Data Analysis Functions for 'SBpipe' Package

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Documentation for package ‘sbpiper’ version 1.9.0

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basic_theme A generic basic theme for time courses. It extends ggplot2 theme_classic().
check_exp_dataset Check that the experimental data set exists.
combine_param_best_fits_stats Combine the parameter best fits statistics.
combine_param_ple_stats Combine the parameter PLE statistics.
compute_aic Compute the Akaike Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
compute_aicc Compute the corrected Akaike Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
compute_bic Compute the Bayesian Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
compute_cl_objval Compute the confidence level based on the minimum objective value.
compute_fratio_threshold Compute the fratio threshold for the confidence level.
compute_sampled_ple_stats Compute the table for the sampled PLE statistics.
gen_stats_table Generate a table of statistics for each model readout.
get_param_names Get parameter names
get_sorted_level_indexes Return the indexes of the files as sorted by levels.
histogramplot Plot a generic histogram
insulin_receptor_1 A stochastic model simulation
insulin_receptor_2 A stochastic model simulation
insulin_receptor_3 A stochastic model simulation
insulin_receptor_all_fits A parameter estimation data set including all the evaluated fits.
insulin_receptor_best_fits A parameter estimation data set including only the best evaluated fits.
insulin_receptor_exp_dataset Experimental data set for the insulin receptor beta phosphorylated at pY1146 as published in Dalle Pezze et al. Science Signaling 2012.
insulin_receptor_IR_beta_pY1146 A stochastic simulation data set for the insulin receptor beta phosphorylated at pY1146.
insulin_receptor_ps1_l0 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 0.
insulin_receptor_ps1_l1 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 1.
insulin_receptor_ps1_l11 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 11.
insulin_receptor_ps1_l13 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 13.
insulin_receptor_ps1_l14 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 14.
insulin_receptor_ps1_l16 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 16.
insulin_receptor_ps1_l3 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 3.
insulin_receptor_ps1_l4 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 4.
insulin_receptor_ps1_l6 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 6.
insulin_receptor_ps1_l8 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 8.
insulin_receptor_ps1_l9 A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 9.
insulin_receptor_ps2_tp2 A deterministic simulation of the insulin receptor model upon scanning of 2 model parameters.
kurtosis Calculate the kurtosis of a numeric vector
leftCI Return the left value of the parameter confidence interval. The provided dataset has two columns: ObjVal | ParamValue
load_exp_dataset Load the experimental data set.
normalise_vec Normalise a vector within 0 and 1
objval.col The name of the Objective Value column
objval_vs_iters_analysis Analysis of the Objective values vs Iterations.
parameter_density_analysis Parameter density analysis.
parameter_pca_analysis PCA for the parameters. These plots rely on factoextra fviz functions.
pca_theme A generic basic theme for pca. It extends ggplot2 theme_classic().
pe_ds_preproc Parameter estimation pre-processing. It renames the data set columns, and applies a log10 transformation if logspace is TRUE. If all.fits is true, it also computes the confidence levels.
plot_combined_tc Plot repeated time courses in the same plot with mean, 1 standard deviation, and 95% confidence intervals.
plot_comb_sims Plot the simulation time courses using a heatmap representation.
plot_double_param_scan_data Plot model double parameter scan time courses.
plot_fits Plot the number of iterations vs objective values in log10 scale.
plot_heatmap_tc Plot time courses organised as data frame columns with a heatmap.
plot_objval_vs_iters Plot the Objective values vs Iterations
plot_parameter_density Plot parameter density.
plot_raw_dataset Add experimental data points to a plot. The length of the experimental time course to plot is limited by the length of the simulated time course (=max_sim_tp).
plot_repeated_tc Plot repeated time courses in the same plot separately. First column is Time.
plot_sampled_2d_ple Plot 2D profile likelihood estimations.
plot_sampled_ple Plot the sampled profile likelihood estimations (PLE). The table is made of two columns: ObjVal | Parameter
plot_sep_sims Plot the simulations time course separately.
plot_single_param_scan_data Plot model single parameter scan time courses
plot_single_param_scan_data_homogen Plot model single parameter scan time courses using homogeneous lines.
replace_colnames Rename data frame columns. 'ObjectiveValue' is renamed as 'ObjVal'. Substrings 'Values.' and '..InitialValue' are removed.
rightCI Return the right value of the parameter confidence interval. The provided dataset has two columns: ObjVal | ParamValue
sampled_2d_ple_analysis 2D profile likelihood estimation analysis.
sampled_ple_analysis Run the profile likelihood estimation analysis.
sbpiper_pe Main R function for SBpipe pipeline: parameter_estimation().
sbpiper_ps1 Main R function for SBpipe pipeline: parameter_scan1().
sbpiper_ps2 Main R function for SBpipe pipeline: parameter_scan2().
sbpiper_sim Main R function for SBpipe pipeline: simulate().
scatterplot Plot a generic scatter plot
scatterplot_log10 Plot a generic scatter plot in log10 scale
scatterplot_ple Plot a profile likelihood estimation (PLE) scatter plot
scatterplot_w_colour Plot a scatter plot using a coloured palette
skewness Calculate the skewness of a numeric vector
summarise_data Summarise the model simulation repeats in a single file.
tc_theme A theme for time courses. It extends ggplot2 theme_classic().