ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data
ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data
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ClusterSignificance：一个生物导体包，有助于对降维数据中的类簇分离进行统计分析
DOI：
10.1093/bioinformatics/btx393
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发表时间：
2017
期刊：
影响因子：
5.8
通讯作者：
D. Grandér
中科院分区：
文献类型：
--
作者：
J. Serviss;J. Gådin;P. Eriksson;L. Folkersen;D. Grandér
摘要：通过高通量实验产生的多维数据越来越多地与降维方法结合使用，以确定由此产生的数据分离是否与已知类别相对应。这对于确定变量的子集，例如特定途径中的基因是否单独可以将样本划分到这些已建立的类别中特别有用。尽管如此，对类分离的评估通常是主观的，并通过可视化执行。在这里，我们介绍ClusterSignsignance包；这是一组工具，旨在评估降维算法下游的类分离的统计意义。此外，我们演示了ClusterSignsignance包的设计和用途，并利用它来确定长非编码RNA表达在多种血液系统恶性肿瘤识别中的重要性。供应和实施：ClusterSignsignance是一种R包，可通过BioConductor(https://bioconductor.org/packages/ClusterSignificance)根据GPL-3获得。
Summary: Multi‐dimensional data generated via high‐throughput experiments is increasingly used in conjunction with dimensionality reduction methods to ascertain if resulting separations of the data correspond with known classes. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package; a set of tools designed to assess the statistical significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non‐coding RNA expression in the identity of multiple hematological malignancies. Availability and implementation: ClusterSignificance is an R package available via Bioconductor (https://bioconductor.org/packages/ClusterSignificance) under GPL‐3. Contact: dan.grander@ki.se Supplementary information: Supplementary data are available at Bioinformatics online.
影响因子：
45.3
作者：
Haferlach, Torsten;Kohlmann, Alexander;Foa, Robin
通讯作者：
Foa, Robin