Application of two-part statistics for comparison of sequence variant counts.
Application of two-part statistics for comparison of sequence variant counts.
复制标题
DOI：
10.1371/journal.pone.0020296
复制
发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Harris JK
中科院分区：
文献类型：
--
作者：
Wagner BD;Robertson CE;Harris JK
通过高通量测序技术产生的大量序列数据显著增强了对微生物群落，特别是人类相关群落的研究。然而，这些数据会创建高维复杂数据集，其中包含大部分零、非负偏斜计数以及通常有限数量的样本。这些特征将序列数据与其他形式的高维数据区分开来，并且通常使用的统计方法没有充分解决这些特征。最终，医学研究可能会确定有针对性的干预措施或治疗方法，但缺乏用于特征选择和识别导致组间差异的分类群的分析工具，阻碍了进步。本文的目的是研究应用的两部分统计，以确定类群之间的差异两组。两部分统计的优势，比普通的统计测试应用于序列计数数据集进行了讨论。使用囊性纤维化微生物生态学研究和天然井样本的序列计数比较t检验、Wilcoxon检验和两部分检验的结果。我们展示了序列数据分析的两部分统计量的上级性能。微生物生态学研究中的性能改善与研究类型和所用的序列技术无关。
Investigation of microbial communities, particularly human associated communities, is significantly enhanced by the vast amounts of sequence data produced by high throughput sequencing technologies. However, these data create high-dimensional complex data sets that consist of a large proportion of zeros, non-negative skewed counts, and frequently, limited number of samples. These features distinguish sequence data from other forms of high-dimensional data, and are not adequately addressed by statistical approaches in common use. Ultimately, medical studies may identify targeted interventions or treatments, but lack of analytic tools for feature selection and identification of taxa responsible for differences between groups, is hindering advancement. The objective of this paper is to examine the application of a two-part statistic to identify taxa that differ between two groups. The advantages of the two-part statistic over common statistical tests applied to sequence count datasets are discussed. Results from the t-test, the Wilcoxon test, and the two-part test are compared using sequence counts from microbial ecology studies in cystic fibrosis and from cenote samples. We show superior performance of the two-part statistic for analysis of sequence data. The improved performance in microbial ecology studies was independent of study type and sequence technology used.
登录
查看更多内容
影响因子：
3
作者：
Neuhäuser, M;Boes, T;Jöckel, KH
通讯作者：
Jöckel, KH
影响因子：
14.9
作者：
Ludwig, W;Strunk, O;Schleifer, KH
通讯作者：
Schleifer, KH
影响因子：
14.9
作者：
Pruesse E;Quast C;Knittel K;Fuchs BM;Ludwig W;Peplies J;Glöckner FO
通讯作者：
Glöckner FO
影响因子：
4.3
作者：
Berglund, Eva C.;Nystedt, Bjorn;Andersson, Siv G. E.
通讯作者：
Andersson, Siv G. E.
影响因子：
5.8
作者：
Markowitz, Victor M.;Ivanova, Natalia;Kyrpides, Nikos C.
通讯作者：
Kyrpides, Nikos C.