R/data.R

In SLEMI: Statistical Learning Based Estimation of Mutual Information

#' Exemplary data set I
#'
#' A dataset describing simple one dimensional input - one dimensional output channel 
#' with 500 observations per input. In addition, each observation is assigned to one of three types
#' that occurs with propensities (0.6,0.3,0.1), respectively 
#' Conditional output distributions are Gaussian.
#' 
#' @format A data frame with 1500 rows and 3 variables:
#' \describe{
#' \item{signal}{Label of input}
#' \item{response}{The value of output}
#' \item{sideVar}{Label of the type of given observation}
#' }
#' @source synthetic
#' @keywords internal
"data_example1"

#' Exemplary data set II
#'
#' A dataset describing a channel with 3 possible inputs and 3-dimensional output
#' with 500 observations per input.
#' Conditional output distributions are multivariate Gaussians.
#' 
#' @format A data frame with 1500 rows and 4 variables:
#' \describe{
#' \item{signal}{Label of input}
#' \item{X1}{The value of first dimension of output}
#' \item{X2}{The value of second dimension of output}
#' \item{X3}{The value of third dimension of output}
#' }
#' @source synthetic
#' @keywords internal
"data_example2"


#' Data from experiment with NFkB pathway
#'
#' In the paper describing methodological aspects of our algorithm we present the analysis of information transmission 
#' in NfkB pathway upon the stimulation of TNF-\eqn{\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_nfkb` variable.

#' Each row of `data_nfkb` represents a single observation of a cell. Column 'signal' indicates the level of TNF-\eqn{\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. In the CRAN version of the package
#' we included only a subset of the data (5 time measurements). For the full datasets, please access GitHub pages. 
#'
#' For each concentration, there are at least 1000 single-cell observation (with the exception of 0.5ng stimulation, 
#' where the number of identified cells is almost 900)
#' @format A data frame with 15632 rows and 6 variables:
#' \describe{
#' \item{signal}{Level of TNFa stimulation}
#' \item{response_0}{The concentration of normalised NfkB transcription factor, measured at time 0}
#' \item{response_3}{The concentration of normalised NfkB transcription factor, measured at time 3}
#' \item{response_21}{The concentration of normalised NfkB transcription factor, measured at time 21}
#' \item{response_90}{The concentration of normalised NfkB transcription factor, measured at time 90}
#' \item{response_120}{The concentration of normalised NfkB transcription factor, measured at time 120}
#' #' }
#' @source in-house experimental data
"data_nfkb"