R/Data.R

In JointNets: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application

#' A simulated toy dataset that includes 2 data matrices (from 2 related
#' tasks).
#'
#' A simulated toy dataset that includes 2 data matrices (from 2 related
#' tasks). Each data matrix is about 100 features observed in 200 samples. The
#' two data matrices are about exactly the same set of 100 features. This
#' multi-task dataset is generated from two related random graphs. Please run
#' demo(diffee) to learn the basic functions provided by this package.  For
#' further details, please read the original paper:
#' <http://link.springer.com/article/10.1007/s10994-017-5635-7>.
#'
#'
#' @name exampleData
#' @docType data
#' @format The format is: List of 2 matrices $ : num (1:200, 1:100) -0.0982
#' -0.2417 -1.704 0.4 ...  ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL ..
#' ..$ : NULL $ : num (1:200, 1:100) -0.161 0.41 0.17 0. ...  ..- attr(*,
#' "dimnames")=List of 2 .. ..$ : NULL .. ..$ : NULL
#' @keywords datasets
#' @usage data(exampleData)
NULL


#' A simulated toy dataset that includes 3 igraph objects
#'
#' (first one being the shared graph and second and third being task specific 1 and 2 graphs)
#' The graphs are generated from two related random graphs and the underlaying high dimensional gaussian distribution
#' generates the exampleData dataset. exampleDataGraph serves as a groundtruth to compare in demo(synthetic).
#'
#'
#' @name exampleDataGraph
#' @docType data
#' @format A list of 3 igraph objects
#' @keywords datasets
#' @usage data(exampleDataGraph)
NULL

#' Microarray data set for breast cancer
#'
#' % This gene expression data set is freely available, coming from the Hess
#' \emph{et al}'s paper. It concerns one hundred thirty-three patients with
#' stage I--III breast cancer.  Patients were treated with chemotherapy prior
#' to surgery. Patient response to the treatment can be classified as either a
#' pathologic complete response (pCR) or residual disease (not-pCR). Hess
#' \emph{et al} developed and tested a reliable multigene predictor for
#' treatment response on this data set, composed by a set of 26 genes having a
#' high predictive value.
#'
#' The dataset splits into 2 parts (pCR and not pCR), on which network
#' inference algorithms should be applied independently or in the multitask
#' framework: only individuals from the same classes should be consider as
#' independent and identically distributed.
#'
#'
#' @name cancer
#' @docType data
#'
#' @format a list of two objects: dataframe with 133 observations of 26 features and
#' factors indicating whether each sample (out of 133) is of type "not" or type "pcr"
#'
#'
#' @references % K.R. Hess, K. Anderson, W.F. Symmans, V. Valero, N. Ibrahim,
#' J.A. Mejia, D. Booser, R.L. Theriault, U.  Buzdar, P.J. Dempsey, R. Rouzier,
#' N. Sneige, J.S. Ross, T. Vidaurre, H.L. Gomez, G.N. Hortobagyi, and L.
#' Pustzai (2006). Pharmacogenomic predictor of sensitivity to preoperative
#' chemotherapy with Paclitaxel and Fluorouracil, Doxorubicin, and
#' Cyclophosphamide in breast cancer, \emph{Journal of Clinical Oncology}, vol.
#' 24(26), pp. 4236--4244.
#' @keywords datasets
#' @usage data(cancer)
NULL

#' NIPS word count dataset
#'
#' This NIPS Conference Papers 1987-2015 Data set is avaiable at UCI Machine Learning Repository.
#' The original dataset is in the form of a 11463 x 5812 matrix of word counts (11463 words and 5812 conference papers)
#' Due to the size of the original dataset, it is preprocessed and reduced to a list of two matrices (2900 x 37 and 2911 x 37)
#' The dataset consists of two tasks (early (up to 2006) and recent (after 2006) NIPS conference papers) with 37 words
#'
#'
#' @name nip_37_data
#' @docType data
#' @references 'Poisson Random Fields for Dynamic Feature Models'. Perrone V., Jenkins P. A., Spano D., Teh Y. W. (2016)
#' @format a list of two nonnegative integer matrices (1:2900, 1:37) and (1:2911,1:37)
#' Columns are named with year_paperid and rows are names with word name
#' @keywords datasets
#' @usage data(nip_37_data)
NULL

#' AAL116 brain atlas coordinates in MNI space
#'
#' Automated Anatomical Labeling (AAL): The AAL atlas distributed with the AAL Toolbox was fractionated to functional
#' resolution (3x3x3 mm3) using nearest-neighbor interpolation.
#' This data is available at <http://preprocessed-connectomes-project.org/abide/Pipelines.html> as part of ABIDE-preprocessed dataset.
#' It can be directly downloaded at <https://fcp-indi.s3.amazonaws.com/data/Projects/ABIDE_Initiative/Resources/aal_roi_atlas.nii.gz>
#'
#'
#'
#' @name aal116coordinates
#' @docType data
#' @references Cameron Craddock, Yassine Benhajali, Carlton Chu, Francois Chouinard, Alan Evans, András Jakab, Budhachandra Singh Khundrakpam, John David Lewis, Qingyang Li, Michael Milham, Chaogan Yan, Pierre Bellec (2013). The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. In Neuroinformatics 2013, Stockholm, Sweden.
#' @format 116 observations (Brain Region Names) of 7 variables (name, x.mni, y.mni, z.mni, lobe, hemi, index)
#' @keywords datasets
#' @usage data(aal116coordinates)
NULL


#' ABIDE I preprocessed time series grouped by control and autism and partitioned by AAL116 atlas
#'
#' This time series data is available as part of Autism Brain Imaging Data Exchange (ABIDE).
#' ABIDE is a collaboration of 16 international imaging sites that have aggregated and are openly
#' sharing neuroimaging data from 539 individuals suffering from ASD and 573 typical controls.
#' For data access, please refer to <http://preprocessed-connectomes-project.org/abide/download.html>.
#' The data is preprocessed, cancatnated and organized into two data matrices for easy input.
#'
#' @name ABIDE_aal116_timeseries
#' @docType data
#' @references Cameron Craddock, Yassine Benhajali, Carlton Chu, Francois Chouinard, Alan Evans, András Jakab, Budhachandra Singh Khundrakpam, John David Lewis, Qingyang Li, Michael Milham, Chaogan Yan, Pierre Bellec (2013). The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. In Neuroinformatics 2013, Stockholm, Sweden.
#' @format a list of two data matrices of time series(1:2250, 1:116) and (1:2060,1:116)
#' @keywords datasets
#' @usage data(ABIDE_aal116_timeseries)
NULL