CORM: An R Package Implementing the Clustering of Regression Models Method for Gene Clustering
نویسندگان
چکیده
منابع مشابه
CORM: An R Package Implementing the Clustering of Regression Models Method for Gene Clustering
We report a new R package implementing the clustering of regression models (CORM) method for clustering genes using gene expression data and provide data examples illustrating each clustering function in the package. The CORM package is freely available at CRAN from http://cran.r-project.org.
متن کاملClustOfVar: An R Package for the Clustering of Variables
Clustering of variables is as a way to arrange variables into homogeneous clusters i.e. groups of variables which are strongly related to each other and thus bring the same information. Clustering of variables can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative v...
متن کاملoptCluster: An R Package for Determining the Optimal Clustering Algorithm
There exist numerous programs and packages that perform validation for a given clustering solution; however, clustering algorithms fare differently as judged by different validation measures. If more than one performance measure is used to evaluate multiple clustering partitions, an optimal result is often difficult to determine by visual inspection alone. This paper introduces optCluster, an R...
متن کاملRankcluster: An R Package for clustering multivariate partial ranking
Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account m...
متن کاملAPCluster: an R package for affinity propagation clustering
SUMMARY Affinity propagation (AP) clustering has recently gained increasing popularity in bioinformatics. AP clustering has the advantage that it allows for determining typical cluster members, the so-called exemplars. We provide an R implementation of this promising new clustering technique to account for the ubiquity of R in bioinformatics. This article introduces the package and presents an ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Cancer Informatics
سال: 2014
ISSN: 1176-9351,1176-9351
DOI: 10.4137/cin.s13967