Package 'PressPurt' reference manual

Title:	Indeterminacy of Networks via Press Perturbations
Description:	This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.
Authors:	David Koslicki [aut, cre], Dana Gibbon [aut, trl], Mark Novak [aut]
Maintainer:	David Koslicki <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.2
Built:	2025-03-13 03:46:56 UTC
Source:	https://github.com/cran/PressPurt

Compute Entry Wise Perturbation Expectation

Description

This function computes the expected number of sign switches from perturbing each entry individually. Run after PreprocessMatrix().

Usage

ComputeEntryWisePerturbationExpectation(
  input_folder = NULL,
  PreProsMatrix = NULL,
  prefix = NULL,
  distribution_type = "truncnorm",
  input_a = 0,
  input_b = -2,
  threads = 1
)
ComputeEntryWisePerturbationExpectation(
  input_folder = NULL,
  PreProsMatrix = NULL,
  prefix = NULL,
  distribution_type = "truncnorm",
  input_a = 0,
  input_b = -2,
  threads = 1
)

Arguments

`input_folder`	Input folder. The location of the files created by PreprocessMatrix if you specified an output_folder. If this option is specified, this is also where the num switch array will be saved. Must specify an input_folder OR PreProsMatrix. Default: NULL
`PreProsMatrix`	Object where the PreprocessMatrix output was saved. Must specify an input_folder OR PreProsMatrix. Default: NULL
`prefix`	Prefix of output files, if you so choose.
`distribution_type`	Kind of distribution to use. Valid choices are: truncnorm, uniform, trunc_lognorm, beta. Default: “truncnorm”
`input_a`	First parameter to the distribution you choose. For truncnorm, this is the mean. Default: 0
`input_b`	First parameter to the distribution you choose. For truncnorm, this is the variance. Using a negative value indicates you want the standard deviation to be the length of the interval divided by the absolute value of the input parameter. Default: -2
`threads`	Number of threads to use. Default: 1

Value

If an input folder is specified the objects will be saved to that folder. If the PreProsMatrix object is specified, an R list object with the following: original_matrix, matrix_size, column_names, row_names, non_zero, num_switch_functions, asymptotic_stability_start, asymptotic_stability_end, num_switch_funcs_r, distributions, expected_num_switch, distributions_object

Examples

## Not run: 
# Set input file
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
# Preprocess the matrix
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)
# Run ComputeEntryWisePerturbationExpectation
Entrywise <- ComputeEntryWisePerturbationExpectation(PreProsMatrix = PreProsMatrix,
    distribution_type = "truncnorm", 
    input_a = 0, input_b = -2, threads = 1)

## End(Not run)
## Not run: 
# Set input file
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
# Preprocess the matrix
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)
# Run ComputeEntryWisePerturbationExpectation
Entrywise <- ComputeEntryWisePerturbationExpectation(PreProsMatrix = PreProsMatrix,
    distribution_type = "truncnorm", 
    input_a = 0, input_b = -2, threads = 1)

## End(Not run)

Compute Multi Entry Perturbation Expectation

Description

This function takes a jacobian matrix and computes the multi-entry perturbation expectation.

Usage

ComputeMultiEntryPerturbationExpectation(
  input_file,
  num_iterates = 1000,
  interval_length = 0.01,
  threads = 1
)
ComputeMultiEntryPerturbationExpectation(
  input_file,
  num_iterates = 1000,
  interval_length = 0.01,
  threads = 1
)

Arguments

`input_file`	Input comma separated file for the jacobian matrix.
`num_iterates`	Number of iterates in the Monte Carlo sampling to perform. Default: 10000
`interval_length`	Interval length over which to make the perturbations. Default: 0.01
`threads`	Number of threads to use. Default: 1

Value

returns a scalar

Examples

## Not run: 
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt") 
ComputeMultiEntryPerturbationExpectation(input_file = infile)

## End(Not run)
## Not run: 
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt") 
ComputeMultiEntryPerturbationExpectation(input_file = infile)

## End(Not run)

Make a new conda environment

Description

This function creates a new conda environment and initializes the new conda environment. In doing so, this function sets your python version and one may specify a specific python version. This is useful if you have multiple versions of python installed. When making a new conda environment, if the python version isn't set, then your default one will be used.

Usage

create_conda_env(condaenv, version = NULL, verbose = TRUE)
create_conda_env(condaenv, version = NULL, verbose = TRUE)

Arguments

`condaenv`	Specify conda environment name
`version`	Set path to specific version of python.
`verbose`	TRUE or FALSE. When TRUE, shows python and conda configuration. Default: TRUE

Value

None

Examples

## Not run: 
create_conda_env( 
    condaenv = "r-reticulate",
    version = "~/anaconda3/bin/python",
    verbose = TRUE)

## End(Not run)
## Not run: 
create_conda_env( 
    condaenv = "r-reticulate",
    version = "~/anaconda3/bin/python",
    verbose = TRUE)

## End(Not run)

Make a new virtual environment

Description

This function creates a new virtual environment and initializes the new virtual environment. In doing so, this function sets your python version and one may specify a specific python version. This is useful if you have multiple versions of python installed. When making a new virtual environment, if the python version isn't set, then your default one will be used.

Usage

create_virtual_env(virtualenv, version = NULL, verbose = TRUE)
create_virtual_env(virtualenv, version = NULL, verbose = TRUE)

Arguments

`virtualenv`	Specify conda environment name
`version`	Set path to specific version of python.
`verbose`	TRUE or FALSE. When TRUE, shows python and conda configuration. Default: TRUE

Value

None

Examples

## Not run: 
create_virtual_env(version = "/usr/bin/python3", 
    virtualenv = "r-reticulate", 
    verbose = TRUE)

## End(Not run)
## Not run: 
create_virtual_env(version = "/usr/bin/python3", 
    virtualenv = "r-reticulate", 
    verbose = TRUE)

## End(Not run)

Find Python versions, Conda, & Virtual Environments

Description

This function lists available python versions, conda environments, and virtual environments. One may show all three or just one.

Usage

find_python(python = TRUE, conda = TRUE, virtualenv = TRUE)
find_python(python = TRUE, conda = TRUE, virtualenv = TRUE)

Arguments

`python`	If TRUE will list available python versions. Default: TRUE
`conda`	If TRUE will list available conda environments. Default: TRUE
`virtualenv`	If TRUE will list available virtual environments. Default: TRUE

Value

None

Examples

## Not run: 
find_python()

## End(Not run)
## Not run: 
find_python()

## End(Not run)

Generate Entry Wise Figures

Description

This function plots the number of mis-predictions versus perturbation value, overlaid with distribution over stable perturbation values. Run after ComputeEntryWisePerturbationExpectation()

Usage

GenerateEntryWiseFigures(
  input_folder = NULL,
  EntryWise = NULL,
  prefix = NULL,
  all_numswitch_plots = FALSE,
  list_of_numswitch_to_plot = NULL
)
GenerateEntryWiseFigures(
  input_folder = NULL,
  EntryWise = NULL,
  prefix = NULL,
  all_numswitch_plots = FALSE,
  list_of_numswitch_to_plot = NULL
)

Arguments

`input_folder`	Input folder. The location of the files created by PreprocessMatrix if you specified an output_folder. This is also where the num switch array was saved. Must specify an input_folder OR EntryWise object. Default: NULL
`EntryWise`	Object where the ComputeEntryWisePerturbationExpectation output was saved.
`prefix`	Prefix of output files, if you so choose.
`all_numswitch_plots`	set to TRUE if you ant to plot all num switch plots (potentially very large). Default: FALSE
`list_of_numswitch_to_plot`	List of entries you want visualized with num switch. Should be a list of vectors. Example: list(c(0, 0), c(0, 1))

Value

plot or plots

Examples

## Not run: 
# Set input file
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
# Preprocess the matrix
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)

# Run ComputeEntryWisePerturbationExpectation
Entrywise <- ComputeEntryWisePerturbationExpectation(PreProsMatrix = PreProsMatrix,
    distribution_type = "truncnorm", 
    input_a = 0, input_b = -2, threads = 1)

# Plot specific entries using entrywise object
list_of_numswitch_to_plot <- list(c(1, 1), c(1, 2))
GenerateEntryWiseFigures(EntryWise=Entrywise, 
    all_numswitch_plots = FALSE, 
    list_of_numswitch_to_plot=list_of_numswitch_to_plot)
     

# Plot specific entries from folder
GenerateEntryWiseFigures(input_folder = "test_r/test3", 
    all_numswitch_plots = FALSE, 
    list_of_numswitch_to_plot=list_of_numswitch_to_plot)

# Plot all numswitch plots
GenerateEntryWiseFigures(EntryWise=Entrywise, 
    all_numswitch_plots = TRUE)

## End(Not run)
## Not run: 
# Set input file
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
# Preprocess the matrix
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)

# Run ComputeEntryWisePerturbationExpectation
Entrywise <- ComputeEntryWisePerturbationExpectation(PreProsMatrix = PreProsMatrix,
    distribution_type = "truncnorm", 
    input_a = 0, input_b = -2, threads = 1)

# Plot specific entries using entrywise object
list_of_numswitch_to_plot <- list(c(1, 1), c(1, 2))
GenerateEntryWiseFigures(EntryWise=Entrywise, 
    all_numswitch_plots = FALSE, 
    list_of_numswitch_to_plot=list_of_numswitch_to_plot)
     

# Plot specific entries from folder
GenerateEntryWiseFigures(input_folder = "test_r/test3", 
    all_numswitch_plots = FALSE, 
    list_of_numswitch_to_plot=list_of_numswitch_to_plot)

# Plot all numswitch plots
GenerateEntryWiseFigures(EntryWise=Entrywise, 
    all_numswitch_plots = TRUE)

## End(Not run)

Get PDF distribution

Description

This function retrieves the PDF (Probability Distribution Function) object from the scipy method <scipy.stats._distn_infrastructure.rv_frozen>.

Usage

get_distributions_single(
  matrix_entry,
  distribution_list,
  asymp_stab,
  points = 250
)
get_distributions_single(
  matrix_entry,
  distribution_list,
  asymp_stab,
  points = 250
)

Arguments

`matrix_entry`	Position in the matrix. Example: c(1, 1)
`distribution_list`	list of scipy distributions
`asymp_stab`	asymptotic stability interval
`points`	the number of values in x range

Value

Probability Distribution Function from scipy

Examples

## Not run: 
k <- 1
l <- 1
np <- reticulate::import("numpy")
distributions <- reticulate::py_load_object("distributions.pkl")
single_dist <- get_distributions_single(matrix_entry = c(k,l), 
    distribution_list = distributions, 
    asymp_stab = c(combined$asymptotic_stability_start[k,l], 
    combined$asymptotic_stability_end[k,l]))

## End(Not run)
## Not run: 
k <- 1
l <- 1
np <- reticulate::import("numpy")
distributions <- reticulate::py_load_object("distributions.pkl")
single_dist <- get_distributions_single(matrix_entry = c(k,l), 
    distribution_list = distributions, 
    asymp_stab = c(combined$asymptotic_stability_start[k,l], 
    combined$asymptotic_stability_end[k,l]))

## End(Not run)

Num Switch Function to step function

Description

This function transforms a Num Switch Function to a plot ready step function with x and y values. Returns a data frame of x and y values to plot.

Usage

ns_to_step(asymp_stab_start, asymp_stab_end, num_switch_func)
ns_to_step(asymp_stab_start, asymp_stab_end, num_switch_func)

Arguments

`asymp_stab_start`	start interval from asymptotic_stability
`asymp_stab_end`	end interval from asymptotic_stability
`num_switch_func`	a single num switch function

Value

plot ready x and y values from the Num Switch Function

Examples

## Not run: 
# Set input file
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
# Preprocess the matrix
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)

# Run ComputeEntryWisePerturbationExpectation
Entrywise <- ComputeEntryWisePerturbationExpectation(
    PreProsMatrix = PreProsMatrix,
    distribution_type = "truncnorm", 
    input_a = 0, input_b = -2, threads = 1)

ns_step <- ns_to_step(
    asymp_stab_start = Entrywise$asymptotic_stability_start[1,1],
    asymp_stab_end = Entrywise$asymptotic_stability_end[1,1],
    num_switch_func = Entrywise$num_switch_funcs_r$`(1, 1)`)

## End(Not run)
## Not run: 
# Set input file
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
# Preprocess the matrix
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)

# Run ComputeEntryWisePerturbationExpectation
Entrywise <- ComputeEntryWisePerturbationExpectation(
    PreProsMatrix = PreProsMatrix,
    distribution_type = "truncnorm", 
    input_a = 0, input_b = -2, threads = 1)

ns_step <- ns_to_step(
    asymp_stab_start = Entrywise$asymptotic_stability_start[1,1],
    asymp_stab_end = Entrywise$asymptotic_stability_end[1,1],
    num_switch_func = Entrywise$num_switch_funcs_r$`(1, 1)`)

## End(Not run)

Preprocess Matrix

Description

This script pre-processes a matrix by figuring out what the intervals of asymptotic stability are, as well as finding which perturbation values lead to a sign switch.

Usage

PreprocessMatrix(
  input_file,
  output_folder = NULL,
  prefix = NULL,
  max_bound = 10,
  zero_perturb = FALSE,
  threads = 1,
  verbose = FALSE
)
PreprocessMatrix(
  input_file,
  output_folder = NULL,
  prefix = NULL,
  max_bound = 10,
  zero_perturb = FALSE,
  threads = 1,
  verbose = FALSE
)

Arguments

`input_file`	Input comma separated file for the jacobian matrix.
`output_folder`	Optional output folder to save python objects to disk. A number of files will be created in the form ‘output_folder/<prefix>_*.npy’. Default is NULL.
`prefix`	Prefix of output files, if you so choose.
`max_bound`	some of the matrices are unbounded stable towards one end, this is the limit the user imposes. Default: 10
`zero_perturb`	Flag to indicate you want to perturb the zero entries. Default: FALSE
`threads`	Number of threads to use. Default: 1
`verbose`	Default: FALSE

Value

A list of with the following objects: matrix_size, column_names, row_names, non_zero, num_switch_functions, asymptotic_stability_start, asymptotic_stability_end, num_switch_funcs_r

Examples

## Not run: 
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt") 
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)

## End(Not run)
## Not run: 
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt") 
PreProsMatrix <- PreprocessMatrix(input_file = infile, 
    output_folder = NULL, max_bound = 10, threads = 2)

## End(Not run)

Convert data to R format if saved to disk

Description

This function will convert objects saved to disk to R friendly objects, or the same output as ComputeEntryWisePerturbationExpectation. If you used the "save to disk" option or ran via python directly, run this function to read the data into R. Files read in: asymptotic_stability.npy, column_names.txt, distributions.pkl, expected_num_switch.csv, num_non_zero.npy, num_switch_funcs.pkl, row_names.txt and size.npy. Note how most of these objects are python based objects- numpy or pickle objects.

Usage

process_data(matrix, type = "csv", folder, prefix = NULL)
process_data(matrix, type = "csv", folder, prefix = NULL)

Arguments

`matrix`	path to the original matrix.
`type`	csv or tab. Is the original matrix comma separated or tab separated? Default: csv
`folder`	path to the folder where output data was saved.
`prefix`	optional prefix to file names

Value

object formatted in the same way the output of ComputeEntryWisePerturbationExpectation

Examples

## Not run: 
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
data <- process_data(matrix = infile, 
    type = "csv", folder = "output")

## End(Not run)
## Not run: 
infile <- system.file("extdata", "Modules", "IGP.csv", 
    package = "PressPurt")
data <- process_data(matrix = infile, 
    type = "csv", folder = "output")

## End(Not run)

Install Python Dependencies

Description

This function installs needed python libraries into the specified conda environment OR virtual environment. Should be the same as the one specified in set_python. Required python libraries: matplotlib, numpy, pandas, pathos, scipy and sympy On CentOS 7 pandas & scipy may need to be installed with pip install from the command line. Will get the error: /lib/libstdc++.so.6: version ‘CXXABI_1.3.9’ not found See vignette for more information.

Usage

py_depend(condaenv = NULL, virtualenv = NULL)
py_depend(condaenv = NULL, virtualenv = NULL)

Arguments

`condaenv`	Name of conda environment to install python libraries to. Default: NULL
`virtualenv`	Name of virtual environment to install python libraries to. Default: NULL

Value

None

Examples

## Not run: 
# Cond env
py_depend(condaenv = "r-reticulate", 
    virtualenv = NULL)
# virtualenv:
py_depend(virtualenv = "r-reticulate", 
    condaenv = NULL)

## End(Not run)
## Not run: 
# Cond env
py_depend(condaenv = "r-reticulate", 
    virtualenv = NULL)
# virtualenv:
py_depend(virtualenv = "r-reticulate", 
    condaenv = NULL)

## End(Not run)

Set Python Conda environment

Description

This function sets your conda environment. Run this command before PreprocessMatrix. Install python dependencies in the same conda environment that you set here. To make a new conda environment use the create_conda_env function.

Usage

set_python_conda(condaenv, verbose = TRUE)
set_python_conda(condaenv, verbose = TRUE)

Arguments

`condaenv`	Specify conda environment name
`verbose`	TRUE or FALSE. When TRUE, shows python and conda configuration. Default: TRUE

Value

None

Examples

## Not run: 
set_python_conda(
    condaenv = "r-reticulate", 
    verbose = TRUE)

## End(Not run)
## Not run: 
set_python_conda(
    condaenv = "r-reticulate", 
    verbose = TRUE)

## End(Not run)

Set your Python Virtual environment

Description

This function sets your virtual environment. Run this command before PreprocessMatrix. Install python dependencies in the same virtual environment that you set here. To make a new virtual environment use the create_virtual_env function.

Usage

set_python_virtual(virtualenv, verbose = TRUE)
set_python_virtual(virtualenv, verbose = TRUE)

Arguments

`virtualenv`	Specify virtual environment name
`verbose`	TRUE or FALSE. When TRUE, shows python and virtual environment configuration. Default: TRUE

Value

None

Examples

## Not run: 
set_python_virtual(
    virtualenv = "r-reticulate", 
    verbose = TRUE)

## End(Not run)
## Not run: 
set_python_virtual(
    virtualenv = "r-reticulate", 
    verbose = TRUE)

## End(Not run)

Package 'PressPurt'

Help Index

Compute Entry Wise Perturbation Expectation

Description

Usage

Arguments

Value

Examples

Compute Multi Entry Perturbation Expectation

Description

Usage

Arguments

Value

Examples

Make a new conda environment

Description

Usage

Arguments

Value

Examples

Make a new virtual environment

Description

Usage

Arguments

Value

Examples

Find Python versions, Conda, & Virtual Environments

Description

Usage

Arguments

Value

Examples

Generate Entry Wise Figures

Description

Usage

Arguments

Value

Examples

Get PDF distribution

Description

Usage

Arguments

Value

Examples

Num Switch Function to step function

Description

Usage

Arguments

Value

Examples

Preprocess Matrix

Description

Usage

Arguments

Value

Examples

Convert data to R format if saved to disk

Description

Usage

Arguments

Value

Examples

Install Python Dependencies

Description

Usage

Arguments

Value

Examples

Set Python Conda environment

Description

Usage

Arguments

Value

Examples

Set your Python Virtual environment

Description

Usage

Arguments

Value

Examples