without affecting the meaning of the dendrogram. A dendrogram can be sorted based
especially for tasks such as comparison of different distance measures or linkage

Title: Modular Leaf Orering Methods for Dendrogram Nodes
Suggests: knitr, RColorBrewer, gplots, heatmap.plus, seriation

Title: Modular Leaf Ordering Methods for Dendrogram Nodes

Description: An implementation of functions to optimize ordering of nodes in
a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can
be sorted based on the average distance of subtrees, or based on the smallest
distance value. These sorting methods improve readability and interpretability
of tree structure, especially for tasks such as comparison of different distance
measures or linkage types and identification of tight clusters and outliers. As
a result, it also introduces more meaningful reordering for a coupled heatmap
Suggests: knitr, RColorBrewer, gplots, heatmap.plus, seriation, gapmap

Authors@R: c(person("Ryo", "Sakai", email="ryo@vda-lab.be", role="aut"), person("Evan", "Biederstedt", email="evan.biederstedt@gmail.com", role=c("cre", "aut")))
Description: An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 <doi:10.12688/f1000research.4784.1>.
Suggests: knitr, RColorBrewer, gplots, seriation, gapmap, rmarkdown
Packaged: 2021-04-19 19:12:09 UTC; evanbiederstedt

Title: Differential Expression Analysis Using a Bottom-Up Model
Authors@R: c(person("Gershom", "Buri", email = "buri@aims.edu.gh", role = c("aut","cre")),person("Wilfred", "Ndifon", email = "ndifon@gmail.com", role = "aut"))
Description: Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.