Fouad Yousif, Xihui Lin, Fan Fan, Christopher Lalansingh
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. SeqKat reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Upon publication of the paper describing the methods used in this package, a reference will be added here.

Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.

Fouad Yousif, Xihui Lin, Fan Fan, Christopher Lalansingh, John Macdonald

Ki-Yeol Kim and Gwan-Su Yi, Cbio lab., Information and Communications University
This function estimates missing values sequentially from the gene that had least missing rate in microarray data

Ki-Yeol Kim and Gwan-Su Yi, CSBio lab., Information and
Communications University
This function estimates missing values sequentially from
the gene that had least missing rate in microarray data

Network Module-Based Model in the Differential Expression
Analysis for RNA-Seq
Mingli Lei, Jia Xu, Li-Ching Huang, Lily Wang, Jing Li
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.

Generate RNA-Seq Data from Gene-Gene Association Networks
c(person("Tyler", "Grimes", role = c("aut", "cre"),
email = "tyler.grimes@ufl.edu"),
person("Somnath", "Datta", role = c("aut")))
Tyler Grimes [aut, cre],
Somnath Datta [aut]
Methods to generate random gene-gene association networks and simulate RNA-seq data from them. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.
fitdistrplus, ggplot2, grDevices, graphics, igraph, mvtnorm,
purrr, RColorBrewer, tibble, Rcpp, rlang, stats, utils