Zhu Wang <https://orcid.org/0000-0002-0773-0052> and others (see COPYRIGHTS)

Interactive Shiny App for Bulk Sequencing Data
c(
person("Ilias", "Moutsopoulos", , "im383@cam.ac.uk", c("aut", "cre")),
person("Eleanor", "Williams", , "ecw63@cam.ac.uk", role = c("aut", "ctb")),
person("Irina", "Mohorianu", , "iim22@cam.ac.uk", role = c("aut", "ctb"))
)
Given an expression matrix from a bulk RNA-Seq experiment,
pre-processes it and creates a shiny app for interactive data
analysis and visualisation. The app contains quality checks,
differential expression analysis, volcano and cross plots,
enrichment analysis and gene regulatory network inference,
and can be customised to contain more panels by the user.
ggplot2, shinythemes, shiny, gprofiler2, edgeR, DESeq2, stats,
ggrepel, utils, RColorBrewer, ComplexHeatmap, circlize, grid,
shinyWidgets, shinyjqui, dplyr, magrittr, ggforce, rlang, glue,
preprocessCore, matrixStats, noisyr, tibble, ggnewscale,
ggrastr, GENIE3, visNetwork, DT, scales, shinyjs, tidyr
rmarkdown, knitr, BiocManager, AnnotationDbi, org.Hs.eg.db,
org.Mm.eg.db
Ilias Moutsopoulos [aut, cre],
Eleanor Williams [aut, ctb],
Irina Mohorianu [aut, ctb]

Given an expression matrix from a bulk sequencing experiment,
pre-processes it and creates a shiny app for interactive data
analysis and visualisation. The app contains quality checks,
differential expression analysis, volcano and cross plots,
enrichment analysis and gene regulatory network inference,
and can be customised to contain more panels by the user.
ggplot2, shiny, gprofiler2, edgeR, DESeq2, stats, ggrepel,
utils, RColorBrewer, ComplexHeatmap, circlize, grid,
shinyWidgets, shinyjqui, dplyr, magrittr, ggforce, rlang, glue,
preprocessCore, matrixStats, noisyr, tibble, ggnewscale,
ggrastr, GENIE3, visNetwork, DT, scales, shinyjs, tidyr,
shinyLP, UpSetR, stringr
rmarkdown, knitr, shinythemes, BiocManager, AnnotationDbi,
org.Hs.eg.db, org.Mm.eg.db, readxl, testthat (>= 3.0.0)

Automatic Groove Identification via Bayesian Changepoint
Detection
c(person("Nathaniel", "Garton", email = "nate.garton13@gmail.com", role = c("aut","cre")),
person("Kiegan", "Rice", email = "ricek@iastate.edu", role = "ctb"))
Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step
of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option
in the function from the package 'bulletxtrctr' which identifies the groove locations.
Nathaniel Garton [aut, cre],
Kiegan Rice [ctb]