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An Open Source Microarray Data Analysis System with GUI: Quintet

机译:具有GUI的开源微阵列数据分析系统:五重奏

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摘要

We address Quintet, an R-based unified cDNA microarray data analysis system with GUI. Five principal categories of microarray data analysis have been coherently integrated in Quintet: data processing steps such as faulty spot filtering and normalization, data quality assessment (QA), identification of differentially expressed genes (DEGs), clustering of gene expression profiles, and classification of samples. Though many microarray data analysis systems normally consider DEG identification and clustering/classification the most important problems, we emphasize that data processing and QA are equally important and should be incorporated into the regular-base data analysis practices because microarray data are very noisy. In each analysis category, customized plots and statistical summaries are also given for users convenience. Using these plots and summaries, analysis results can be easily examined for their biological plausibility and compared with other results. Since Quintet is written in R, it is highly extendable so that users can insert new algorithms and experiment them with minimal efforts. Also, the GUI makes it easy to learn and use and since R-language and its GUI engine, Tcl/Tk, are available in all operating systems, Quintet is OS-independent too.
机译:我们解决了Quintet,一个基于GUI的基于R的统一cDNA微阵列数据分析系统。五重芯片数据分析的五个主要类别已被整合到Quintet中:数据处理步骤,例如错误点过滤和标准化,数据质量评估(QA),差异表达基因(DEG)的识别,基因表达谱的聚类以及样品。尽管许多微阵列数据分析系统通常将DEG识别和聚类/分类视为最重要的问题,但我们强调数据处理和质量保证同等重要,并且应将其纳入常规数据分析实践中,因为微阵列数据非常嘈杂。在每个分析类别中,还提供了自定义的图和统计摘要,以方便用户使用。使用这些图和摘要,可以轻松检查分析结果的生物学合理性,并将其与其他结果进行比较。由于Quintet用R编写,因此具有高度可扩展性,因此用户可以插入新算法并以最小的努力对其进行试验。而且,GUI易于学习和使用,并且由于R语言及其GUI引擎Tcl / Tk在所有操作系统中都可用,因此Quintet也与操作系统无关。

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