R/CircadianTools.R

In nathansam/CircadianTools: Collection of Tools for Detecting Rhythmic Genes

#' CircadianTools: A Collection of Tools for Detecting Rhythmic Genes
#'
#' Allows analysis of rhythmic genes to be easily carried out on transcriptomics
#' data using R. Designed to be as flexible as possible such as by allowing an
#' unequal number of replicates. \cr \cr
#'------------------------------------------------------------------------------
#' @section Simple Plots:
#' \code{\link{BasicPlot}}: Plots activity data as points and average activity
#'  as lines \cr \cr
#'
#' \code{\link{CompPlot}}: Plots two genes from a gene activity dataset. \cr \cr
#'
#' \code{\link{DatasetPlot}}: Saves plots of all genes in a dataset. \cr \cr
#'
#' \code{\link{TurningPlot}}: Fits a spline to a given gene in a given dataset.
#'  Finds the turning points and then plots the turning points and spline.
#'  \cr \cr
#'------------------------------------------------------------------------------
#' @section Clustering:
#'
#' \code{\link{AgglomClustering}}: Applies hierarchical clustering, clustering to a
#'  transcriptomics dataset and appends a cluster column to this dataset for all
#'  genes. \cr \cr
#'
#' \code{\link{AgglomParamSelection}}: Runs hierarchical clustering with
#'  differing numbers of partitions and returns validation metrics. \cr \cr
#'
#' \code{\link{ClusterCenterGenerator}}: Finds the center of every cluster in a
#'  dataset \cr \cr
#'
#' \code{\link{ClusterCorDatasetPlot}}: Uses \link{ClusterCorPlot} to plot all
#'  of the clusters generated by a clustering method when absolute
#'  Pearson's correlation was used as a distance measure. \cr \cr
#'
#'  \code{\link{ClusterCorPlot}}: Plots the activity level for a cluster
#'  generated by using absolute Pearson's correlation as a distance measure.
#'  Plots positively and negatively correlated genes as two different lines.
#'  \cr \cr
#'
#' \code{\link{ClusterDatasetPlot}}: Plots the mean and error bars for all
#'  clusters across time. \cr \cr
#'
#' \code{\link{ClusterParamSelection}}: Calculates validation metrics for
#'  different clustering methods and different numbers of partitions.
#'  The validation metrics are plotted. \cr \cr
#'
#' \code{\link{ClusterPlot}}: Plots the mean and error bars for the genes in a
#'  cluster across time. \cr \cr
#'
#' \code{\link{ClusterSpread}}: Shows how many genes are in each cluster after
#'  clustering has been applied. \cr \cr
#'
#' \code{\link{ClusterText}}: Takes a dataframe of clusters and stores the name
#'  of all genes in a text file. The row number deontes the cluster number. \cr
#'  \cr
#'
#' \code{\link{ClusterTimeProfile}}: Provides a dataframe of median values at
#'  each time point for each cluster. \cr \cr
#'
#' \code{\link{CommonSingletonFinder}}: Finds the genes which belong to common
#'  singleton clusters in two different clustered datasets. \cr \cr
#'
#'  \code{\link{DendogramDatasetPlot}}: Plots the dendogram for every cluster in
#'   a clustered dataset. \cr \cr
#'
#' \code{\link{DendogramPlot}}: Plots the dendogram for a cluster in a
#'  clustered dataset \cr \cr
#'
#' \code{\link{DianaClustering}}: Applies DIANA (DIvisive ANAlysis) clustering
#'  to a transcriptomics dataset and appends a cluster column to this dataset
#'  for all genes. \cr \cr
#'
#' \code{\link{DianaParamSelection}}:  Runs DIANA (DIvisive ANAlysis) clustering
#'  with differing numbers of partitions and returns validation metrics. \cr \cr
#'
#' \code{\link{FindClusterDistanceQuantiles}}: Finds The distances between the
#'  center of each cluster and the centers of all the other clusters. \cr \cr
#'
#' \code{\link{FindClusterMedian}}: Finds the center of a cluster \cr \cr
#'
#' \code{\link{FindClusterQuantile}}: Finds The distances between the center of
#'  a cluster and the centers of all other clusters. \cr \cr
#'
#'  \code{\link{MDSPlot}}: Applies multidimensional scaling to a clustered
#'   transcriptomics dataset to reduce the clusters to two dimensions and
#'    then plots the clusters.  \cr \cr
#'
#' \code{\link{PamClustering}}: Applies PAM (Partitioning around Medoids)
#'  clustering to a transcriptomics dataset and appends a cluster column to this
#'  dataset for all genes. \cr \cr
#'
#' \code{\link{PamParamSelection}}: Runs PAM with differing numbers of
#'  partitions and returns validation metrics. \cr \cr
#'
#' \code{\link{QuantilePlots}}: Finds the quartiles for intercluster distances
#'  and plots these distances as a set of histograms \cr \cr
#'
#' \code{\link{SingletonNameFinder}}: Finds the genes which belong to
#'  singleton clusters. \cr \cr
#'  ----------------------------------------------------------------------------
#' @section Correlation:
#' \code{\link{CorAnalysis}}: Ranks correlation between a given gene and all
#'  other genes in a dataset. Plots both the given gene and highly correlated
#'  genes for a given correlation value. \cr \cr
#'
#' \code{\link{CorAnalysisCluster}}: Correlates the average activity of a
#'  cluster with the average activity of every other cluster. \cr \cr
#'
#' \code{\link{CorAnalysisClusterDataset}}: Correlates the average activity of
#'  each cluster with every other cluster in a dataset. \cr \cr
#'
#' \code{\link{CorAnalysisDataset}}: Correlates every gene in a dataset with
#'  every other gene in the same dataset. Allows a timelag between genes to be
#'  correlated. \cr \cr
#'
#' \code{\link{CorAnalysisPar}}: Parallel Implementation of \code{CorAnalysis}.
#'  \cr \cr
#'
#'
#' \code{\link{CorSignificantPlot}}: Prints or saves the genes found to be
#'  most significant by \code{CorAnalysis}. \cr \cr
#'  ----------------------------------------------------------------------------
#' @section Cosinor:
#' \code{\link{CosinorAnalysis}}: Fits cosinor models to transcriptomics data
#'  and plots the best-fitting models using ggplot2. \cr \cr
#'
#' \code{\link{CosinorAnalysisPar}}: Parallel Implementation of
#' \code{CosinorAnalysis}. \cr \cr
#'
#' \code{\link{CosinorPlot}}: Fits a cosinor model to a given gene in a given
#'  dataset and plots the model. \cr \cr
#'
#' \code{\link{CosinorResidualDatasetPlot}}: Fits cosinor models and plots the
#'  residuals for multiple genes in a dataset \cr \cr
#'
#' \code{\link{CosinorResidualPlot}}: Fits a cosinor model to a gene and plots
#'  the residuals \cr \cr
#'
#' \code{\link{CosinorSignificantPlot}}: Prints or saves the genes found to be
#'  most significant by \code{cosinoranalysis}. \cr \cr
#'
#' \code{\link{MultiCosinorTest}}: Fits a cosinor model and carries out ANOVA
#'  using raw coefficients. Then fits a cosinor model with additonal sine and
#'  cosine terms with a different period. ANOVA tests are carried out on the
#'  more complex model as well as directly comparing the two models. \cr \cr
#'  ----------------------------------------------------------------------------
#' @section Cytoscape:
#' \code{\link{CytoscapeFile}}: Converts a correlation dataframe object into a
#'  format suitable for cytoscape and saves as a csv file. \cr \cr
#'
#' \code{\link{CytoscapeFilter}}: Reduces the size of a file intended for
#' Cytoscape by filtering out the genes/clusters which are not correlated
#' \cr \cr
#' -----------------------------------------------------------------------------
#' @section Fasta Files:
#' \code{\link{ContigGen}}: Finds all unique contig IDs in a transcriptomics
#'  dataset \cr \cr
#'
#' \code{\link{FastaSub}}: Creates a fasta file from only certain sequences in
#'  another fasta file \cr \cr
#'  ----------------------------------------------------------------------------
#' @section Filtering:
#' \code{\link{AnovaFilter}}: Filters a gene activity dataframe via ANOVA.
#' \cr \cr
#'
#' \code{\link{CombiFilter}}: Filters a transcriptomics dataset by using
#'  \code{Zerofilter}, \code{Anovafilter} and \code{Sizefilter}. \cr \cr
#'
#' \code{\link{SizeFilter}}: Filters the genes with the smallest range from a
#'  transcriptomics dataset. \cr \cr
#'
#' \code{\link{TFilter}}: Applies a filter where a t-test is carried out on gene
#'  activity levels between time points. \cr \cr
#'
#' \code{\link{ZeroFilter}}: Filters a transcriptomics dataset such that there
#'  is a minimum number of non-zero activity readings for each gene in the
#'  resultant dataset. \cr \cr
#'  ----------------------------------------------------------------------------
#' @section RAIN:
#' \code{\link{RainAnalysis}}: Carries out RAIN analysis on a transcriptomics
#'  dataset. \cr \cr
#'
#' \code{\link{RainSignificantPlot}}: Prints or saves plots of the genes found
#'  to be most significant by \code{rainanalysis}. \cr \cr
#' -----------------------------------------------------------------------------
#' @section Utility Functions:
#' \code{\link{AbsCorDist}}: Calculates a distance matrix based on the distance
#'  measure of: \deqn{1 - |cor(x, y)|} \cr \cr
#'
#' \code{\link{ActivitySelect}}: Returns gene activity by either gene name or
#'  row number \cr \cr
#'
#' \code{\link{FileConflict}}: Checks if a file which will be created already
#'  exists and, if necessary asks the user if this file should be overwritten.
#'  \cr \cr
#'
#' \code{\link{GeneClean}}: Removes columns and rows which show no gene activity
#'  over time. \cr \cr
#'
#' \code{\link{GeneRange}}: Finds the range of gene activity for each gene in a
#'  dataframe. The median for the replicates is used for each time point.
#'  \cr \cr
#'
#' \code{\link{GeneScale}}: Centers/scales every gene in a transcriptomics
#'  dataset. \cr \cr
#'
#' \code{\link{GeneSub}}: Takes an object where the first column is genenames
#'  (IE a column of known Circadian genes) and subsets from a dataset containing
#'  activity readings for these genes. \cr \cr
#'
#' \code{\link{ggplot.cosinor.lm}}: Adapted from the Cosinor package by
#' Michael Sachs. Given a cosinor.lm model fit, generate a plot of the data with
#' the fitted values.\cr \cr
#'
#' \code{\link{MakeTimevector}}: Produces a vector of time values for the gene
#'  activity readings. \cr \cr
#'
#' \code{\link{MedList}}: Provides a dataframe of median values at each
#'  time point for each gene from a transcriptomics dataset. \cr \cr
#'
#' \code{\link{TAnalysis}}: A t.test is carried out on gene
#'  activity levels between time points and the number of significant increases
#'  & decreases is returned. \cr \cr
#'
#' @docType package
#' @name CircadianTools

NULL