A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.
Seurat, ggplot2, patchwork, dplyr, tidyr, tibble, magrittr,
stringr, assertthat, viridis, circlize, colorspace,
ComplexHeatmap, ggbeeswarm, ggdist, ggExtra, ggplotify,
ggrastr, ggrepel, ggridges, ggsignif, graphics, Nebulosa,
pheatmap, plyr, rlang, scales, scattermore, covr, forcats,
infercnv, knitr, Matrix, purrr, qpdf, rgdal, rjags, rmarkdown,
svglite, testthat (>= 3.0.0)

assertthat, circlize, colorspace, covr, ComplexHeatmap,
dplyr, ggbeeswarm, ggdist, ggExtra, ggplot2, ggplotify,
ggrastr, ggrepel, ggridges, ggsignif, graphics, magrittr,
Nebulosa, patchwork, pheatmap, plyr, rlang, scales,
scattermore, Seurat, tibble, tidyr, forcats, infercnv, knitr,
Matrix, purrr, qpdf, rgdal, rjags, rmarkdown, stringr, svglite,
ggalluvial, testthat (>= 3.0.0), viridis

assertthat, circlize, colorspace, covr, ComplexHeatmap,
dplyr, ggbeeswarm, ggdist, ggExtra, ggplot2, ggplotify,
ggrastr, ggrepel, ggridges, ggsignif, graphics, magrittr,
Nebulosa, patchwork, pheatmap, plyr, rlang, scales,
scattermore, Seurat, tibble, tidyr, forcats, infercnv, knitr,
Matrix, purrr, qpdf, rgdal, rjags, rmarkdown, stringr, svglite,
ggalluvial, testthat (>= 3.0.0), viridis, UCell (>= 2.0)

Extensions for Synthetic Controls Analysis
c(
person(given = 'Bruno',
family = 'Castanho Silva',
role = c('aut', 'cre'),
email = 'bcsilva@wiso.uni-koeln.de',
comment = c(ORCID = '0000-0001-9363-4704')),
person(given = 'Michael',
family = 'DeWitt',
role = c('aut'),
email = 'me.dewitt.jr@gmail.com',
comment = c(ORCID = '0000-0001-8940-1967')))
Extensions to the synthetic controls analyses
performed by the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011)
<doi:10.18637/jss.v042.i13>. Includes generating
and plotting placebos, post/pre-MSPE (mean squared prediction error)
significance tests and plots, and calculating average treatment effects
for multiple treated units. This package represents an implementation of
those methods suggested in  Abadie, Diamond,and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746> for
use in synthetic control analysis.
ggplot2, Synth, stringr, stats, cvTools, furrr, dplyr, purrr
Bruno Castanho Silva [aut, cre]
(<https://orcid.org/0000-0001-9363-4704>),
Michael DeWitt [aut] (<https://orcid.org/0000-0001-8940-1967>)