DAnTE：一种用于组学数据定量分析的统计工具。
DAnTE: a statistical tool for quantitative analysis of -omics data.
作者信息
Polpitiya Ashoka D, Qian Wei-Jun, Jaitly Navdeep, Petyuk Vladislav A, Adkins Joshua N, Camp David G, Anderson Gordon A, Smith Richard D
机构信息
Pacific Northwest National Laboratory, Richland, WA 99352, USA.
出版信息
Bioinformatics. 2008 Jul 1;24(13):1556-8. doi: 10.1093/bioinformatics/btn217. Epub 2008 May 3.
Abstract
UNLABELLED
Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly.
AVAILABILITY
DAnTE may be downloaded free of charge at http://omics.pnl.gov/software/.
SUPPLEMENTARY INFORMATION
An example dataset with instructions on how to perform a series of analysis steps is available at http://omics.pnl.gov/software/
摘要
未标注
数据分析工具扩展（DAnTE）是一种统计工具，旨在应对与定量自下而上的鸟枪法蛋白质组学数据相关的挑战。该工具也已在微阵列数据中得到验证，并且可以轻松扩展到其他高通量数据类型。DAnTE具有选定的归一化方法、缺失值插补算法、肽到蛋白质的汇总方法、大量的绘图功能以及可处理不平衡数据和随机效应的全面假设检验方案。图形用户界面（GUI）设计得非常直观且用户友好。
可用性
可在http://omics.pnl.gov/software/免费下载DAnTE。
补充信息
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1
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J Am Soc Mass Spectrom. 1994 Nov;5(11):976-89. doi: 10.1016/1044-0305(94)80016-2.
2
Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics.用于消除与质谱分析和无标记蛋白质组学相关的系统偏差的归一化方法。
J Proteome Res. 2006 Feb;5(2):277-86. doi: 10.1021/pr050300l.
3
Bioconductor: open software development for computational biology and bioinformatics.生物导体：用于计算生物学和生物信息学的开源软件开发。
Genome Biol. 2004;5(10):R80. doi: 10.1186/gb-2004-5-10-r80. Epub 2004 Sep 15.
4
Statistical issues in cDNA microarray data analysis.cDNA微阵列数据分析中的统计学问题。
Methods Mol Biol. 2003;224:111-36. doi: 10.1385/1-59259-364-X:111.
5
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Biotechniques. 2003 Feb;34(2):374-8. doi: 10.2144/03342mt01.
6
Microarray data normalization and transformation.微阵列数据归一化与转换
Nat Genet. 2002 Dec;32 Suppl:496-501. doi: 10.1038/ng1032.
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Missing value estimation methods for DNA microarrays.DNA微阵列的缺失值估计方法。
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