Package 'INSPECTumours'

Title: IN-vivo reSPonsE Classification of Tumours
Description: 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]
Maintainer: Bairu Zhang <[email protected]>
License: Apache License (== 2)
Version: 0.1.0
Built: 2025-02-13 04:15:19 UTC
Source: https://github.com/cran/INSPECTumours

Help Index

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

Description

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

Usage

aggregate_study_info(df)

Arguments

df

data.frame

Value

data.frame

Generate table representing number of animals in classification groups

Description

Generate table representing number of animals in classification groups

Usage

animal_info_classification(data)

Arguments

data

final classification data

Value

data frame

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 effective

Description

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 effective

Usage

assess_efficacy(data, reference = "Control")

Arguments

data

prediction results
reference

name of the reference treatment

Value

dataframe with information about drug efficacy

makes df with data to be excluded

Description

makes df with data to be excluded

Usage

below_min_points(df, min_points)

Arguments

df

initial data frame
min_points

minimum number of data points for one animal_id per study

Value

df

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

Description

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

Usage

calc_gr(df, log_tv = "log_tv", day = "day")

Arguments

df

subset, one animal_id
log_tv

name of the column, tumour volume
day

name of the column, days

Value

tibble with GR and GR_SE

Calculate probability of categories

Description

Calculate probability of categories

Usage

calc_probability(data)

Arguments

data

data frame with predictions

Value

data frame

Calculate percentage of survived animals

Description

Calculate percentage of survived animals

Usage

calc_survived(df)

Arguments

df

data frame

Value

data frame

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

Description

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

Usage

change_time_multi(model)

Arguments

model

an object of class brmsfit

Value

data frame

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

Description

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

Usage

change_time_single(model)

Arguments

model

an object of class brmsfit

Value

a numeric vector of length one

Classify individual data points as Responders or Non-responders

Description

Classify individual data points as Responders or Non-responders

Usage

classify_data_point(df_newstudy, pred_newstudy)

Arguments

df_newstudy

data from new study
pred_newstudy

data frame with predictions

Value

data frame with "Responder"/"Non-responder" for individual data points

Make predictions for subcategories

Description

Make predictions for subcategories

Usage

classify_subcategories(data, model)

Arguments

data

data frame with classification results
model

object of class brmsfit

Value

data frame

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"

Description

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"

Usage

classify_type_responder(df)

Arguments

df

data frame

Value

data frame with a new column classify_tumour

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

Description

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

Usage

clean_string(string)

Arguments

string

to modify

Value

modified string

Function to plot a control growth profile

Description

Function to plot a control growth profile

Usage

control_growth_plot(df, model_type, col_palette)

Arguments

df

data frame
model_type

string
col_palette

character palette

Value

ggplot object

Tumour volume data over time for in-vivo studies

Description

A dataset containing the repeatedly measurements of tumour volume data over time for individual animals.

Usage

example_data

Format

A data frame with 1462 rows and 6 variables:

study

study identifier

group

group identifier

treatment

treatment type

animal_id

animal identifier

day

day after implant

tumour_volume

volume in mm3

Filter rows to exclude from the analysis

Description

Filter rows to exclude from the analysis

Usage

exclude_data(df, study_id_ex, animal_id_ex, day_ex, reason)

Arguments

df

initial df
study_id_ex

string: study id
animal_id_ex

string: animal id
day_ex

string: day
reason

string: why it should be excluded

Value

dataframe with rows that meets exclusion criteria

Function to expand a vector of colors if needed

Description

Function to expand a vector of colors if needed

Usage

expand_palette(col_palette, n)

Arguments

col_palette

character palette to color the treatments
n

how many colors are needed

Value

a character vector of colors

Calculate coefficients for a nonlinear model

Description

Calculate coefficients for a nonlinear model

Usage

f_start(df, x, y, r_change)

Arguments

df

data frame with x as a predictor and y is an outcome
x

predictor string
y

outcome string
r_change

numeric

Value

list of coefficients

Classify tumour based on response status of individuals

Description

Classify tumour based on response status of individuals

Usage

get_responder(x, n)

Arguments

x

character vector with response statuses of one animal
n

consecutive measurements for classification

Value

"Responder" or "Non-responder"

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

Description

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

Usage

guess_match(colnames_df, crit_cols)

Arguments

colnames_df

a character vector with names
crit_cols

a character vector

Value

list: possible match to each critical column

Function to hide outliers in boxplots with jitterdodge as suggested

Description

Function to hide outliers in boxplots with jitterdodge as suggested

Usage

hide_outliers(x)

Arguments

x

plotly object

Value

plotly object without boxplot outliers

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

Description

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

Usage

load_data(path, name)

Arguments

path

path to a temp file
name

filename provided by the web browser

Value

data frame

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

Description

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

Usage

make_terms(days, studies = NULL)

Arguments

days

vector with days with which to predict
studies

vector with studies with which to predict

Value

vector with values for predictions

Build model and make predictions

Description

Build model and make predictions

Usage

model_control(df_control, df_newstudy, method, end_day)

Arguments

df_control

data frame with control data (including historical control, if provided)
df_newstudy

data frame, data from new study
method

"Two-stage non-linear model" or "Linear model"
end_day

period of time used for the statistical modelling of the control data

Value

list: two data frames with prediction results (for new study and for control data)

Display a popup message and reset fileInput

Description

Display a popup message and reset fileInput

Usage

notify_error_and_reset_input(message_text)

Arguments

message_text

the modal's text

Fit model (Bayesian ordered logistic regression)

Description

Fit model (Bayesian ordered logistic regression)

Usage

ordered_regression(df, formula, n_cores)

Arguments

df

data frame with classification results. Tumour classification is converted into ordinal data
formula

string
n_cores

number of cores to use

Value

object of class brmsfit

Plot representing number of animals in classification groups

Description

Plot representing number of animals in classification groups

Usage

plot_animal_info(data, col_palette)

Arguments

data

final classification data
col_palette

character palette

Value

ggplot object

Function to plot classification over growth rate

Description

Function to plot classification over growth rate

Usage

plot_class_gr(df, col_palette)

Arguments

df

data frame
col_palette

character palette

Value

ggplot object

Function to plot classification over tumour volume

Description

Function to plot classification over tumour volume

Usage

plot_class_tv(df, col_palette, title_name)

Arguments

df

data frame
col_palette

named vector
title_name

character

Value

ggplot object

Plot estimated proportions

Description

Plot estimated proportions

Usage

plot_proportions(data, col_palette)

Arguments

data

table of the category prediction
col_palette

character palette

Function to plot waterfall

Description

Function to plot waterfall

Usage

plot_waterfall(df, col_palette, study_name)

Arguments

df

data frame
col_palette

character palette
study_name

string: to show on title

Value

ggplot object

Create volume plot for one-batch data

Description

Create volume plot for one-batch data

Usage

plotly_volume(
  df,
  col_palette = NULL,
  faceting_var,
  y_name,
  y_var,
  p_title,
  ...
)

Arguments

df

data.frame, single-batch long format
col_palette

character palette to color the treatments
faceting_var

string
y_name

string
y_var

string: column name for y axis
p_title

plot title
...

arguments passed to plot_ly

Value

plotly object

Make predictions, linear model

Description

Make predictions, linear model

Usage

predict_lm(model, newdata, single)

Arguments

model

a model object
newdata

data frame in which to look for variables with which to predict
single

logical: TRUE if single study experiment

Value

data frame with predictions

Make predictions based on non-linear model, multiple studies

Description

Make predictions based on non-linear model, multiple studies

Usage

predict_nlm_multi(model, newdata, change_time)

Arguments

model

an object of class brmsfit
newdata

data frame in which to look for variables with which to predict
change_time

data frame

Value

data frame with predictions

Make predictions based on non-linear model, single study

Description

Make predictions based on non-linear model, single study

Usage

predict_nlm_single(model, newdata, change_time)

Arguments

model

an object of class brmsfit
newdata

data frame in which to look for variables with which to predict
change_time

numeric

Value

data frame with predictions

Make predictions

Description

Make predictions

Usage

predict_regr_model(model, df)

Arguments

model

object of class brmsfit
df

data frame with classification results

Value

data frame

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(...)

Arguments

...

additional options passed to shinyApp()

Value

No return value, called for the shiny app interface

Fit nonlinear model - continuous hinge function

Description

Fit nonlinear model - continuous hinge function

Usage

run_nl_model(start, df_mod, formula, n_cores)

Arguments

start

df with coefficients
df_mod

data of all variables used in the model
formula

an object of class brmsformula
n_cores

number of cores to use

Value

object of class brmsfit

Set up a waiting screen

Description

Set up a waiting screen

Usage

set_waiter(header)

Arguments

header

text to display on loading screen

Value

object of a class waiter