Matthew Owen, Skyler Cranmer, Olivia Lau, Kosuke Imai and Gary
King
General Additive Models extend GLM's to support the
application of smoothing functions to the predictor terms.
'compute.ev.R' 'describe.logit.gam.R' 'describe.normal.gam.R'
'describe.poisson.gam.R' 'describe.probit.gam.R'
'model.frame.GamFormula.R' 'param.logit.gam.R'
'param.normal.gam.R' 'param.poisson.gam.R' 'param.probit.gam.R'
'plots.R' 'pv.R' 'qi.logit.gam.R' 'qi.normal.gam.R'
'qi.poisson.gam.R' 'qi.probit.gam.R' 'repeat.data.R'
'zelig2logit.gam.R' 'zelig2normal.gam.R' 'zelig2poisson.gam.R'
'zelig2probit.gam.R' 'ZeligGAM-package.R' 'common-methods.R'
'anova.zelig.R' 'logLik.zelig.R'

Matthew Owen, Skyler Cranmer, Olivia Lau, Kosuke Imai and Gary King
General Additive Models extend GLM's to support the application of
smoothing functions to the predictor terms.

Matthew Owen, Ferdinand Alimadhi, Delia Bailey
Add-on package for Zelig. Enables the use of multilevel
(mixed-effect) models.
'compute.mixed.terms.R' 'describes.R' 'model.frame.lmer.R'
'param.gamma.mixed.R' 'param.logit.mixed.R' 'param.ls.mixed.R'
'param.poisson.mixed.R' 'param.probit.mixed.R'
'qi.gamma.mixed.R' 'qi.logit.mixed.R' 'qi.ls.mixed.R'
'qi.poisson.mixed.R' 'qi.probit.mixed.R' 'setup.x.matrix.R'
'zelig2gamma.mixed.R' 'zelig2logit.mixed.R' 'zelig2ls.mixed.R'
'zelig2poisson.mixed.R' 'zelig2probit.mixed.R'
'ZeligMixed-package.R' 'common-methods.R'

Add-on package for Zelig. Enables the use of multilevel (mixed-effect) models.

Analyze High-Dimensional High-Throughput Dataset and Quality
Control Single-Cell RNA-Seq
c(
person(given = "Yajing", family = "Hao", email = "yahao@health.ucsd.edu", role  = c("aut"), comment = c(ORCID = "0000-0003-1384-4176")),
person(given = "Shuyang", family = "Zhang", email = "s4zhang@ucsd.edu", role  = "ctb", comment=c(ORCID="0000-0002-8428-1828")),
person(given = "Junhui", family = "Li", email = "ljh.biostat@gmail.com", role = "cre"),
person(given = "Guofeng", family = "Zhao", email = "gzhao@health.ucsd.edu", role = "ctb"),
person(given = "Xiang-Dong", family = "Fu", email = "xdfu@health.ucsd.edu", role = c("cph","fnd"), comment = c(ORCID = "0000-0001-5499-8732"))
)
The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.
RColorBrewer, Rtsne, dplyr, e1071, ggplot2, reshape2, scater,
gridExtra, mixtools
Yajing Hao [aut] (<https://orcid.org/0000-0003-1384-4176>),
Shuyang Zhang [ctb] (<https://orcid.org/0000-0002-8428-1828>),
Junhui Li [cre],
Guofeng Zhao [ctb],
Xiang-Dong Fu [cph, fnd] (<https://orcid.org/0000-0001-5499-8732>)