Analyzing RNA sequence count tables using quasi-likelihood
Steve Lund <lundsp@gmail.com> and Long Qu <long.qu@wright.edu>
This package is used to apply the QL, QLShrink and QLSpline methods (Lund, Nettleton, McCarthy, and Smyth, 2012) to quasi-Poisson or quasi-negative binomial models for identifying differentially expressed genes in RNA-seq data.

Analyzing RNA Sequence Count Tables Using Quasi-likelihood
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with QL, QLShrink and QLSpline methods (Lund, Nettleton, McCarthy, and Smyth, 2012).

ANALYZING RNA SEQUENCE COUNT TABLES USING QUASI-LIKELIHOOD
c(
person("Steve", "Lund", role=c("aut"), email="lundsp@gmail.com"),
person("Long", "Qu", role=c("aut", "cre","ctb"), email="long.qu@wright.edu"),
person("Ian", "Marschner", role="aut", comment="The author of glm2::glm.fit2, which was modified slightly leading to glm.fit3 in this package."),
person("", "R Core Team", role="aut", comment="The author of stats::glm.fit, which was modified slightly leading to glm.fit3 in this package.")
)
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods (Lund, Nettleton, McCarthy, and Smyth, 2012).
Steve Lund [aut],
Long Qu [aut, cre, ctb],
Ian Marschner [aut] (The author of glm2::glm.fit2, which was modified
slightly leading to glm.fit3 in this package.),
R Core Team [aut] (The author of stats::glm.fit, which was modified
slightly leading to glm.fit3 in this package.)

Adriano Polpo <polpo@ufscar.br>,
Carlos A. de B. Pereira <cpereira@ime.usp.br>,
Cassio P. de Campos <cassiopc@acm.org>
Functions to compute confidence statistics
for ordering population quantiles.

Processing Pipeline for 'SamBada' from Pre- To Post-Processing
Solange Duruz, Sylvie Stucki, Oliver Selmoni, Elia Vajana
Processing pipeline for 'SamBada' from pre- to post-processing.
'SamBada' is a landscape genomic software designed to run univariate or multivariate logistic regression between the presence of a genotype and one or several environmental variables. See Stucki (2017) <doi:10.1111/1755-0998.12629> and <https://github.com/Sylvie/sambada>.
The package provides functions that can be classified into four categories:
1) Install 'SamBada'
2) Preprocessing (prepare genomic file into standards compatible with 'SamBada' and apply quality-control; retrieve environmental conditions at sampling location; prepare environmental file including removal of correlated variables and computation of population structure)
3) Processing (run 'SamBada' on multiple cores using 'Supervision')
4) Post-processing (calculate p-values and q-values, produce interactive Manhattan plots and query 'Ensembl' database, produce maps).
Rcpp, utils, data.table, shiny, plotly, httr, biomaRt,
ggplot2, sp, packcircles, raster, mapplots, spdep, rgdal,
gdalUtils, rworldmap, doParallel, foreach, knitr, rmarkdown