) algorithm to estimate the model parameters. Simulation studies suggest that our approach achieves lower misclassification error and potential gain in power compared with models not incorporating spatial similarity and temporal dependency.

Entities:  

Keywords:  Markov Random Field model; Monte Carlo expectation–maximization algorithm; differential expression; gene expression; microarray; neurodevelopment; spatial and temporal data

Year:  2015        PMID： 26877824      PMCID： PMC4751044          DOI： 10.1214/14-AOAS802

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN： 1932-6157            Impact factor:   2.083


  25 in total

1.  Significance analysis of time course microarray experiments.

Authors:  John D Storey; Wenzhong Xiao; Jeffrey T Leek; Ronald G Tompkins; Ronald W Davis
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-02       Impact factor: 11.205

2.  A Markov random field model for network-based analysis of genomic data.

Authors:  Zhi Wei; Hongzhe Li
Journal:  Bioinformatics       Date:  2007-05-05       Impact factor: 6.937

3.  Functional hierarchical models for identifying genes with different time-course expression profiles.

Authors:  F Hong; H Li
Journal:  Biometrics       Date:  2006-06       Impact factor: 2.571

4.  Network-based empirical Bayes methods for linear models with applications to genomic data.

Authors:  Caiyan Li; Zhi Wei; Hongzhe Li
Journal:  J Biopharm Stat       Date:  2010-03       Impact factor: 1.051

5.  Functional and evolutionary insights into human brain development through global transcriptome analysis.

Authors:  Matthew B Johnson; Yuka Imamura Kawasawa; Christopher E Mason; Zeljka Krsnik; Giovanni Coppola; Darko Bogdanović; Daniel H Geschwind; Shrikant M Mane; Matthew W State; Nenad Sestan
Journal:  Neuron       Date:  2009-05-28       Impact factor: 17.173

6.  Spatio-temporal transcriptome of the human brain.

Authors:  Hyo Jung Kang; Yuka Imamura Kawasawa; Feng Cheng; Ying Zhu; Xuming Xu; Mingfeng Li; André M M Sousa; Mihovil Pletikos; Kyle A Meyer; Goran Sedmak; Tobias Guennel; Yurae Shin; Matthew B Johnson; Zeljka Krsnik; Simone Mayer; Sofia Fertuzinhos; Sheila Umlauf; Steven N Lisgo; Alexander Vortmeyer; Daniel R Weinberger; Shrikant Mane; Thomas M Hyde; Anita Huttner; Mark Reimers; Joel E Kleinman; Nenad Sestan
Journal:  Nature       Date:  2011-10-26       Impact factor: 49.962

7.  Transcriptomic analysis of autistic brain reveals convergent molecular pathology.

Authors:  Irina Voineagu; Xinchen Wang; Patrick Johnston; Jennifer K Lowe; Yuan Tian; Steve Horvath; Jonathan Mill; Rita M Cantor; Benjamin J Blencowe; Daniel H Geschwind
Journal:  Nature       Date:  2011-05-25       Impact factor: 49.962

8.  Patterns and rates of exonic de novo mutations in autism spectrum disorders.

Authors:  Benjamin M Neale; Yan Kou; Li Liu; Avi Ma'ayan; Kaitlin E Samocha; Aniko Sabo; Chiao-Feng Lin; Christine Stevens; Li-San Wang; Vladimir Makarov; Paz Polak; Seungtai Yoon; Jared Maguire; Emily L Crawford; Nicholas G Campbell; Evan T Geller; Otto Valladares; Chad Schafer; Han Liu; Tuo Zhao; Guiqing Cai; Jayon Lihm; Ruth Dannenfelser; Omar Jabado; Zuleyma Peralta; Uma Nagaswamy; Donna Muzny; Jeffrey G Reid; Irene Newsham; Yuanqing Wu; Lora Lewis; Yi Han; Benjamin F Voight; Elaine Lim; Elizabeth Rossin; Andrew Kirby; Jason Flannick; Menachem Fromer; Khalid Shakir; Tim Fennell; Kiran Garimella; Eric Banks; Ryan Poplin; Stacey Gabriel; Mark DePristo; Jack R Wimbish; Braden E Boone; Shawn E Levy; Catalina Betancur; Shamil Sunyaev; Eric Boerwinkle; Joseph D Buxbaum; Edwin H Cook; Bernie Devlin; Richard A Gibbs; Kathryn Roeder; Gerard D Schellenberg; James S Sutcliffe; Mark J Daly
Journal:  Nature       Date:  2012-04-04       Impact factor: 49.962

9.  Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations.

Authors:  Brian J O'Roak; Laura Vives; Santhosh Girirajan; Emre Karakoc; Niklas Krumm; Bradley P Coe; Roie Levy; Arthur Ko; Choli Lee; Joshua D Smith; Emily H Turner; Ian B Stanaway; Benjamin Vernot; Maika Malig; Carl Baker; Beau Reilly; Joshua M Akey; Elhanan Borenstein; Mark J Rieder; Deborah A Nickerson; Raphael Bernier; Jay Shendure; Evan E Eichler
Journal:  Nature       Date:  2012-04-04       Impact factor: 49.962

10.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.

Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nucleic Acids Res       Date:  2008-11-25       Impact factor: 16.971
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  7 in total

1.  A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data.

Authors:  Zhixiang Lin; Mingfeng Li; Nenad Sestan; Hongyu Zhao
Journal:  Stat Appl Genet Mol Biol       Date:  2016-04

2.  On joint estimation of Gaussian graphical models for spatial and temporal data.

Authors:  Zhixiang Lin; Tao Wang; Can Yang; Hongyu Zhao
Journal:  Biometrics       Date:  2017-01-18       Impact factor: 2.571

3.  Simultaneous dimension reduction and adjustment for confounding variation.

Authors:  Zhixiang Lin; Can Yang; Ying Zhu; John Duchi; Yao Fu; Yong Wang; Bai Jiang; Mahdi Zamanighomi; Xuming Xu; Mingfeng Li; Nenad Sestan; Hongyu Zhao; Wing Hung Wong
Journal:  Proc Natl Acad Sci U S A       Date:  2016-12-07       Impact factor: 11.205

4.  Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.

Authors:  Mengmeng Wu; Zhixiang Lin; Shining Ma; Ting Chen; Rui Jiang; Wing Hung Wong
Journal:  J Mol Cell Biol       Date:  2017-12-01       Impact factor: 6.216

5.  A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.

Authors:  Zhixiang Lin; Stephan J Sanders; Mingfeng Li; Nenad Sestan; Matthew W State; Hongyu Zhao
Journal:  Ann Appl Stat       Date:  2015-03       Impact factor: 2.083

6.  A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data.

Authors:  Hongyu Li; Biqing Zhu; Zhichao Xu; Taylor Adams; Naftali Kaminski; Hongyu Zhao
Journal:  BMC Bioinformatics       Date:  2021-10-26       Impact factor: 3.169

Review 7.  Reflections on the genetics-first approach to advancements in molecular genetic and neurobiological research on neurodevelopmental disorders.

Authors:  Anne B Arnett; Tianyun Wang; Evan E Eichler; Raphael A Bernier
Journal:  J Neurodev Disord       Date:  2021-06-21       Impact factor: 4.025
  7 in total

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