choose_quantile: Test gene expression variations with the quantile test for...

In CDecamps/penda: A R package that performs personalized differential analysis of comics data

Test gene expression variations with the quantile test for multiple quantile values in a serie of samples

Description

This function makes the quantile test for each patient with different values of quantile and factor. for each set of parameters, it computes FDR, TPR and FPR.

Usage

choose_quantile(
  controls,
  simulation,
  factor_values = c(1, 1.2, 1.4, 1.6, 1.8, 2),
  quantile_values = c(0.005, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1,
    0.2)
)

Arguments

controls	A matrix with genes expressions in controls for all the patients.
simulation	The list of initial data $initial_data and modified data in $simulated_data
factor_values	A vector with different values of factor. The down-regulated limit will be quantmin/factor, and the up-regulated limit quantmax*factor.
quantile_values	A vector with different values to test for the quantile.

Value

This function returns a matrix with 5 columns : the value of the factor test, the value of the quantile tested, the FDR, the TPR and the FPR of the test.

Examples

# First, load and define the data, make the simulation
controls = penda::penda_data_ctrl[1:10, 1:10]
simu_data = penda::penda_data_ctrl[1:10,11:16]
simulation = penda::simplified_simulation(simu_data, proportion = 0.3, threshold = 60)
# Second, choose the vectors of quantiles and factors to test
quantile_values = c(0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.15, 0.2, 0.25, 0.30, 0.35, 0.4, 0.45)
factor_values = c(1, 1.2, 1.4, 1.6, 1.8, 2)
# Third, make the test on simulation for different quantiles
which_quantile = penda::choose_quantile(controls,
                                simulation,
                                factor_values,
                                quantile_values)

CDecamps/penda documentation built on March 29, 2024, 3:26 a.m.