Differential Exon Usage Test for RNA-Seq data via Empirical
Bayes Shrinkage of the Dispersion Parameter
Differential Exon Usage Test for RNA-Seq data via Empirical Bayes Shrinkage of the Dispersion Parameter

Differential Exon Usage Test for RNA-Seq Data via Empirical
Bayes Shrinkage of the Dispersion Parameter
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
numDeriv, datasets, grDevices, graphics, stats, utils

c(
person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role="aut"),
person("Victor", "Chernozhukov", role="aut"),
person("Malte S.", "Kurz", email = "malte.simon.kurz@uni-hamburg.de", role=c("aut", "cre")),
person("Martin", "Spindler", email="martin.spindler@gmx.de", role="aut"))
Implementation of the double/debiased machine learning framework of
Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear
regression models, partially linear instrumental variable regression models,
interactive regression models and interactive instrumental variable
regression models. 'DoubleML' allows estimation of the nuisance parts in
these models by machine learning methods and computation of the Neyman
orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
the 'R6' package is very flexible.
R6 (>= 2.4.1), data.table (>= 1.12.8), stats, checkmate, mlr3
(>= 0.5.0), mlr3tuning (>= 0.3.0), mvtnorm, utils,
clusterGeneration, readstata13
knitr, rmarkdown, testthat, patrick, mlr3learners (>= 0.3.0),
paradox (>= 0.4.0), dplyr, glmnet, lgr, ranger, sandwich, AER,
rpart
'double_ml.R' 'double_ml_data.R' 'double_ml_iivm.R'
'double_ml_irm.R' 'double_ml_pliv.R' 'double_ml_plr.R'
'helper.R' 'datasets.R' 'zzz.R'
Philipp Bach [aut],
Victor Chernozhukov [aut],
Malte S. Kurz [aut, cre],
Martin Spindler [aut]