Package: INSPECTumours 0.1.0

INSPECTumours: IN-vivo reSPonsE Classification of Tumours

This is a shiny app used for the statistical classifying and analysing pre-clinical tumour responses.

Authors:Bairu Zhang [cre, aut], Olga Muraeva [aut], Natasha Karp [aut]

INSPECTumours_0.1.0.tar.gz
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INSPECTumours.pdf |INSPECTumours.html✨
INSPECTumours/json (API)
NEWS

# Install 'INSPECTumours' in R:
install.packages('INSPECTumours', repos = c('https://bairuzhang.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • example_data - Tumour volume data over time for in-vivo studies

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 3 scripts 200 downloads 1 exports 148 dependencies

Last updated 3 years agofrom:eed636aa77. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winOKNov 15 2024
R-4.5-linuxOKNov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:run_app

Dependencies:abindarrayhelpersaskpassbackportsbase64encbayesplotBHbitbit64bootbridgesamplingbrmsBrobdingnagbroombslibcachemcallrcellrangercheckmateclicodacodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledescdigestdistributionaldplyrDTevaluatefansifarverfastmapfontawesomefsfuturefuture.applygenericsggdistggeffectsggplot2ggridgesglobalsgluegridExtragtablehighrhmshtmltoolshtmlwidgetshttpuvhttrinlineinsightisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelistenvlme4loomagrittrMASSMatrixmatrixStatsmemoisemgcvmimeminqamodelrmunsellmvtnormnleqslvnlmenloptrnumDerivopensslpanderparallellypillarpkgbuildpkgconfigplotlyplyrposteriorprettyunitsprocessxprogresspromisespspurrrquadprogQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelreadxlrematchreshape2rlangrmarkdownrstanrstantoolssassscalesshinyshinyalertshinyFeedbackshinyjsshinytoastrshinyvalidatesourcetoolsStanHeadersstringistringrsvUnitsystensorAtibbletidybayestidyrtidyselecttinytextippytzdbutf8uuidvctrsviridisLitevroomwaiterwithrxfunxtableyaml

Readme and manuals

Help Manual

Help pageTopics
create a table with aggregated data: each row contains information about control and treatments of a single studyaggregate_study_info
Generate table representing number of animals in classification groupsanimal_info_classification
Credible interval (or say “Bayesian confidence interval”) of the mean difference between two groups (treatment and reference) is used to assess the efficacy. If 0 falls outside the interval, the drug was considered significantly effectiveassess_efficacy
makes df with data to be excludedbelow_min_points
Function to return rate of growth (e.g. the slope after a log transformation of the tumour data against time)calc_gr
Calculate probability of categoriescalc_probability
Calculate percentage of survived animalscalc_survived
Get an array with change_time for studies from the population-level effects, multiple studieschange_time_multi
Get a change time from the population-level effects, single studychange_time_single
Classify individual data points as Responders or Non-respondersclassify_data_point
Make predictions for subcategoriesclassify_subcategories
Classify tumour based on the growth rate and the p_value for a two-sided T test Tumour will be considered as "Non-responder", "Modest responder", "Stable responder" or "Regressing responder"classify_type_responder
function to remove hyphens, underscores, spaces and transform to lowercaseclean_string
Function to plot a control growth profilecontrol_growth_plot
Tumour volume data over time for in-vivo studiesexample_data
Filter rows to exclude from the analysisexclude_data
Function to expand a vector of colors if neededexpand_palette
Calculate coefficients for a nonlinear modelf_start
Classify tumour based on response status of individualsget_responder
function to search for the possible critical columns in a data.frameguess_match
Function to hide outliers in boxplots with jitterdodge as suggestedhide_outliers
function to read data from users (.csv or .xlsx files)load_data
Create a character vector with the names of terms from model, for which predictions should be displayed Specific values are specified in square bracketsmake_terms
Build model and make predictionsmodel_control
Display a popup message and reset fileInputnotify_error_and_reset_input
Fit model (Bayesian ordered logistic regression)ordered_regression
Plot representing number of animals in classification groupsplot_animal_info
Function to plot classification over growth rateplot_class_gr
Function to plot classification over tumour volumeplot_class_tv
Plot estimated proportionsplot_proportions
Function to plot waterfallplot_waterfall
Create volume plot for one-batch dataplotly_volume
Make predictions, linear modelpredict_lm
Make predictions based on non-linear model, multiple studiespredict_nlm_multi
Make predictions based on non-linear model, single studypredict_nlm_single
Make predictionspredict_regr_model
Run the Shiny Applicationrun_app
Fit nonlinear model - continuous hinge functionrun_nl_model
Set up a waiting screenset_waiter