The R package SLEMI is designed to estimate channel capacity between finite state input and multidimensional output from experimental data. For efficient computations, it uses iterative algorithm based on logistic regression. The core function
capacity_logreg_main() is the basic interface to all functionalities provided in the package. A comprehensive documentation is available in directory
### Requirements - Hardware
+ A 32 or 64 bit processor (recommended: 64bit)
+ 1GHz processor (recommended: multicore for a comprehensive analysis)
+ 2GB MB RAM (recommended: 4GB+, depends on the size of experimental data)
### Requirements - Software
The main software requirement is the installation of the R environment (version: >= 3.6), which can be downloaded from R project website and is distributed for all common operating systems. We tested the package in R environment installed on Windows 7, 10; Mac OS X 10.11 - 10.13 and Ubuntu 18.04 with no significant differences in the performance. The use of a dedicated Integrated development environment (IDE), e.g. RStudio is recommended.
Apart from base installation of R, SLEMI requires following packages:
1. for installation
doParallel (if parallel computation are needed)
for data handling
install.packages("name_of_a_package")in the R console. ### Installation SLEMI is on R CRAN now. Please install using
In order to install directly from GitHub, use following commands in R's console
# install.packages("devtools") # run if not installed
## Basic usage
The package is based on a main wrapper function -
capacity_logreg_main() for calculation of channel capacity, which calls specific methods implemented within this package. Similarly, functions
mi_logreg_main() can be used to estimate mutual information, while
prob_discr_pairwise() to compute probabilities of discrimination between two different input states.
### Preparing data
For the calculation of channel capacity between X and Y you need structure experimental data into a single
data.frame object with observations in rows, one column with values of input (X), preferably of
factor type and columns with measured output (Y) of
In order to estimate channel capacity, using basic logistic regression model, call
capacity_logreg_main(dataRaw, signal, response, output_path)where:
dataRawis a data.frame with experimental data as described above
signalis a character indicating the name of column in
dataRawwith the input (X)
responseis a character vector indicating names of columns in
dataRawwith output (Y) variables
output_pathis a character with the directory, to which results of the estimation should be saved ### Results The function
capacity_logreg_mainreturns a list, whose main elements are * cc - channel capacity estimate (in bits) p_opt - numeric vector with the optimal input distribution model -
nnetobject describing fitted logistic regression model For convenience of further analysis, this list is saved in
output_pathdirectory in a file
output.rds. In addition to that, a set of exploratory graphs are created to visualise obtained estimates. ## Examples Additional examples of using package with some background on information theory is given in
paper/TestingProcedures.pdfand implemented in script
paper/testing_procedures.R. Codes used in publication are accessible from
paper/paper_SI.Rrespectively. ### Datasets In the manuscript describing methodological aspects of our algorithm we present the analysis of information transmission in NfkB pathway upon the stimulation of TNF-$\alpha$. Experimental data from this experiment in the form of single-cell time series are attached to the package as a data.frame object and can be accessed using
data_nfkbvariable. Each row of
data_nfkbrepresents a single observation of a cell. Column 'signal' indicates the level of TNF-$\alpha$ stimulation for a given cell, while columns 'response_T', gives the normalised ratio of nuclear and cytoplasmic transcription factor as described in Supplementary Methods of the corresponding publication. ## Other functionalities ### Additional parameters Apart from required arguments, the function
capacity_logreg_mainhas also other parameters than can be used to tune the activity of the algorithm. These are *
default=TRUE) - logical, specify if
nnetmodel object should be saved into output file
default=TRUE) - logical, controls creating diagnostic plots in the output directory.
default = 6) - numeric, the basic width of created plots
default = 4) - numeric, the basic height of created plots
default = TRUE) - logical, value indicating if the columns of
dataRaware to be centered and scaled, what is usually recommended for the purpose of stability of numerical computations. From a purely theoretical perspective, such transformation does not influence the value of channel capacity.
default = 1000) - (argument of
nnetpackage) a maximum number of iterations of optimisation step in logistic regression algorithm. Set to higher value if your data is more complex or of high dimension.
default = 5000) - (argument of
nnetpackage) a maximum number of parameters in logistic regression model. Set to higher value if you data has many dimensions or input has many states. ### Diagnostic procedures We implemented two diagnostic procedures to control the performance of channel capacity estimation and to measure uncertainty due to finite sample size and model over-fitting. These include: 1. Bootstrap test - capacity is re-calculated using $x$% of data, sampled from original dataset without replacement. After repeating procedure $n$ times, its standard deviation can be treated as an error of original estimate. 2. Over-fitting test - original data is divided into Training and Testing datasets. Then, logistic regression is estimated using $x$% of data (training dataset) and integrals of channel capacity are calculated via Monte Carlo using remaining $(1-x)$% of data (testing dataset). It is repeated $n$ times. In order to use those procedures, user must provide additional arguments to function
logreg_capacity_main(), i.e. * testing (default=FALSE) - a logical value that turn on/off testing mode, TestingSeed (default= 1234) - the seed for the random number generator used to sample original dataset, testing_cores (default= 4) - a number of cores to use (via
doParallelpackage) in parallel computing, boot_num (default= 40) - a number of repetitions of the bootstrap , boot_prob (default= 0.8) - a fraction of initial observations to use in the bootstrap, traintest_num (default= 40) - a number of repetitions of the overfitting test, partition_trainfrac (default= 0.6) - a fraction of initial observations to use as a training dataset in the overfitting test ## Support Please mail t.jetka at gmail.com in case of any bugs, problems and questions regarding package or inquiries regarding information theory. ## Reference Please cite Jetka T, Nienałtowski K, Winarski T, Błoński S, Komorowski M (2019) Information-theoretic analysis of multivariate single-cell signaling responses. PLOS Computational Biology 15(7): e1007132. https://doi.org/10.1371/journal.pcbi.1007132 ## License SLEMI is released under the GNU license and is freely available. A comprehensive documentation is available in directory
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