Nothing
R/regenrichClasses.R
In RegEnrich: Gene regulator enrichment analysis
Defines functions
RegenrichSet
newScore
newEnrich
newTopNetwork
newDeaSet
Documented in
newDeaSet
newScore
newTopNetwork
RegenrichSet
#' DeaSet class
#'
#'
#' @slot colData DataFrame object, sample information, the row name is
#' corresponding to the column names of expression matrix in the \code{assays}
#' slot.
#' @slot assays SimpleList object of one/multiple matrix/matrices,
#' this is the slot for storing the expression data after filtering
#' (and after Variance Stabilizing Transformation, i.e. VST, if the
#' differential analysis method is 'Wald_DESeq2' or 'LRT_DESeq2'). And the
#' expression matrix is used for network inference and plotting.
#' @slot NAMES row names of expression data in \code{assays} slot and
#' \code{elementMetadata} slot.
#' @slot elementMetadata feature information, contains at least a DataFrame
#' of three columns, i.e. `gene`, `p` and `logFC`, which stores gene names/IDs,
#' differential p values and log2 expression fold changes, respectively.
#' @slot metadata DataFrame object, information of feature columns.
#' @slot assayRaw a slot for saving the raw expression data.
#' @import SummarizedExperiment
#' @export
#' @examples
# \donttest{
#' nrows = 100
#' ncols = 6
#' counts = matrix(rnbinom(nrows * ncols, size = 2, mu = 500),
#' nrow = nrows)
#' assays = SimpleList(assayData = counts)
#'
#' colData = DataFrame(Condition = rep(c("treatment", "ctrl"), 3),
#' row.names=LETTERS[1:6])
#' geneNames = sprintf("G%03s", seq(nrows))
#' elementMetadata = DataFrame(gene = geneNames,
#' p = numeric(nrows),
#' logFC = numeric(nrows))
#'
#' ds = new("DeaSet",
#' assays = Assays(assays),
#' colData = colData,
#' assayRaw = counts,
#' elementMetadata = elementMetadata,
#' NAMES = geneNames)
#' ds
# }
#'
#'
setClass(Class = "DeaSet",
contains = "SummarizedExperiment",
representation = representation(
assayRaw = "matrix"),
prototype = prototype(assayRaw = matrix(nrow = 0, ncol = 0),
elementMetadata = DataFrame(gene = character(0),
p = numeric(0),
logFC = numeric(0))),
validity = function(object){
error = character()
if(!all(c("gene", "p", "logFC") %in% colnames(mcols(object)))){
error = c(error, paste0("mcols(object) must contain 'gene', ",
"'p', 'logFC' columns.\n"))
}
return(if(length(error) == 0) TRUE else error)
})
#' DeaSet object creator
#'
#' @param assayRaw A matrix of gene expression data. This can be the same
#' as the matrix-like element in \code{assays} parameter.
#' @param rowData A DataFrame object describing the rows.
#' @param assays A list or SimpleList of matrix-like element,
#' or a matrix-like object. The matrix-like element can be the same
#' as \code{assayRaw} parameter.
#' @param colData A DataFrame describing the sample information.
#' @param metadata An optional list of arbitrary content describing the
#' overall experiment.
#' @return A DeaSet object.
#'
#' @export
#' @examples
#' # Empty DeaSet object
#' newDeaSet()
#'
#' # 100 * 6 DeaSet object
#' nrows = 100
#' ncols = 6
#' counts = matrix(rnbinom(nrows * ncols, size = 2, mu = 500),
#' nrow = nrows)
#' assays = SimpleList(counts=counts)
#'
#' colData = DataFrame(Condition = rep(c("treatment", "ctrl"), 3),
#' row.names=LETTERS[1:6])
#' geneNames = sprintf("G%03s", seq(nrows))
#' elementMetadata = DataFrame(gene = geneNames,
#' p = numeric(nrows),
#' logFC = numeric(nrows))
#' newDeaSet(assayRaw = counts,
#' rowData = elementMetadata,
#' assays = SimpleList(assayData = counts),
#' colData = colData)
newDeaSet = function(assayRaw = matrix(nrow = 0, ncol = 0),
rowData = NULL,
assays = SimpleList(),
colData = DataFrame(),
metadata = list()){
if(is.null(rowData)){
rowData = DataFrame(gene = character(0),
p = numeric(0),
logFC = numeric(0))
}
se = SummarizedExperiment(assays = assays,
rowData = rowData,
colData = colData,
metadata = metadata)
new("DeaSet",
assayRaw = assayRaw,
assays = Assays(assays),
elementMetadata = rowData,
colData = colData,
NAMES = se@NAMES,
metadata = metadata)
}
#' TopNetwork class
#'
#' The `TopNetwork` object is to store either a full network (the percentage
#' of top edges is 100%) or a sub-network (with percentage of top edges
#' between 0 to 10).
#' @slot element tibble, the pool of targets in the network.
#' @slot set tibble, the pool of valid regulators.
#' @slot elementset tibble, regulator-target edges with edge weights.
# @slot net list, in which the names of elements are regulators,
# and the elements are targets of the regulators indicated by the
# element name.
# @slot netTable data frame. A table showing regulator-target
# relationship. It contains 3 columns, representing 'from.gene'
# ('regulators'), 'to.gene' ('targets') and 'weight', respectively.
# @slot validReg vector, a set of valid regulators in the network.
# @slot tarReg list, in which the names of elements are targets,
#' and the elements are regulators of the targets indicated by the
#' element name.
#' @slot directed logical, whether the network is directed.
#' @slot networkConstruction character, by which method this network
#' is constructed. Either 'COEN' (coexpression network using WGCNA),
#' or 'GRN' (gene regulatory network using random forest), or 'new'
#' (a network provided by the user).
#' @slot percent numeric, what percentage of the top edges are remained.
#' The value must be between 0 (excluding) and 100 (including).
#' @slot active character, which data table is activated, the default is
#' "elementset".
#' @importClassesFrom BiocSet BiocSet
#' @export
setClass(Class = "TopNetwork",
contains = "BiocSet",
representation = representation(
directed = "logical",
networkConstruction = "character",
percent = "numeric"),
prototype = prototype(# net = list(),
directed = TRUE,
networkConstruction = "new",
percent = 100,
active = "elementset"),
validity = function(object) {
if (object@percent > 100 || object@percent <= 0) {
"@percent must be between 0 (excluding) and 100 (including)"
} else {
if (length(object@networkConstruction) != 1 ||
!object@networkConstruction %in%
c("COEN", "GRN", "new")) {
"@networkConstruction must be one of 'COEN', 'GRN', and 'new'"
} else {
TRUE
}
}
})
#' TopNetwork object creator
#'
#' This function create `TopNetwork` object using 3-column edge table.
#'
#' @param networkEdgeTable a data frame of 3 columns, representing 'from.gene'
#' ('regulators'), 'to.gene' ('targets') and 'weight', respectively.
#' @param reg a vector of gene regulators.
#' @param directed logical, whether the network is directed. Default is TRUE.
#' @param networkConstruction the method to construct this network.
#' Possible can be:
#' 'COEN', coexpression network;
#' 'GRN', gene regulatory network by random forest;
#' 'new' (default), meaning a network provided by user, rather than
#' infered based
#' on the expression data.
#' @param percent the percentage of edges in the original whole network.
#' Default is 100, meaning 100\% edges in whole network.
# @param extendObject logical, whether net and tarReg will be constructed.
# Default is FALSE, which can save memory. This can be set FALSE for
# construting 'network' slot
# in RegenrichSet objects, but must be set TRUE when construting
# 'topNetP' slot.
#' @return an object of topNetwork class.
#' @include globals.R
#' @import tibble
#' @export
#' @examples
#' data(TFs)
#' edge = data.frame(from = rep(TFs$TF_name[seq(3)], seq(3)),
#' to = TFs$TF_name[11:16], weight = 0.1*(6:1))
#' object = newTopNetwork(edge, networkConstruction = 'new', percent = 100)
#' object
#' str(object)
newTopNetwork = function(networkEdgeTable,
reg = "",
directed = TRUE,
networkConstruction = c("new", "COEN", "GRN"),
percent = 100) {
if(missing(networkEdgeTable)){
networkEdgeTable = data.frame(from.gene = character(),
to.gene = character(),
weight = numeric())
}
networkEdgeTable = as.data.frame(networkEdgeTable)
stopifnot(is.data.frame(networkEdgeTable) && ncol(networkEdgeTable) == 3)
if (!is.numeric(networkEdgeTable[, 3])) {
stop("The third column ('weight') of networkEdgeTable must be numeric.")
}
networkConstruction = match.arg(networkConstruction)
colnames(networkEdgeTable) = c("from.gene", "to.gene", "weight")
# retain edges which start from regulators
networkEdgeTable = networkEdgeTable[networkEdgeTable$from.gene %in%
reg, , drop = FALSE]
reg = unique(as.character(networkEdgeTable$from.gene))
# remove duplicated edges
duID = duplicated(networkEdgeTable[, seq(2)])
networkEdgeTable = networkEdgeTable[!duID, , drop = FALSE]
# convert potential factor to character
networkEdgeTable$from.gene = as.character(networkEdgeTable$from.gene)
networkEdgeTable$to.gene = as.character(networkEdgeTable$to.gene)
as_elementset = utils::getFromNamespace("subclass_tbl_elementset_base",
"BiocSet")
.tbl_element = utils::getFromNamespace(".tbl_element", "BiocSet")
.tbl_set = utils::getFromNamespace(".tbl_set", "BiocSet")
elementset = tibble(set = networkEdgeTable$from.gene,
element = networkEdgeTable$to.gene,
weight = networkEdgeTable$weight) %>%
as_elementset("tbl_elementset")
new(Class = "TopNetwork",
#net = net,
elementset = elementset,
set = .tbl_set(elementset),
element = .tbl_element(elementset),
#tarReg = tarReg,
directed = directed,
networkConstruction = networkConstruction,
percent = percent)
}
#' Enrich class
#'
#' The `Enrich` object is to store enrichment analysis results by either
#' `FET` method or `GSEA` method.
#'
#' @slot topResult data frame. The enrichment results that pass thresholds
#' (default threshold is 0.05).
#' @slot allResult data frame. The enrichment results by FET or GSEA methods.
#' @slot gene character vector indicating the genes used for enrichment
#' analysis.
#' @slot namedScores numeric vector, a vector of ranked scores (decendent),
#' the names of the scores are the genes to perform enrichment analysis.
#' Here the scores are p-value of each gene.
#' @slot type character indicating enrichment method, either 'FET' or 'GSEA'.
#' @import tibble
#' @export
setClass(Class = "Enrich",
representation = representation(topResult = "tbl_df",
allResult = "tbl_df",
gene = "vector",
namedScores = "vector",
type = "character" # 'FET'/'SEA'
), prototype = prototype(topResult = tibble(),
allResult = tibble(),
gene = character(),
namedScores = character(),
type = "FET"
))
# Enrich object creator
newEnrich = function(topResult = tibble(),
allResult = tibble(),
gene = character(),
namedScores = character(),
type = "FET"){
new("Enrich", topResult = topResult, allResult = allResult,
gene = gene, namedScores = namedScores, type = type)
}
#' Score class
#'
#' `Score` class inherits tibble ("tbl"). The objects of `Score` class
#' are to store information of regulator ranking scores.
#' @slot names character vector, containing "reg", "negLogPDEA",
#' "negLogPEnrich", "logFC", and "score".
#' @slot .Data a list of length 5, each elements corresponds to the
#' \code{names} slots.
#' @slot row.names character, regulators corresponding to \code{.Data} slot.
#' @slot .S3Class character vector, containing "tbl_df", "tbl", "data.frame",
#' indicating the classes that `Score` class inherits.
#' @rdname Score
#' @export
setClass(Class = "Score", contains = class(tibble()))
#' Score object creator
#' @param reg character, regulator IDs.
#' @param negLogPDEA numeric, -log(p_DEA).
#' @param negLogPEnrich numeric, -log(p_Enrich).
#' @param logFC numeric, log2 fold change.
#' @param score numeric, RegEnrich ranking score.
#' @return newScore function returns a \code{Score} object.
#' @rdname Score
#' @export
#' @examples
#' newScore()
#' newScore(letters[1:5], 1:5, 1:5, -2:2, seq(2, 1, len = 5))
newScore = function(reg = character(),
negLogPDEA = numeric(),
negLogPEnrich = numeric(),
logFC = numeric(),
score = numeric()){
new("Score", tibble(reg, negLogPDEA, negLogPEnrich, logFC, score))
}
#' RegenrichSet class
#' @description The \code{RegenrichSet} is the fundamental class that RegEnrich
#' package is working with.
#' @slot assayRaw \code{matrix}, the initial raw expression data.
#' @slot colData \code{DataFrame} object, indicating sample information.
#' Each row represent a sample and each column represent a feature of samples.
#' @slot assays \code{SimpleList} object, containing the expression data after
#' filtering (and after Variance Stabilizing Transformation, i.e. VST, if the
#' differential analysis method is 'Wald_DESeq2' or 'LRT_DESeq2').
#' @slot elementMetadata DataFrame object, a slot for saving results by
#' differential expression analysis, containing at least three
#' columns:`gene`, `p` and `logFC`.
#' @slot topNetwork \code{TopNetwork} object, a slot for saving top network
#' edges. After regulator-target network inference,
#' a \code{\link{TopNetwork-class}}
#' object is assigned to this slot, containing only top ranked edges
#' in the full network. Default is NULL.
#' @slot resEnrich \code{Enrich} object, a slot for saving enrichment analysis
#' either by Fisher's exact test (FET) or gene set enrichment analysis (GSEA).
#' @slot resScore \code{Score} object, a slot for saving regulator ranking
#' results. It contains five components,
#' which are 'reg' (regulator), 'negLogPDEA' (-log10(p values of differential
#' expression analysis)), 'negLogPEnrich' (-log10(p values of enrichment
#' analysis)), 'logFC' (log2 fold changes), and 'score' (RegEnrich ranking
#' score).
#' @slot paramsIn list. The parameters used in the whole RegEnrich
#' analysis. This slot can be updated by respecifying arguments in each step of
#' RegEnrich analysis.
#' @slot paramsOut a list of four elements: DeaMethod (differential expression
#' method), networkType (regulator-target network construction method),
#' percent (what percentage of edges from the full network is used),
#' and enrichTest (enrichment method). By default, each element is NULL.
#' @slot network \code{TopNetwork} object, a slot for saving a full network.
#' @importClassesFrom S4Vectors RectangularData
#' @export
setClass(Class = "RegenrichSet", contains = "DeaSet",
representation = representation(
topNetwork = "TopNetwork",
resEnrich = "Enrich",
resScore = "Score",
paramsIn = "list",
paramsOut = "list",
network = "TopNetwork"),
prototype = prototype(
topNetwork = newTopNetwork(),
resEnrich = newEnrich(),
resScore = newScore(),
paramsIn = list(),
paramsOut = list(DeaMethod = NULL,
networkType = NULL,
percent = NULL,
enrichTest = NULL),
network = newTopNetwork()))
#' RegenrichSet object creator
#'
#' This is `RegenrichSet` object creator function.
#' There are four types of parameters in this function.\cr
#' First, parameters to provide raw data and sample information;\cr
#' `expr` and `colData`.\cr\cr
#' Second, parameters to perform differential expression analysis;\cr
#' `method`, `minMeanExpr`, `design`, `reduced`, `contrast`,
#' `coef`, `name`, `fitType`, `sfType`, `betaPrior`, `minReplicatesForReplace`,
#' `useT`, `minmu`, `parallel`, `BPPARAM` (also for network inference),
#' `altHypothesis`, `listValues`, `cooksCutoff`, `independentFiltering`,
#' `alpha`, `filter`, `theta`, `filterFun`, `addMLE`, `blind`, `ndups`,
#' `spacing`, `block`, `correlation`, `weights`, `proportion`,
#' `stdev.coef.lim`, `trend`, `robust`, and `winsor.tail.p`.\cr\cr
#' Thrid, parameters to perform regulator-target network inference;\cr
#' `reg`, `networkConstruction`, `topNetPercent`, `directed`, `rowSample`,
#' `softPower`, `networkType`, `TOMDenom`, `RsquaredCut`, `edgeThreshold`,
#' `K`, `nbTrees`, `importanceMeasure`, `trace`,
#' `BPPARAM` (also for differential expression analysis), and `minR`.\cr\cr
#' Fourth, parameters to perform enrichment analysis:\cr
#' `enrichTest`, `namedScoresCutoffs`, `minSize`, `maxSize`, `pvalueCutoff`,
#' `qvalueCutoff`, `regAltName`, `universe`, and `nperm`.\cr\cr
#'
#' @param expr matrix or data.frame, expression profile of a set of
#' genes or a set of proteins. If the \code{method = 'Wald_DESeq2' or
#' 'LRT_DESeq2'}
#' only non-negative integer matrix (read counts by RNA sequencing) is
#' accepted.
#' @param colData data frame, sample phenotype data.
#' The rows of colData must correspond to the columns of expr.
#' @param rowData NULL or data frame, information of each row/gene.
#' Default is NULL, which will generate a DataFrame of three columns, i.e.,
#' "gene", "p", and "logFC".
#' @param method either 'Wald_DESeq2', 'LRT_DESeq2', 'limma', or 'LRT_LM'
#' for the differential expression analysis.
#' \itemize{
#' \item When method = 'Wald_DESeq2', the Wald test in DESeq2 package is used;
#' \item When method = 'LRT_DESeq2', the likelihood ratio test (LRT) in DESeq2
#' package is used;
#' \item When method = 'limma', the `ls` method and empirical Bayes method in
#' limma package are used to calculate moderated t-statistics and differential
#' p-values;
#' \item When method = 'LRT_LM', a likelihood ratio test is performed for each
#' row of `expr` to compare two linear model specified by `design` and
#' `reduced` arguments. In this case, the fold changes are not calculated
#' but set to 0.
#' }
#' @param minMeanExpr numeric, the cutoff of gene average expression for
#' pre-filtering. The rows of `expr` with everage expression < minMeanExpr is
#' removed. The higher `minMeanExpr` is, the more genes are not included for
#' testing.
#' @param design either model formula or model matrix. For method =
#' 'LRT_DESeq2' or
#' 'LRT_LM', the design is the full model formula/matrix. For method =
#' 'limma',
#' and if design is a formula, the model matrix is constructed using
#' model.matrix(design, colData), so the name of each term in the design
#' formula must
#' be included in the column names of `colData`.
#' @param reduced The argument is used only when method = 'LRT_DESeq2' or
#' 'LRT_LM', it is a reduced formula/matrix to compare against.
#' If the design is a model matrix, `reduced` must also be a model matrix.
#' @param contrast The argument is used only when method = 'LRT_DESeq2',
#' 'Wald_DESeq2', or 'limma'. \cr
#' When method = 'LRT_DESeq2', or 'Wald_DESeq2', it specifies what comparison
#' to extract from the `DESeqDataSet` object to build a results table
#' (when method = 'LRT_DESeq2', this does not affect the value of `stat`,
#' `pvalue`, or `padj`). \cr
#' It can be one of following three formats:
#' \itemize{
#' \item a character vector with exactly three elements: the name of a
#' factor in the
#' design formula, the name of the numerator level for the fold change,
#' and the
#' name of the denominator level for the fold change;
#' \item a list of 1 or 2 character vector(s): the first element specifies
#' the names
#' of the fold changes for the numerator, and the second element (optional)
#' specifies the
#' names of the fold changes for the denominator. These names should be
#' elements
#' of \code{getResultsNames(design, colData)};
#' \item a numeric contrast vector with one element for each element in
#' \code{getResultsNames(design, colData)}.\cr
#' }
#'
#' When method = 'limma', It can be one of following two formats:
#' \itemize{
#' \item a numeric matrix with rows corresponding to coefficients in
#' design matrix and
#' columns containing contrasts;
#' \item a numeric vector if there is only one contrast. Each element of
#' the vector
#' corresponds to coefficients in design matrix. This is similar to the
#' third
#' format of contrast when method = 'LRT_DESeq2', or 'Wald_DESeq2'.
#' }
#'
#' @param coef The argument is used only when method = 'limma'. (Vector of)
#' column
#' number or column name specifying which coefficient or contrast of the
#' linear model
#' is of interest. Default is NULL.
#' @param name The argument is used only when method = 'LRT_DESeq2' or
#' 'Wald_DESeq2'.
#' the name of the individual effect (coefficient) for building a results
#' table.
#' Use this argument rather than contrast for continuous variables,
#' individual
#' effects or for individual interaction terms. The value provided to
#' name must
#' be an element of \code{getResultsNames(design, colData)}.
#'
#' @param fitType either 'parametric', 'local', or 'mean' for the type of
#' fitting
#' of dispersions to the mean intensity. This argument is used only when
#' method =
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 'parametric'.
#' @param sfType either 'ratio', 'poscounts', or 'iterate' for the type
#' of size
#' factor estimation. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 'ratio'.
#' @param betaPrior This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details.
#' @param minReplicatesForReplace This argument is used only when method
#' = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 7.
#' @param useT This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is FALSE,
#' @param minmu This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 0.5.
#' @param parallel whether computing (only for differential analysis
#' with method = "Wald_DESeq2" or "LRT_DESeq2") is parallel (default
#' is FALSE).
#' @param BPPARAM parameters for parallel computing (default is
#' \code{bpparam()}).
#' @param altHypothesis = c('greaterAbs', 'lessAbs', 'greater', 'less').
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is 'greaterAbs'.
#' @param listValues This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is c(1, -1),
#' @param cooksCutoff theshold on Cook's distance, such that if one or
#' more
#' samples for a row have a distance higher, the p-value for the row is
#' set to NA.
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param independentFiltering logical, whether independent filtering
#' should be
#' applied automatically. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is TRUE.
#' @param alpha the significance cutoff used for optimizing the independent
#' filtering.
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is 0.1,
#' @param filter the vector of filter statistics over which the independent
#' filtering is optimized. By default the mean of normalized counts is used.
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param theta the quantiles at which to assess the number of rejections
#' from
#' independent filtering. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param filterFun an optional custom function for performing independent
#' filtering
#' and p-value adjustment. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param addMLE if betaPrior=TRUE was used, whether the 'unshrunken' maximum
#' likelihood estimates (MLE) of log2 fold change should be added as a column
#' to the results table. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is FALSE.
#' @param blind logical, whether to blind the transformation to the
#' experimental
#' design. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{vst}} from DESeq2 package
#' for
#' more details. Default is FALSE, which is different from the default of
#' vst function.
#'
#' @param ndups positive integer giving the number of times each distinct
#' probe is
#' printed on each array. This argument is used only when method = 'limma'.
#' See \code{\link{lmFit}} from limma package for more details. Default is 1.
#' @param spacing positive integer giving the spacing between duplicate
#' occurrences of
#' the same probe, spacing=1 for consecutive rows. This argument is used only
#' when method = 'limma'. See \code{\link{lmFit}} from limma package for
#' more details. Default is 1.
#' @param block vector or factor specifying a blocking variable on the arrays.
#' Has length equal to the number of arrays. Must be NULL if ndups > 2.
#' This argument is used only when method = 'limma'. See \code{\link{lmFit}}
#' from limma package for more details. Default is NULL.
#' @param correlation the inter-duplicate or inter-technical replicate
#' correlation.
#' The correlation value should be estimated using the
#' \code{\link{duplicateCorrelation}}
#' function. This argument is used only when method = 'limma'.
#' See \code{\link{lmFit}}
#' from limma package for more details.
#' @param weights non-negative precision weights. Can be a numeric matrix of
#' individual weights of same size as the object expression matrix, or a
#' numeric
#' vector of array weights with length equal to ncol of the expression matrix,
#' or a numeric vector of gene weights with length equal to nrow of the
#' expression
#' matrix. This argument is used only when method = 'limma' or 'LRT_LM'.
#' See \code{\link{lmFit}} from limma package for more details. Default
#' is NULL.
#' @param proportion numeric value between 0 and 1, assumed proportion of
#' genes which
#' are differentially expressed. This argument is used only when method =
#' 'limma'.
#' See \code{\link{eBayes}} from limma package for more details. Default is
#' 0.01.
#' @param stdev.coef.lim numeric vector of length 2, assumed lower and
#' upper limits
#' for the standard deviation of log2-fold-changes for differentially
#' expressed
#' genes. This argument is used only when method = 'limma'.
#' See \code{\link{eBayes}}
#' from limma package for more details. Default is c(0.1, 4).
#' @param trend logical, should an intensity-trend be allowed for the prior
#' variance?
#' This argument is used only when method = 'limma'. See \code{\link{eBayes}}
#' from limma package for more details. Default is FALSE, meaning that the
#' prior
#' variance is constant.
#' @param robust logical, should the estimation of df.prior and var.prior be
#' robustified against outlier sample variances? This argument is used only
#' when method = 'limma'. See \code{\link{eBayes}}
#' from limma package for more details. Default is FALSE.
#' @param winsor.tail.p numeric vector of length 1 or 2, giving left and right
#' tail proportions of x to Winsorize. Used only when method = 'limma' and
#' robust=TRUE. See \code{\link{eBayes}}
#' from limma package for more details. Default is c(0.05,0.1)
#'
#' @param reg a vector of regulator names (ID). By default, these are
#' transcription
#' (co-)factors defined by three literatures/databases, namely RegNet,
#' TRRUST, and Marbach2016. The type (for example ENSEMBL gene ID, Entrez
#' gene ID,
#' or gene symble/name) of names or IDs of these regulators must be the
#' same as the type of names or IDs in the regulator-target network.
#' @param networkConstruction the method to construct this network.
#' Possible can be:\cr
#' 'COEN', coexpression network;\cr
#' 'GRN', gene regulatory network by random forest;\cr
#' 'new' (default), meaning a network provided by user, rather than
#' infered based
#' on the expression data.\cr
#' @param topNetPercent numeric, what percentage of the top edges in the
#' full
#' network is ratained. Default is 5, meaning top 5\% of edges. This value
#' must
#' be between 0 and 100.
#' @param directed logical, whether the network is directed. Default is
#' FALSE.
#' @param rowSample logic, if TRUE, each row represents a sample.
#' Otherwise, each column represents a sample. Default is FALSE.
#' @param softPower numeric, a soft power to achieve scale free topology.
#' If not provided, the parameter will be picked automatically by
#' \code{\link{plotSoftPower}} function.
#' @param networkType network type. Allowed values are (unique abbreviations
#' of)
#' 'unsigned' (default), 'signed', 'signed hybrid'.
#' See \code{\link{adjacency}}.
#' @param TOMDenom a character string specifying the TOM variant to be used.
#' Recognized values are 'min' giving the standard TOM described in Zhang
#' and Horvath (2005), and 'mean' in which the min function in the
#' denominator is replaced by mean. The 'mean' may produce better results
#' but at this time should be considered experimental.
#' @param RsquaredCut desired minimum scale free topology fitting index R^2.
#' Default is 0.85.
#' @param edgeThreshold numeric, the threshold to remove the low weighted
#' edges, Default is NULL, which means no edges will be removed.
#'
#' @param K integer or character. The number of features in each tree,
#' can be either a integer number, `sqrt`, or `all`.
#' `sqrt` denotes sqrt(the number of `reg`), `all`
#' means the number of `reg`. Default is `sqrt`.
#' @param nbTrees integer. The number of trees. Default is 1000.
#' @param importanceMeasure character. importanceMeasure can be `\%IncMSE`
#' or `IncNodePurity`, corresponding to type = 1 and 2 in
#' \code{\link{importance}}
#' function, respectively. Default is `IncNodePurity`(decrease in node
#' impurity),
#' which is faster than `\%IncMSE` (decrease in accuracy).
#' @param trace logical. To show the progress or not (default).
#' @param minR numeric. The minimum correlation coefficient of
#' prediction is to
#' control model accuracy. Default is 0.3.
#'
#' @param enrichTest character, specifying the enrichment analysis method,
#' which
#' is either `FET` (Fisher's exact test) or `GSEA` (gene set enrichment
#' analysis).
#' @param namedScoresCutoffs numeric, the significance cutoff for the
#' differential
#' analysis p value. Default is 0.05.
#' @param minSize The minimum number (default 5) of target genes.
#' @param maxSize The maximum number (default 5000) of target genes.
#' @param pvalueCutoff numeric, the significance cutoff for adjusted
#' enrichment p value.
#' This is used for obtaining the `topResult` slot in the final `Enrich`
#' object. Default is 0.05.
#' @param qvalueCutoff numeric, the significance cutoff of enrichment
#' q-value.
#' Default is 0.2.
#' @param regAltName alternative name for regulator. Default is NULL.
#' @param universe a vector of charactors. Background target genes.
#' @param nperm integer, number of permutations. The minimial possible
#' nominal p-value is about 1/nperm. Default is 10000.
#' @return an object of RegenrichSet class.
#' @import WGCNA
#' @import BiocParallel
# @importFrom S4Vectors DataFrame
#' @include globals.R
#' @export
#' @examples
#' # library(RegEnrich)
#' data("Lyme_GSE63085")
#' data("TFs")
#'
#' data = log2(Lyme_GSE63085$FPKM + 1)
#' colData = Lyme_GSE63085$sampleInfo
#'
#' # Take first 2000 rows for example
#' data1 = data[seq(2000), ]
#'
#' design = model.matrix(~0 + patientID + week, data = colData)
#'
#' # Initializing a 'RegenrichSet' object
#' object = RegenrichSet(expr = data1,
#' colData = colData,
#' method = 'limma', minMeanExpr = 0,
#' design = design,
#' contrast = c(rep(0, ncol(design) - 1), 1),
#' networkConstruction = 'COEN',
#' enrichTest = 'FET')
#' object
RegenrichSet = function(expr, colData, rowData = NULL,
method = c("Wald_DESeq2", "LRT_DESeq2",
"limma", "LRT_LM"),
minMeanExpr = NULL,
design, reduced, contrast,
coef = NULL, name,
fitType = c("parametric", "local", "mean"),
sfType = c("ratio", "poscounts", "iterate"),
betaPrior,
minReplicatesForReplace = 7,
useT = FALSE, minmu = 0.5,
parallel = FALSE,
BPPARAM = bpparam(),
altHypothesis = c("greaterAbs", "lessAbs",
"greater", "less"),
listValues = c(1, -1),
cooksCutoff, independentFiltering = TRUE,
alpha = 0.1, filter,
theta, filterFun, addMLE = FALSE,
blind = FALSE, ndups = 1,
spacing = 1, block = NULL, correlation,
weights = NULL, proportion = 0.01,
stdev.coef.lim = c(0.1, 4),
trend = FALSE, robust = FALSE,
winsor.tail.p = c(0.05, 0.1), reg = TFs$TF_name,
networkConstruction = c("COEN", "GRN", "new"),
topNetPercent = 5, directed = FALSE, rowSample = FALSE,
softPower = NULL, networkType = "unsigned",
TOMDenom = "min",
RsquaredCut = 0.85, edgeThreshold = NULL,
K = "sqrt", nbTrees = 1000,
importanceMeasure = "IncNodePurity", trace = FALSE,
minR = 0.3, enrichTest = c("FET", "GSEA"),
namedScoresCutoffs = 0.05,
minSize = 5, maxSize = 5000,
pvalueCutoff = 0.05, qvalueCutoff = 0.2,
regAltName = NULL, universe = NULL, nperm = 10000) {
if (missing(expr) || missing(colData)) {
stop("Must provide both 'expr' and 'colData'")
}
if (ncol(expr) != nrow(colData)) {
stop("The number of columns in 'expr' must be ",
"the same as the number of rows in 'colData'")
if (all(colnames(expr) != rownames(colData))) {
stop("The columns of 'expr' must match the rows of 'colData'")
}
}
se = SummarizedExperiment(assays = SimpleList(assayData = as.matrix(expr)),
rowData = rowData,
colData = as(colData, "DataFrame"))
seHi = if (!is.null(minMeanExpr) && is.numeric(minMeanExpr)) {
se[rowMeans(assay(se)) > minMeanExpr,]
} else se
rowN = nrow(seHi)
NAMES = rownames(seHi)
if(ncol(rowData(seHi)) != 0){
if(all(c("gene", "p", "logFC") %in% colnames(rowData))){
rowData = rowData(seHi)
} else {
stop("rowData must be either NULL or a data frame containing at",
"least the following columns: \n 'gene', 'p', 'logFC'")
}
} else {
if (is.null(NAMES)){
stop("Either rownames(expr) or rowData must not be NULL.")
}
rowData = DataFrame(gene = NAMES,
p = numeric(rowN),
logFC = numeric(rowN),
row.names = NAMES)
}
par = list(method = method,
minMeanExpr = minMeanExpr,
design = if (missing(design)) substitute() else design,
reduced = if (missing(reduced)) substitute() else reduced,
contrast = if (missing(contrast)) substitute() else contrast,
coef = if (missing(coef)) substitute() else coef,
name = if (missing(name)) substitute() else name,
fitType = fitType, sfType = sfType,
betaPrior = if (missing(betaPrior)) substitute() else betaPrior,
minReplicatesForReplace = minReplicatesForReplace,
useT = useT, minmu = minmu, parallel = parallel,
BPPARAM = BPPARAM, altHypothesis = altHypothesis,
listValues = listValues,
cooksCutoff = if (missing(cooksCutoff)) {
substitute()} else cooksCutoff,
independentFiltering = independentFiltering, alpha = alpha,
filter = if (missing(filter)) substitute() else filter,
theta = if (missing(theta)) substitute() else theta,
filterFun = if (missing(filterFun)) substitute() else filterFun,
addMLE = addMLE, blind = blind, ndups = ndups, spacing = spacing,
block = block,
correlation = if (missing(correlation)) {
substitute()} else correlation,
weights = weights, proportion = proportion,
stdev.coef.lim = stdev.coef.lim,
trend = trend, robust = robust, winsor.tail.p = winsor.tail.p,
reg = reg, rowSample = rowSample, softPower = softPower,
networkConstruction = networkConstruction,
networkType = networkType,
TOMDenom = TOMDenom, RsquaredCut = RsquaredCut,
edgeThreshold = edgeThreshold,
K = K, nbTrees = nbTrees, importanceMeasure = importanceMeasure,
trace = trace,
minR = minR, topNetPercent = topNetPercent, directed = directed,
enrichTest = enrichTest, namedScoresCutoffs = namedScoresCutoffs,
minSize = minSize, maxSize = maxSize, pvalueCutoff = pvalueCutoff,
qvalueCutoff = qvalueCutoff, regAltName = regAltName,
universe = universe, nperm = nperm)
object = new(Class = "RegenrichSet",
assayRaw = assay(se),
assays = Assays(assays(seHi)),
colData = colData(seHi),
NAMES = NAMES,
elementMetadata = rowData,
paramsIn = par,
topNetwork = newTopNetwork(),
resEnrich = newEnrich(),
resScore = newScore(),
paramsOut = list(DeaMethod = NULL, networkType = NULL,
enrichTest = NULL, percent = NULL),
network = newTopNetwork())
return(object)
}
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Defines functions RegenrichSet newScore newEnrich newTopNetwork newDeaSet
Documented in newDeaSet newScore newTopNetwork RegenrichSet
#' DeaSet class
#'
#'
#' @slot colData DataFrame object, sample information, the row name is
#' corresponding to the column names of expression matrix in the \code{assays}
#' slot.
#' @slot assays SimpleList object of one/multiple matrix/matrices,
#' this is the slot for storing the expression data after filtering
#' (and after Variance Stabilizing Transformation, i.e. VST, if the
#' differential analysis method is 'Wald_DESeq2' or 'LRT_DESeq2'). And the
#' expression matrix is used for network inference and plotting.
#' @slot NAMES row names of expression data in \code{assays} slot and
#' \code{elementMetadata} slot.
#' @slot elementMetadata feature information, contains at least a DataFrame
#' of three columns, i.e. `gene`, `p` and `logFC`, which stores gene names/IDs,
#' differential p values and log2 expression fold changes, respectively.
#' @slot metadata DataFrame object, information of feature columns.
#' @slot assayRaw a slot for saving the raw expression data.
#' @import SummarizedExperiment
#' @export
#' @examples
# \donttest{
#' nrows = 100
#' ncols = 6
#' counts = matrix(rnbinom(nrows * ncols, size = 2, mu = 500),
#' nrow = nrows)
#' assays = SimpleList(assayData = counts)
#'
#' colData = DataFrame(Condition = rep(c("treatment", "ctrl"), 3),
#' row.names=LETTERS[1:6])
#' geneNames = sprintf("G%03s", seq(nrows))
#' elementMetadata = DataFrame(gene = geneNames,
#' p = numeric(nrows),
#' logFC = numeric(nrows))
#'
#' ds = new("DeaSet",
#' assays = Assays(assays),
#' colData = colData,
#' assayRaw = counts,
#' elementMetadata = elementMetadata,
#' NAMES = geneNames)
#' ds
# }
#'
#'
setClass(Class = "DeaSet",
contains = "SummarizedExperiment",
representation = representation(
assayRaw = "matrix"),
prototype = prototype(assayRaw = matrix(nrow = 0, ncol = 0),
elementMetadata = DataFrame(gene = character(0),
p = numeric(0),
logFC = numeric(0))),
validity = function(object){
error = character()
if(!all(c("gene", "p", "logFC") %in% colnames(mcols(object)))){
error = c(error, paste0("mcols(object) must contain 'gene', ",
"'p', 'logFC' columns.\n"))
}
return(if(length(error) == 0) TRUE else error)
})
#' DeaSet object creator
#'
#' @param assayRaw A matrix of gene expression data. This can be the same
#' as the matrix-like element in \code{assays} parameter.
#' @param rowData A DataFrame object describing the rows.
#' @param assays A list or SimpleList of matrix-like element,
#' or a matrix-like object. The matrix-like element can be the same
#' as \code{assayRaw} parameter.
#' @param colData A DataFrame describing the sample information.
#' @param metadata An optional list of arbitrary content describing the
#' overall experiment.
#' @return A DeaSet object.
#'
#' @export
#' @examples
#' # Empty DeaSet object
#' newDeaSet()
#'
#' # 100 * 6 DeaSet object
#' nrows = 100
#' ncols = 6
#' counts = matrix(rnbinom(nrows * ncols, size = 2, mu = 500),
#' nrow = nrows)
#' assays = SimpleList(counts=counts)
#'
#' colData = DataFrame(Condition = rep(c("treatment", "ctrl"), 3),
#' row.names=LETTERS[1:6])
#' geneNames = sprintf("G%03s", seq(nrows))
#' elementMetadata = DataFrame(gene = geneNames,
#' p = numeric(nrows),
#' logFC = numeric(nrows))
#' newDeaSet(assayRaw = counts,
#' rowData = elementMetadata,
#' assays = SimpleList(assayData = counts),
#' colData = colData)
newDeaSet = function(assayRaw = matrix(nrow = 0, ncol = 0),
rowData = NULL,
assays = SimpleList(),
colData = DataFrame(),
metadata = list()){
if(is.null(rowData)){
rowData = DataFrame(gene = character(0),
p = numeric(0),
logFC = numeric(0))
}
se = SummarizedExperiment(assays = assays,
rowData = rowData,
colData = colData,
metadata = metadata)
new("DeaSet",
assayRaw = assayRaw,
assays = Assays(assays),
elementMetadata = rowData,
colData = colData,
NAMES = se@NAMES,
metadata = metadata)
}
#' TopNetwork class
#'
#' The `TopNetwork` object is to store either a full network (the percentage
#' of top edges is 100%) or a sub-network (with percentage of top edges
#' between 0 to 10).
#' @slot element tibble, the pool of targets in the network.
#' @slot set tibble, the pool of valid regulators.
#' @slot elementset tibble, regulator-target edges with edge weights.
# @slot net list, in which the names of elements are regulators,
# and the elements are targets of the regulators indicated by the
# element name.
# @slot netTable data frame. A table showing regulator-target
# relationship. It contains 3 columns, representing 'from.gene'
# ('regulators'), 'to.gene' ('targets') and 'weight', respectively.
# @slot validReg vector, a set of valid regulators in the network.
# @slot tarReg list, in which the names of elements are targets,
#' and the elements are regulators of the targets indicated by the
#' element name.
#' @slot directed logical, whether the network is directed.
#' @slot networkConstruction character, by which method this network
#' is constructed. Either 'COEN' (coexpression network using WGCNA),
#' or 'GRN' (gene regulatory network using random forest), or 'new'
#' (a network provided by the user).
#' @slot percent numeric, what percentage of the top edges are remained.
#' The value must be between 0 (excluding) and 100 (including).
#' @slot active character, which data table is activated, the default is
#' "elementset".
#' @importClassesFrom BiocSet BiocSet
#' @export
setClass(Class = "TopNetwork",
contains = "BiocSet",
representation = representation(
directed = "logical",
networkConstruction = "character",
percent = "numeric"),
prototype = prototype(# net = list(),
directed = TRUE,
networkConstruction = "new",
percent = 100,
active = "elementset"),
validity = function(object) {
if (object@percent > 100 || object@percent <= 0) {
"@percent must be between 0 (excluding) and 100 (including)"
} else {
if (length(object@networkConstruction) != 1 ||
!object@networkConstruction %in%
c("COEN", "GRN", "new")) {
"@networkConstruction must be one of 'COEN', 'GRN', and 'new'"
} else {
TRUE
}
}
})
#' TopNetwork object creator
#'
#' This function create `TopNetwork` object using 3-column edge table.
#'
#' @param networkEdgeTable a data frame of 3 columns, representing 'from.gene'
#' ('regulators'), 'to.gene' ('targets') and 'weight', respectively.
#' @param reg a vector of gene regulators.
#' @param directed logical, whether the network is directed. Default is TRUE.
#' @param networkConstruction the method to construct this network.
#' Possible can be:
#' 'COEN', coexpression network;
#' 'GRN', gene regulatory network by random forest;
#' 'new' (default), meaning a network provided by user, rather than
#' infered based
#' on the expression data.
#' @param percent the percentage of edges in the original whole network.
#' Default is 100, meaning 100\% edges in whole network.
# @param extendObject logical, whether net and tarReg will be constructed.
# Default is FALSE, which can save memory. This can be set FALSE for
# construting 'network' slot
# in RegenrichSet objects, but must be set TRUE when construting
# 'topNetP' slot.
#' @return an object of topNetwork class.
#' @include globals.R
#' @import tibble
#' @export
#' @examples
#' data(TFs)
#' edge = data.frame(from = rep(TFs$TF_name[seq(3)], seq(3)),
#' to = TFs$TF_name[11:16], weight = 0.1*(6:1))
#' object = newTopNetwork(edge, networkConstruction = 'new', percent = 100)
#' object
#' str(object)
newTopNetwork = function(networkEdgeTable,
reg = "",
directed = TRUE,
networkConstruction = c("new", "COEN", "GRN"),
percent = 100) {
if(missing(networkEdgeTable)){
networkEdgeTable = data.frame(from.gene = character(),
to.gene = character(),
weight = numeric())
}
networkEdgeTable = as.data.frame(networkEdgeTable)
stopifnot(is.data.frame(networkEdgeTable) && ncol(networkEdgeTable) == 3)
if (!is.numeric(networkEdgeTable[, 3])) {
stop("The third column ('weight') of networkEdgeTable must be numeric.")
}
networkConstruction = match.arg(networkConstruction)
colnames(networkEdgeTable) = c("from.gene", "to.gene", "weight")
# retain edges which start from regulators
networkEdgeTable = networkEdgeTable[networkEdgeTable$from.gene %in%
reg, , drop = FALSE]
reg = unique(as.character(networkEdgeTable$from.gene))
# remove duplicated edges
duID = duplicated(networkEdgeTable[, seq(2)])
networkEdgeTable = networkEdgeTable[!duID, , drop = FALSE]
# convert potential factor to character
networkEdgeTable$from.gene = as.character(networkEdgeTable$from.gene)
networkEdgeTable$to.gene = as.character(networkEdgeTable$to.gene)
as_elementset = utils::getFromNamespace("subclass_tbl_elementset_base",
"BiocSet")
.tbl_element = utils::getFromNamespace(".tbl_element", "BiocSet")
.tbl_set = utils::getFromNamespace(".tbl_set", "BiocSet")
elementset = tibble(set = networkEdgeTable$from.gene,
element = networkEdgeTable$to.gene,
weight = networkEdgeTable$weight) %>%
as_elementset("tbl_elementset")
new(Class = "TopNetwork",
#net = net,
elementset = elementset,
set = .tbl_set(elementset),
element = .tbl_element(elementset),
#tarReg = tarReg,
directed = directed,
networkConstruction = networkConstruction,
percent = percent)
}
#' Enrich class
#'
#' The `Enrich` object is to store enrichment analysis results by either
#' `FET` method or `GSEA` method.
#'
#' @slot topResult data frame. The enrichment results that pass thresholds
#' (default threshold is 0.05).
#' @slot allResult data frame. The enrichment results by FET or GSEA methods.
#' @slot gene character vector indicating the genes used for enrichment
#' analysis.
#' @slot namedScores numeric vector, a vector of ranked scores (decendent),
#' the names of the scores are the genes to perform enrichment analysis.
#' Here the scores are p-value of each gene.
#' @slot type character indicating enrichment method, either 'FET' or 'GSEA'.
#' @import tibble
#' @export
setClass(Class = "Enrich",
representation = representation(topResult = "tbl_df",
allResult = "tbl_df",
gene = "vector",
namedScores = "vector",
type = "character" # 'FET'/'SEA'
), prototype = prototype(topResult = tibble(),
allResult = tibble(),
gene = character(),
namedScores = character(),
type = "FET"
))
# Enrich object creator
newEnrich = function(topResult = tibble(),
allResult = tibble(),
gene = character(),
namedScores = character(),
type = "FET"){
new("Enrich", topResult = topResult, allResult = allResult,
gene = gene, namedScores = namedScores, type = type)
}
#' Score class
#'
#' `Score` class inherits tibble ("tbl"). The objects of `Score` class
#' are to store information of regulator ranking scores.
#' @slot names character vector, containing "reg", "negLogPDEA",
#' "negLogPEnrich", "logFC", and "score".
#' @slot .Data a list of length 5, each elements corresponds to the
#' \code{names} slots.
#' @slot row.names character, regulators corresponding to \code{.Data} slot.
#' @slot .S3Class character vector, containing "tbl_df", "tbl", "data.frame",
#' indicating the classes that `Score` class inherits.
#' @rdname Score
#' @export
setClass(Class = "Score", contains = class(tibble()))
#' Score object creator
#' @param reg character, regulator IDs.
#' @param negLogPDEA numeric, -log(p_DEA).
#' @param negLogPEnrich numeric, -log(p_Enrich).
#' @param logFC numeric, log2 fold change.
#' @param score numeric, RegEnrich ranking score.
#' @return newScore function returns a \code{Score} object.
#' @rdname Score
#' @export
#' @examples
#' newScore()
#' newScore(letters[1:5], 1:5, 1:5, -2:2, seq(2, 1, len = 5))
newScore = function(reg = character(),
negLogPDEA = numeric(),
negLogPEnrich = numeric(),
logFC = numeric(),
score = numeric()){
new("Score", tibble(reg, negLogPDEA, negLogPEnrich, logFC, score))
}
#' RegenrichSet class
#' @description The \code{RegenrichSet} is the fundamental class that RegEnrich
#' package is working with.
#' @slot assayRaw \code{matrix}, the initial raw expression data.
#' @slot colData \code{DataFrame} object, indicating sample information.
#' Each row represent a sample and each column represent a feature of samples.
#' @slot assays \code{SimpleList} object, containing the expression data after
#' filtering (and after Variance Stabilizing Transformation, i.e. VST, if the
#' differential analysis method is 'Wald_DESeq2' or 'LRT_DESeq2').
#' @slot elementMetadata DataFrame object, a slot for saving results by
#' differential expression analysis, containing at least three
#' columns:`gene`, `p` and `logFC`.
#' @slot topNetwork \code{TopNetwork} object, a slot for saving top network
#' edges. After regulator-target network inference,
#' a \code{\link{TopNetwork-class}}
#' object is assigned to this slot, containing only top ranked edges
#' in the full network. Default is NULL.
#' @slot resEnrich \code{Enrich} object, a slot for saving enrichment analysis
#' either by Fisher's exact test (FET) or gene set enrichment analysis (GSEA).
#' @slot resScore \code{Score} object, a slot for saving regulator ranking
#' results. It contains five components,
#' which are 'reg' (regulator), 'negLogPDEA' (-log10(p values of differential
#' expression analysis)), 'negLogPEnrich' (-log10(p values of enrichment
#' analysis)), 'logFC' (log2 fold changes), and 'score' (RegEnrich ranking
#' score).
#' @slot paramsIn list. The parameters used in the whole RegEnrich
#' analysis. This slot can be updated by respecifying arguments in each step of
#' RegEnrich analysis.
#' @slot paramsOut a list of four elements: DeaMethod (differential expression
#' method), networkType (regulator-target network construction method),
#' percent (what percentage of edges from the full network is used),
#' and enrichTest (enrichment method). By default, each element is NULL.
#' @slot network \code{TopNetwork} object, a slot for saving a full network.
#' @importClassesFrom S4Vectors RectangularData
#' @export
setClass(Class = "RegenrichSet", contains = "DeaSet",
representation = representation(
topNetwork = "TopNetwork",
resEnrich = "Enrich",
resScore = "Score",
paramsIn = "list",
paramsOut = "list",
network = "TopNetwork"),
prototype = prototype(
topNetwork = newTopNetwork(),
resEnrich = newEnrich(),
resScore = newScore(),
paramsIn = list(),
paramsOut = list(DeaMethod = NULL,
networkType = NULL,
percent = NULL,
enrichTest = NULL),
network = newTopNetwork()))
#' RegenrichSet object creator
#'
#' This is `RegenrichSet` object creator function.
#' There are four types of parameters in this function.\cr
#' First, parameters to provide raw data and sample information;\cr
#' `expr` and `colData`.\cr\cr
#' Second, parameters to perform differential expression analysis;\cr
#' `method`, `minMeanExpr`, `design`, `reduced`, `contrast`,
#' `coef`, `name`, `fitType`, `sfType`, `betaPrior`, `minReplicatesForReplace`,
#' `useT`, `minmu`, `parallel`, `BPPARAM` (also for network inference),
#' `altHypothesis`, `listValues`, `cooksCutoff`, `independentFiltering`,
#' `alpha`, `filter`, `theta`, `filterFun`, `addMLE`, `blind`, `ndups`,
#' `spacing`, `block`, `correlation`, `weights`, `proportion`,
#' `stdev.coef.lim`, `trend`, `robust`, and `winsor.tail.p`.\cr\cr
#' Thrid, parameters to perform regulator-target network inference;\cr
#' `reg`, `networkConstruction`, `topNetPercent`, `directed`, `rowSample`,
#' `softPower`, `networkType`, `TOMDenom`, `RsquaredCut`, `edgeThreshold`,
#' `K`, `nbTrees`, `importanceMeasure`, `trace`,
#' `BPPARAM` (also for differential expression analysis), and `minR`.\cr\cr
#' Fourth, parameters to perform enrichment analysis:\cr
#' `enrichTest`, `namedScoresCutoffs`, `minSize`, `maxSize`, `pvalueCutoff`,
#' `qvalueCutoff`, `regAltName`, `universe`, and `nperm`.\cr\cr
#'
#' @param expr matrix or data.frame, expression profile of a set of
#' genes or a set of proteins. If the \code{method = 'Wald_DESeq2' or
#' 'LRT_DESeq2'}
#' only non-negative integer matrix (read counts by RNA sequencing) is
#' accepted.
#' @param colData data frame, sample phenotype data.
#' The rows of colData must correspond to the columns of expr.
#' @param rowData NULL or data frame, information of each row/gene.
#' Default is NULL, which will generate a DataFrame of three columns, i.e.,
#' "gene", "p", and "logFC".
#' @param method either 'Wald_DESeq2', 'LRT_DESeq2', 'limma', or 'LRT_LM'
#' for the differential expression analysis.
#' \itemize{
#' \item When method = 'Wald_DESeq2', the Wald test in DESeq2 package is used;
#' \item When method = 'LRT_DESeq2', the likelihood ratio test (LRT) in DESeq2
#' package is used;
#' \item When method = 'limma', the `ls` method and empirical Bayes method in
#' limma package are used to calculate moderated t-statistics and differential
#' p-values;
#' \item When method = 'LRT_LM', a likelihood ratio test is performed for each
#' row of `expr` to compare two linear model specified by `design` and
#' `reduced` arguments. In this case, the fold changes are not calculated
#' but set to 0.
#' }
#' @param minMeanExpr numeric, the cutoff of gene average expression for
#' pre-filtering. The rows of `expr` with everage expression < minMeanExpr is
#' removed. The higher `minMeanExpr` is, the more genes are not included for
#' testing.
#' @param design either model formula or model matrix. For method =
#' 'LRT_DESeq2' or
#' 'LRT_LM', the design is the full model formula/matrix. For method =
#' 'limma',
#' and if design is a formula, the model matrix is constructed using
#' model.matrix(design, colData), so the name of each term in the design
#' formula must
#' be included in the column names of `colData`.
#' @param reduced The argument is used only when method = 'LRT_DESeq2' or
#' 'LRT_LM', it is a reduced formula/matrix to compare against.
#' If the design is a model matrix, `reduced` must also be a model matrix.
#' @param contrast The argument is used only when method = 'LRT_DESeq2',
#' 'Wald_DESeq2', or 'limma'. \cr
#' When method = 'LRT_DESeq2', or 'Wald_DESeq2', it specifies what comparison
#' to extract from the `DESeqDataSet` object to build a results table
#' (when method = 'LRT_DESeq2', this does not affect the value of `stat`,
#' `pvalue`, or `padj`). \cr
#' It can be one of following three formats:
#' \itemize{
#' \item a character vector with exactly three elements: the name of a
#' factor in the
#' design formula, the name of the numerator level for the fold change,
#' and the
#' name of the denominator level for the fold change;
#' \item a list of 1 or 2 character vector(s): the first element specifies
#' the names
#' of the fold changes for the numerator, and the second element (optional)
#' specifies the
#' names of the fold changes for the denominator. These names should be
#' elements
#' of \code{getResultsNames(design, colData)};
#' \item a numeric contrast vector with one element for each element in
#' \code{getResultsNames(design, colData)}.\cr
#' }
#'
#' When method = 'limma', It can be one of following two formats:
#' \itemize{
#' \item a numeric matrix with rows corresponding to coefficients in
#' design matrix and
#' columns containing contrasts;
#' \item a numeric vector if there is only one contrast. Each element of
#' the vector
#' corresponds to coefficients in design matrix. This is similar to the
#' third
#' format of contrast when method = 'LRT_DESeq2', or 'Wald_DESeq2'.
#' }
#'
#' @param coef The argument is used only when method = 'limma'. (Vector of)
#' column
#' number or column name specifying which coefficient or contrast of the
#' linear model
#' is of interest. Default is NULL.
#' @param name The argument is used only when method = 'LRT_DESeq2' or
#' 'Wald_DESeq2'.
#' the name of the individual effect (coefficient) for building a results
#' table.
#' Use this argument rather than contrast for continuous variables,
#' individual
#' effects or for individual interaction terms. The value provided to
#' name must
#' be an element of \code{getResultsNames(design, colData)}.
#'
#' @param fitType either 'parametric', 'local', or 'mean' for the type of
#' fitting
#' of dispersions to the mean intensity. This argument is used only when
#' method =
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 'parametric'.
#' @param sfType either 'ratio', 'poscounts', or 'iterate' for the type
#' of size
#' factor estimation. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 'ratio'.
#' @param betaPrior This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details.
#' @param minReplicatesForReplace This argument is used only when method
#' = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 7.
#' @param useT This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is FALSE,
#' @param minmu This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{DESeq}} from DESeq2
#' package
#' for more details. Default is 0.5.
#' @param parallel whether computing (only for differential analysis
#' with method = "Wald_DESeq2" or "LRT_DESeq2") is parallel (default
#' is FALSE).
#' @param BPPARAM parameters for parallel computing (default is
#' \code{bpparam()}).
#' @param altHypothesis = c('greaterAbs', 'lessAbs', 'greater', 'less').
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is 'greaterAbs'.
#' @param listValues This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is c(1, -1),
#' @param cooksCutoff theshold on Cook's distance, such that if one or
#' more
#' samples for a row have a distance higher, the p-value for the row is
#' set to NA.
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param independentFiltering logical, whether independent filtering
#' should be
#' applied automatically. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is TRUE.
#' @param alpha the significance cutoff used for optimizing the independent
#' filtering.
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is 0.1,
#' @param filter the vector of filter statistics over which the independent
#' filtering is optimized. By default the mean of normalized counts is used.
#' This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param theta the quantiles at which to assess the number of rejections
#' from
#' independent filtering. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param filterFun an optional custom function for performing independent
#' filtering
#' and p-value adjustment. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details.
#' @param addMLE if betaPrior=TRUE was used, whether the 'unshrunken' maximum
#' likelihood estimates (MLE) of log2 fold change should be added as a column
#' to the results table. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{results}} from DESeq2
#' package
#' for more details. Default is FALSE.
#' @param blind logical, whether to blind the transformation to the
#' experimental
#' design. This argument is used only when method = either
#' 'Wald_DESeq2' or 'LRT_DESeq2'. See \code{\link{vst}} from DESeq2 package
#' for
#' more details. Default is FALSE, which is different from the default of
#' vst function.
#'
#' @param ndups positive integer giving the number of times each distinct
#' probe is
#' printed on each array. This argument is used only when method = 'limma'.
#' See \code{\link{lmFit}} from limma package for more details. Default is 1.
#' @param spacing positive integer giving the spacing between duplicate
#' occurrences of
#' the same probe, spacing=1 for consecutive rows. This argument is used only
#' when method = 'limma'. See \code{\link{lmFit}} from limma package for
#' more details. Default is 1.
#' @param block vector or factor specifying a blocking variable on the arrays.
#' Has length equal to the number of arrays. Must be NULL if ndups > 2.
#' This argument is used only when method = 'limma'. See \code{\link{lmFit}}
#' from limma package for more details. Default is NULL.
#' @param correlation the inter-duplicate or inter-technical replicate
#' correlation.
#' The correlation value should be estimated using the
#' \code{\link{duplicateCorrelation}}
#' function. This argument is used only when method = 'limma'.
#' See \code{\link{lmFit}}
#' from limma package for more details.
#' @param weights non-negative precision weights. Can be a numeric matrix of
#' individual weights of same size as the object expression matrix, or a
#' numeric
#' vector of array weights with length equal to ncol of the expression matrix,
#' or a numeric vector of gene weights with length equal to nrow of the
#' expression
#' matrix. This argument is used only when method = 'limma' or 'LRT_LM'.
#' See \code{\link{lmFit}} from limma package for more details. Default
#' is NULL.
#' @param proportion numeric value between 0 and 1, assumed proportion of
#' genes which
#' are differentially expressed. This argument is used only when method =
#' 'limma'.
#' See \code{\link{eBayes}} from limma package for more details. Default is
#' 0.01.
#' @param stdev.coef.lim numeric vector of length 2, assumed lower and
#' upper limits
#' for the standard deviation of log2-fold-changes for differentially
#' expressed
#' genes. This argument is used only when method = 'limma'.
#' See \code{\link{eBayes}}
#' from limma package for more details. Default is c(0.1, 4).
#' @param trend logical, should an intensity-trend be allowed for the prior
#' variance?
#' This argument is used only when method = 'limma'. See \code{\link{eBayes}}
#' from limma package for more details. Default is FALSE, meaning that the
#' prior
#' variance is constant.
#' @param robust logical, should the estimation of df.prior and var.prior be
#' robustified against outlier sample variances? This argument is used only
#' when method = 'limma'. See \code{\link{eBayes}}
#' from limma package for more details. Default is FALSE.
#' @param winsor.tail.p numeric vector of length 1 or 2, giving left and right
#' tail proportions of x to Winsorize. Used only when method = 'limma' and
#' robust=TRUE. See \code{\link{eBayes}}
#' from limma package for more details. Default is c(0.05,0.1)
#'
#' @param reg a vector of regulator names (ID). By default, these are
#' transcription
#' (co-)factors defined by three literatures/databases, namely RegNet,
#' TRRUST, and Marbach2016. The type (for example ENSEMBL gene ID, Entrez
#' gene ID,
#' or gene symble/name) of names or IDs of these regulators must be the
#' same as the type of names or IDs in the regulator-target network.
#' @param networkConstruction the method to construct this network.
#' Possible can be:\cr
#' 'COEN', coexpression network;\cr
#' 'GRN', gene regulatory network by random forest;\cr
#' 'new' (default), meaning a network provided by user, rather than
#' infered based
#' on the expression data.\cr
#' @param topNetPercent numeric, what percentage of the top edges in the
#' full
#' network is ratained. Default is 5, meaning top 5\% of edges. This value
#' must
#' be between 0 and 100.
#' @param directed logical, whether the network is directed. Default is
#' FALSE.
#' @param rowSample logic, if TRUE, each row represents a sample.
#' Otherwise, each column represents a sample. Default is FALSE.
#' @param softPower numeric, a soft power to achieve scale free topology.
#' If not provided, the parameter will be picked automatically by
#' \code{\link{plotSoftPower}} function.
#' @param networkType network type. Allowed values are (unique abbreviations
#' of)
#' 'unsigned' (default), 'signed', 'signed hybrid'.
#' See \code{\link{adjacency}}.
#' @param TOMDenom a character string specifying the TOM variant to be used.
#' Recognized values are 'min' giving the standard TOM described in Zhang
#' and Horvath (2005), and 'mean' in which the min function in the
#' denominator is replaced by mean. The 'mean' may produce better results
#' but at this time should be considered experimental.
#' @param RsquaredCut desired minimum scale free topology fitting index R^2.
#' Default is 0.85.
#' @param edgeThreshold numeric, the threshold to remove the low weighted
#' edges, Default is NULL, which means no edges will be removed.
#'
#' @param K integer or character. The number of features in each tree,
#' can be either a integer number, `sqrt`, or `all`.
#' `sqrt` denotes sqrt(the number of `reg`), `all`
#' means the number of `reg`. Default is `sqrt`.
#' @param nbTrees integer. The number of trees. Default is 1000.
#' @param importanceMeasure character. importanceMeasure can be `\%IncMSE`
#' or `IncNodePurity`, corresponding to type = 1 and 2 in
#' \code{\link{importance}}
#' function, respectively. Default is `IncNodePurity`(decrease in node
#' impurity),
#' which is faster than `\%IncMSE` (decrease in accuracy).
#' @param trace logical. To show the progress or not (default).
#' @param minR numeric. The minimum correlation coefficient of
#' prediction is to
#' control model accuracy. Default is 0.3.
#'
#' @param enrichTest character, specifying the enrichment analysis method,
#' which
#' is either `FET` (Fisher's exact test) or `GSEA` (gene set enrichment
#' analysis).
#' @param namedScoresCutoffs numeric, the significance cutoff for the
#' differential
#' analysis p value. Default is 0.05.
#' @param minSize The minimum number (default 5) of target genes.
#' @param maxSize The maximum number (default 5000) of target genes.
#' @param pvalueCutoff numeric, the significance cutoff for adjusted
#' enrichment p value.
#' This is used for obtaining the `topResult` slot in the final `Enrich`
#' object. Default is 0.05.
#' @param qvalueCutoff numeric, the significance cutoff of enrichment
#' q-value.
#' Default is 0.2.
#' @param regAltName alternative name for regulator. Default is NULL.
#' @param universe a vector of charactors. Background target genes.
#' @param nperm integer, number of permutations. The minimial possible
#' nominal p-value is about 1/nperm. Default is 10000.
#' @return an object of RegenrichSet class.
#' @import WGCNA
#' @import BiocParallel
# @importFrom S4Vectors DataFrame
#' @include globals.R
#' @export
#' @examples
#' # library(RegEnrich)
#' data("Lyme_GSE63085")
#' data("TFs")
#'
#' data = log2(Lyme_GSE63085$FPKM + 1)
#' colData = Lyme_GSE63085$sampleInfo
#'
#' # Take first 2000 rows for example
#' data1 = data[seq(2000), ]
#'
#' design = model.matrix(~0 + patientID + week, data = colData)
#'
#' # Initializing a 'RegenrichSet' object
#' object = RegenrichSet(expr = data1,
#' colData = colData,
#' method = 'limma', minMeanExpr = 0,
#' design = design,
#' contrast = c(rep(0, ncol(design) - 1), 1),
#' networkConstruction = 'COEN',
#' enrichTest = 'FET')
#' object
RegenrichSet = function(expr, colData, rowData = NULL,
method = c("Wald_DESeq2", "LRT_DESeq2",
"limma", "LRT_LM"),
minMeanExpr = NULL,
design, reduced, contrast,
coef = NULL, name,
fitType = c("parametric", "local", "mean"),
sfType = c("ratio", "poscounts", "iterate"),
betaPrior,
minReplicatesForReplace = 7,
useT = FALSE, minmu = 0.5,
parallel = FALSE,
BPPARAM = bpparam(),
altHypothesis = c("greaterAbs", "lessAbs",
"greater", "less"),
listValues = c(1, -1),
cooksCutoff, independentFiltering = TRUE,
alpha = 0.1, filter,
theta, filterFun, addMLE = FALSE,
blind = FALSE, ndups = 1,
spacing = 1, block = NULL, correlation,
weights = NULL, proportion = 0.01,
stdev.coef.lim = c(0.1, 4),
trend = FALSE, robust = FALSE,
winsor.tail.p = c(0.05, 0.1), reg = TFs$TF_name,
networkConstruction = c("COEN", "GRN", "new"),
topNetPercent = 5, directed = FALSE, rowSample = FALSE,
softPower = NULL, networkType = "unsigned",
TOMDenom = "min",
RsquaredCut = 0.85, edgeThreshold = NULL,
K = "sqrt", nbTrees = 1000,
importanceMeasure = "IncNodePurity", trace = FALSE,
minR = 0.3, enrichTest = c("FET", "GSEA"),
namedScoresCutoffs = 0.05,
minSize = 5, maxSize = 5000,
pvalueCutoff = 0.05, qvalueCutoff = 0.2,
regAltName = NULL, universe = NULL, nperm = 10000) {
if (missing(expr) || missing(colData)) {
stop("Must provide both 'expr' and 'colData'")
}
if (ncol(expr) != nrow(colData)) {
stop("The number of columns in 'expr' must be ",
"the same as the number of rows in 'colData'")
if (all(colnames(expr) != rownames(colData))) {
stop("The columns of 'expr' must match the rows of 'colData'")
}
}
se = SummarizedExperiment(assays = SimpleList(assayData = as.matrix(expr)),
rowData = rowData,
colData = as(colData, "DataFrame"))
seHi = if (!is.null(minMeanExpr) && is.numeric(minMeanExpr)) {
se[rowMeans(assay(se)) > minMeanExpr,]
} else se
rowN = nrow(seHi)
NAMES = rownames(seHi)
if(ncol(rowData(seHi)) != 0){
if(all(c("gene", "p", "logFC") %in% colnames(rowData))){
rowData = rowData(seHi)
} else {
stop("rowData must be either NULL or a data frame containing at",
"least the following columns: \n 'gene', 'p', 'logFC'")
}
} else {
if (is.null(NAMES)){
stop("Either rownames(expr) or rowData must not be NULL.")
}
rowData = DataFrame(gene = NAMES,
p = numeric(rowN),
logFC = numeric(rowN),
row.names = NAMES)
}
par = list(method = method,
minMeanExpr = minMeanExpr,
design = if (missing(design)) substitute() else design,
reduced = if (missing(reduced)) substitute() else reduced,
contrast = if (missing(contrast)) substitute() else contrast,
coef = if (missing(coef)) substitute() else coef,
name = if (missing(name)) substitute() else name,
fitType = fitType, sfType = sfType,
betaPrior = if (missing(betaPrior)) substitute() else betaPrior,
minReplicatesForReplace = minReplicatesForReplace,
useT = useT, minmu = minmu, parallel = parallel,
BPPARAM = BPPARAM, altHypothesis = altHypothesis,
listValues = listValues,
cooksCutoff = if (missing(cooksCutoff)) {
substitute()} else cooksCutoff,
independentFiltering = independentFiltering, alpha = alpha,
filter = if (missing(filter)) substitute() else filter,
theta = if (missing(theta)) substitute() else theta,
filterFun = if (missing(filterFun)) substitute() else filterFun,
addMLE = addMLE, blind = blind, ndups = ndups, spacing = spacing,
block = block,
correlation = if (missing(correlation)) {
substitute()} else correlation,
weights = weights, proportion = proportion,
stdev.coef.lim = stdev.coef.lim,
trend = trend, robust = robust, winsor.tail.p = winsor.tail.p,
reg = reg, rowSample = rowSample, softPower = softPower,
networkConstruction = networkConstruction,
networkType = networkType,
TOMDenom = TOMDenom, RsquaredCut = RsquaredCut,
edgeThreshold = edgeThreshold,
K = K, nbTrees = nbTrees, importanceMeasure = importanceMeasure,
trace = trace,
minR = minR, topNetPercent = topNetPercent, directed = directed,
enrichTest = enrichTest, namedScoresCutoffs = namedScoresCutoffs,
minSize = minSize, maxSize = maxSize, pvalueCutoff = pvalueCutoff,
qvalueCutoff = qvalueCutoff, regAltName = regAltName,
universe = universe, nperm = nperm)
object = new(Class = "RegenrichSet",
assayRaw = assay(se),
assays = Assays(assays(seHi)),
colData = colData(seHi),
NAMES = NAMES,
elementMetadata = rowData,
paramsIn = par,
topNetwork = newTopNetwork(),
resEnrich = newEnrich(),
resScore = newScore(),
paramsOut = list(DeaMethod = NULL, networkType = NULL,
enrichTest = NULL, percent = NULL),
network = newTopNetwork())
return(object)
}
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