INSPECTumours (version 0.1.0)

IN-vivo reSPonsE Classification of Tumours

Description

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

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Version

Install

install.packages('INSPECTumours')

Monthly Downloads

156

Version

0.1.0

License

Apache License (== 2)

Maintainer

Bairu Zhang

Last Published

May 6th, 2022

Functions in INSPECTumours (0.1.0)

animal_info_classification

Generate table representing number of animals in classification groups

classify_data_point

Classify individual data points as Responders or Non-responders

calc_gr

Function to return rate of growth (e.g. the slope after a log transformation of the tumour data against time)

change_time_single

Get a change time from the population-level effects, single study

calc_survived

Calculate percentage of survived animals

assess_efficacy

Credible interval (or say <U+201C>Bayesian confidence interval<U+201D>) 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 effective

aggregate_study_info

create a table with aggregated data: each row contains information about control and treatments of a single study

clean_string

function to remove hyphens, underscores, spaces and transform to lowercase

exclude_data

Filter rows to exclude from the analysis

example_data

Tumour volume data over time for in-vivo studies

expand_palette

Function to expand a vector of colors if needed

below_min_points

makes df with data to be excluded

predict_regr_model

Make predictions

guess_match

function to search for the possible critical columns in a data.frame

plot_class_gr

Function to plot classification over growth rate

plot_animal_info

Plot representing number of animals in classification groups

predict_nlm_single

Make predictions based on non-linear model, single study

get_responder

Classify tumour based on response status of individuals

load_data

function to read data from users (.csv or .xlsx files)

hide_outliers

Function to hide outliers in boxplots with jitterdodge as suggested

f_start

Calculate coefficients for a nonlinear model

control_growth_plot

Function to plot a control growth profile

plot_class_tv

Function to plot classification over tumour volume

plot_proportions

Plot estimated proportions

make_terms

Create a character vector with the names of terms from model, for which predictions should be displayed Specific values are specified in square brackets

model_control

Build model and make predictions

plot_waterfall

Function to plot waterfall

plotly_volume

Create volume plot for one-batch data

ordered_regression

Fit model (Bayesian ordered logistic regression)

predict_nlm_multi

Make predictions based on non-linear model, multiple studies

notify_error_and_reset_input

Display a popup message and reset fileInput

predict_lm

Make predictions, linear model

set_waiter

Set up a waiting screen

classify_subcategories

Make predictions for subcategories

run_app

Run the Shiny Application

run_nl_model

Fit nonlinear model - continuous hinge function

classify_type_responder

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"

change_time_multi

Get an array with change_time for studies from the population-level effects, multiple studies

calc_probability

Calculate probability of categories