Time-course microarray transcriptome data of in vitro cultured testes and age-matched in vivo testes

被引:2
|
作者
Abe, Takeru [1 ,2 ]
Nishimura, Hajime [2 ]
Sato, Takuya [1 ]
Suzuki, Harukazu [2 ]
Ogawa, Takehiko [1 ]
Suzuki, Takahiro [2 ,3 ]
机构
[1] Yokohama City Univ, Grad Sch Med Life Sci, Biopharmaceut & Regenerat Sci, Yokohama, Kanagawa, Japan
[2] RIKEN Ctr Integrat Med Sci, Lab Cellular Funct Convers Technol, Yokohama, Kanagawa, Japan
[3] Yokohama City Univ, Grad Sch Med Life Sci, Funct Genom, Yokohama, Kanagawa, Japan
来源
DATA IN BRIEF | 2020年 / 33卷
基金
日本学术振兴会;
关键词
Spermatogenesis; Organ culture; Transcriptome; Microarray;
D O I
10.1016/j.dib.2020.106482
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In vitro spermatogenesis, which produces fertile spermatozoa, has been successfully performed using an organ culture method from murine tissue. Here, we provide a dataset of time-course microarray transcriptome data of in vitro cultured neonate murine testes and age-matched in vivo-derived testes. The dataset presented here is related to the article titled "Transcriptome analysis reveals inadequate spermatogenesis and immediate radical immune reactions during organ culture in vitro spermatogenesis" published in Biochemical and Biophysical Research Communications in 2020 [1]. The raw data and pre-processed data are publicly available on the GEO repository (accession number GSE147982). Furthermore, the dataset provided here includes additional metadata, detailed explanations of the experiment, results of pre-processing, analysis scripts, and lists of differentially expressed genes from in vitro culture testes and in vivo testes at each time point. (C) 2020 The Authors. Published by Elsevier Inc.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions
    Tsai, Guei-Feng
    Qu, Annie
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2008, 52 (03) : 1387 - 1398
  • [32] A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data
    Thomas, Reuben
    Paredes, Carlos J.
    Mehrotra, Sanjay
    Hatzimanikatis, Vassily
    Papoutsakis, Eleftherios T.
    BMC BIOINFORMATICS, 2007, 8
  • [33] Dynamic Gene Regulatory Network Analysis Using Saccharomyces cerevisiae Large-Scale Time-Course Microarray Data
    Zhang, L.
    Wu, H. C.
    Lin, J. Q.
    Chan, S. C.
    2017 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2017,
  • [34] A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data
    Reuben Thomas
    Carlos J Paredes
    Sanjay Mehrotra
    Vassily Hatzimanikatis
    Eleftherios T Papoutsakis
    BMC Bioinformatics, 8
  • [35] A time-course microarray data analysis reveals consistent dysregulated genes and upstream microRNAs in autoantibody-mediated arthritis
    Xinwen Wang
    Jie Bai
    Zhen Jia
    Yangjun Zhu
    Jijun Liu
    Kun Zhang
    Dingjun Hao
    Lisong Heng
    Journal of Orthopaedic Surgery and Research, 12
  • [36] Time-Course Microarray Analysis of Rejected Human Donor Lungs during 12 Hours of Acellular Normothermic Ex Vivo Lung Perfusion
    Yeung, J. C.
    Boutros, P. C.
    Zamel, R.
    Cypel, M.
    Bai, X. -H.
    Matsuda, Y.
    Waddell, T. K.
    Liu, M.
    Keshavjee, S.
    JOURNAL OF HEART AND LUNG TRANSPLANTATION, 2012, 31 (04): : S106 - S107
  • [37] Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering
    Hakamada, K
    Okamoto, M
    Hanai, T
    BIOINFORMATICS, 2006, 22 (07) : 843 - 848
  • [38] Dynamic Gene and Transcriptional Regulatory Networks Inferring with Multi-Laplacian Prior from Time-Course Gene Microarray Data
    Zhang, L.
    Wu, H. C.
    Chan, S. C.
    Wang, C.
    2017 22ND INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP), 2017,
  • [39] EPILEPTIC FOCUS INDUCED BY INTRAHIPPOCAMPAL CHOLERA-TOXIN IN RAT - TIME-COURSE AND PROPERTIES IN-VIVO AND IN-VITRO
    WILLIAMS, SF
    COLLING, SB
    WHITTINGTON, MA
    JEFFERYS, JGR
    EPILEPSY RESEARCH, 1993, 16 (02) : 137 - 146
  • [40] An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)
    Aryee, Martin J.
    Gutierrez-Pabello, Jose A.
    Kramnik, Igor
    Maiti, Tapabrata
    Quackenbush, John
    BMC BIOINFORMATICS, 2009, 10