NeatMap--non-clustering heat map alternatives in R.
NeatMap--non-clustering heat map alternatives in R.
复制标题
DOI：
10.1186/1471-2105-11-45
复制
发表时间：
2010-01-22
影响因子：
3
通讯作者：
Oono Y
中科院分区：
文献类型：
--
作者：
Rajaram S;Oono Y
聚类热图是可视化基因组数据的最常用方法。它以直观的格式复杂地显示大量数据，便于检测数据中隐藏的结构和关系。然而，它是阻碍了它的使用聚类分析，并不总是尊重数据中的内在关系，往往需要非标准化的行/列重新排序进行后聚类。这有时会导致信息不足和/或误导性的结论。通常，使用降维算法（如主成分分析和多维缩放）会提供更多的信息，这些算法尊重数据中固有的拓扑结构。然而，尽管它们在生物数据分析中被证明是有用的，但它们并没有被广泛使用。这至少部分是由于缺乏用户友好的可视化方法与热图的内脏影响。NeatMap是一个为满足这一需求而设计的R包。NeatMap提供了各种新颖的图（二维和三维），可与这些降维技术结合使用。与热图类似，但与传统的结果显示不同，它允许显示整个数据集，同时可视化元素之间的关系。它还允许叠加聚类分析结果进行相互验证。NeatMap比传统的热图具有更丰富的信息，并得到了两个著名的微阵列数据集的帮助。因此，NeatMap保留了聚类热图的许多优点，同时解决了它的一些缺陷。人们希望NeatMap能够促进非聚类降维算法的采用。
The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not always respect the intrinsic relations in the data, often requiring non-standardized reordering of rows/columns to be performed post-clustering. This sometimes leads to uninformative and/or misleading conclusions. Often it is more informative to use dimension-reduction algorithms (such as Principal Component Analysis and Multi-Dimensional Scaling) which respect the topology inherent in the data. Yet, despite their proven utility in the analysis of biological data, they are not as widely used. This is at least partially due to the lack of user-friendly visualization methods with the visceral impact of the heat map. NeatMap is an R package designed to meet this need. NeatMap offers a variety of novel plots (in 2 and 3 dimensions) to be used in conjunction with these dimension-reduction techniques. Like the heat map, but unlike traditional displays of such results, it allows the entire dataset to be displayed while visualizing relations between elements. It also allows superimposition of cluster analysis results for mutual validation. NeatMap is shown to be more informative than the traditional heat map with the help of two well-known microarray datasets. NeatMap thus preserves many of the strengths of the clustered heat map while addressing some of its deficiencies. It is hoped that NeatMap will spur the adoption of non-clustering dimension-reduction algorithms.
登录
查看更多内容
影响因子：
3
作者：
Hibbs MA;Dirksen NC;Li K;Troyanskaya OG
通讯作者：
Troyanskaya OG
影响因子：
5.8
作者：
Medvedovic, M;Sivaganesan, S
通讯作者：
Sivaganesan, S
DOI：
10.1073/pnas.96.6.2907
发表时间：
1999-03-16
影响因子：
11.1
作者：
Tamayo, P;Slonim, D;Golub, TR
通讯作者：
Golub, TR
影响因子：
5.8
作者：
Qin, Zhaohui S.
通讯作者：
Qin, Zhaohui S.
影响因子：
30.8
作者：
Tavazoie, S;Hughes, JD;Church, GM
通讯作者：
Church, GM