Function reference • QurvE

All functions

biosensor.eq(): Internal function used to fit a biosensor response model with nlsLM

export_RData(): Export an R object as .RData file

export_Table(): Export a tabular object as tab-separated .txt file

fl.control(): Create a fl.control object.

fl.drFit(): Fit a biosensor model (Meyer et al., 2019) to response vs. concentration data

fl.drFitModel(): Perform a biosensor model fit on response vs. concentration data of a single sample.

fl.report(): Create a PDF and HTML report with results from a fluorescence analysis workflow

fl.workflow(): Run a complete fluorescence curve analysis and dose-reponse analysis workflow.

flBootSpline(): flBootSpline: Function to generate a bootstrap

flFit(): Perform a fluorescence curve analysis on all samples in the provided dataset.

flFitLinear(): Data fit via a heuristic linear method

flFitSpline(): Perform a smooth spline fit on fluorescence data

growth.control(): Create a grofit.control object.

growth.drBootSpline(): Perform a smooth spline fit on response vs. concentration data of a single sample

growth.drFit(): Perform a dose-response analysis on response vs. concentration data

growth.drFitModel(): Fit various models to response vs. concentration data of a single sample to determine the EC50.

growth.drFitSpline(): Perform a smooth spline fit on response vs. concentration data of a single sample to determine the EC50.

growth.gcBootSpline(): Perform a bootstrap on growth vs. time data followed by spline fits for each resample

growth.gcFit(): Perform a growth curve analysis on all samples in the provided dataset.

growth.gcFitLinear(): Fit an exponential growth model with a heuristic linear method

growth.gcFitModel(): Fit nonlinear growth models to growth data

growth.gcFitSpline(): Perform a smooth spline fit on growth data

growth.report(): Create a PDF and HTML report with results from a growth curve analysis workflow

growth.workflow(): Run a complete growth curve analysis and dose-reponse analysis workflow.

inflect(): Find indices of maxima an minima in a data series

lm_parms() lm_window(): Helper functions for handling linear fits.

low.integrate(): Function to estimate the area under a curve given as x and y(x) values

parse_Gen5Gen6(): Extract relevant data from a raw data export file generated with the "Gen5" or "Gen6" software.

parse_data(): Parse raw plate reader data and convert it to a format compatible with QurvE

parse_victornivo(): Extract relevant data from a raw data export file generated from the software of Perkin Elmer's "Victor Nivo" plate readers.

parse_victorx3(): Extract relevant data from a raw data export file generated from the software of Perkin Elmer's "Victor X3" plate readers.

plot(<drBootSpline>): Generic plot function for gcBootSpline objects.

plot(<drFit>): Generic plot function for drFit objects.

plot(<drFitFLModel>): Generic plot function for drFitFLModel objects.

plot(<drFitModel>): Generic plot function for drFitModel objects.

plot(<drFitSpline>): Generic plot function for drFitSpline objects.

plot(<drFitfl>): Generic plot function for drFitFL objects.

plot(<dr_parameter>): Compare calculated dose-response parameters between conditions.

plot(<dual>): Compare fluorescence and growth over time

plot(<flBootSpline>): Generic plot function for flBootSpline objects.

plot(<flFitLinear>): Generic plot function for flcFittedLinear objects. Plot the results of a linear regression on ln-transformed data

plot(<flFitRes>) plot(<flFit>): Combine different groups of samples into a single plot

plot(<flFitSpline>): Generic plot function for flFitSpline objects.

plot(<gcBootSpline>): Generic plot function for gcBootSpline objects.

plot(<gcFitLinear>): Generic plot function for gcFittedLinear objects. Plot the results of a linear regression on ln-transformed data

plot(<gcFitModel>): Generic plot function for gcFitModel objects.

plot(<gcFitSpline>): Generic plot function for gcFitSpline objects.

plot(<grid>): Plot a matrix of growth curve panels

plot(<grodata>): Generic plot function for grodata objects. Plots raw growth, fluorescence, or normalized fluorescence data of multiple samples or conditions.

plot(<grofit>): Generic plot function for grofit objects. Combine different groups of samples into a single plot

plot(<parameter>): Compare growth parameters between samples or conditions

rdm.data(): The function calls the baranyi function to generate curves between time zero and t and adds some random noise to the x- and y-axes. The three growth parameters given as input values will be slightly changed to produce different growth curves. The resulting datasets can be used to test the growth.workflow function.

read_data(): Read growth and fluorescence data in table format

read_file(): Call the appropriate function required to read a table file and return the table as a dataframe object.

run_app(): Run Shiny QurvE App

summary(<drBootSpline>): Generic summary function for drBootSpline objects

summary(<drFit>): Generic summary function for drFit objects

summary(<drFitFLModel>): Generic summary function for drFitFLModel objects

summary(<drFitModel>): Generic summary function for drFitModel objects

summary(<drFitSpline>): Generic summary function for drFitSpline objects

summary(<drFitfl>): Generic summary function for drFitfl objects

summary(<flBootSpline>): Generic summary function for flBootSpline objects

summary(<flFit>): Generic summary function for flFit objects

summary(<flFitLinear>): Generic summary function for flFitLinear objects

summary(<flFitSpline>): Generic summary function for flFitSpline objects

summary(<gcBootSpline>): Generic summary function for gcBootSpline objects

summary(<gcFit>): Generic summary function for gcFit objects

summary(<gcFitLinear>): Generic summary function for gcFitLinear objects

summary(<gcFitModel>): Generic summary function for gcFitModel objects

summary(<gcFitSpline>): Generic summary function for gcFitSpline objects

table_group_fluorescence_linear(): Generate a grouped results table for linear fits with average and standard deviations

table_group_fluorescence_spline(): Generate a grouped results table for spline fits with average and standard deviations

table_group_growth_linear(): Generate a grouped results table for linear fits with average and standard deviations

table_group_growth_model(): Generate a grouped results table for parametric fits with average and standard deviations

table_group_growth_spline(): Generate a grouped results table for spline fits with average and standard deviations

zipFastener(): Combine two dataframes like a zip-fastener