R/map_information_on_reaction_network.R
In michaelwitting/wormLipidPredictR: Lipid reaction prediction based on WormJam
Defines functions
add_vertex_attributes
add_edge_attributes
add_edge_attributes_from_data.frame
add_attributes
Documented in
add_attributes
add_edge_attributes
add_edge_attributes_from_data.frame
add_vertex_attributes
#' @name add_attributes
#'
#' @title Add attributes to igraph object
#'
#' @description
#' The function \code{add_attributes} adds attributes to an \code{igraph}
#' object. Attributes can be either added for edges
#' \code{attribute_type = "edges"} or vertices
#' \code{attribute_type = "vertices"}. The function will return a \code{igraph}
#' object.
#'
#' For \code{attribute_type = "edges"}, the function adds edge weights to a
#' \code{igraph} object \code{g}. The weights are stored in the
#' \code{attributes} object. The function will return a \code{igraph} object
#' with updated edge weights.
#' The \code{attributes} object can be either a \code{matrix} or a
#' \code{data.frame}.
#' The \code{matrix} is an adjacency matrix containing as
#' entries the weights. The weights will be stored in the \code{E(g)$value}
#' slot of the returned \code{igraph} object.
#' The \code{data.frame} contains the columns \code{vertex}, a
#' \code{character} vector of length 2, specifying the out- and ingoing
#' vertices for the edge and the edge weights in the remaining columns. The
#' weights will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} of the returned \code{igraph} object.
#'
#' For \code{attribute_type = "vertices"}, the function adds vertex attributes
#' to a \code{igraph} object \code{g}. The values are stored in the
#' \code{attributes} object. The function will return a \code{igraph} object
#' with updated vertex attributes.
#' The \code{attributes} object is a \code{data.frame}.
#' The \code{data.frame} contains the columns \code{col_vertex}, a
#' \code{character} vector of length 1, specifying the vertices and
#' the vertex attributes weights in the remaining columns. The
#' attributes will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} for each vertex
#' of the returned \code{igraph} object.
#' @details
#' For \code{attribute_type = "edges"}, \code{cols_vertex} has to be adjusted
#' only when \code{attributes} is a \code{data.frame}. The \code{character}
#' of length 2 will specify the columns
#' containing the out- and ingoing vertices of the graph.
#' For \code{attribute_type = "vertices"}, \code{cols_vertex} has to be
#' adjusted such that it specifies the vertices.
#' The \code{character} of length 1 will specify the column
#' containing the vertices of the graph.
#'
#' @param g igraph object
#' @param attribute_type \code{character} of length 1, either \code{"edges"} or
#' \code{"vertices"}
#' @param attributes \code{data.frame} or \code{matrix} containing edge or
#' vertex attribute information
#' @param cols_vertex \code{character} of length 1 or length 2 specifying the
#' columns in \code{attributes}
#'
#' @export
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, weighted = TRUE,
#' diag = FALSE)
#'
#' ## attribute_type: vertex
#' attributes_df <- data.frame(
#' name = c("CoA(12:0)", "CoA(14:0)", "CoA(16:0)", "DG(12:0/12:0/0:0)",
#' "DG(12:0/14:0/0:0)", "DG(12:0/16:0/0:0)", "DG(14:0/12:0/0:0)",
#' "DG(14:0/14:0/0:0)", "DG(14:0/16:0/0:0)", "DG(16:0/12:0/0:0)",
#' "DG(16:0/14:0/0:0)", "DG(16:0/16:0/0:0)", "FA(12:0)", "FA(14:0)",
#' "FA(16:0)", "PA(12:0/0:0)", "PA(12:0/12:0)", "PA(12:0/14:0)",
#' "PA(12:0/16:0)", "PA(14:0/0:0)", "PA(14:0/12:0)", "PA(14:0/14:0)",
#' "PA(14:0/16:0)", "PA(16:0/0:0)", "PA(16:0/12:0)", "PA(16:0/14:0)",
#' "PA(16:0/16:0)"),
#' logFC_cond1 = c(-5.08, 0.75, 5.43, -0.62, 2.35, 1.39, 2.91, 0.26,
#' -4.14, 0.19, 6.18, 0.78, -1.81, 4.66, -0.10, 2.84, -0.81,
#' -0.81, -0.32, 0.17, 2.25, -1.94, 0.80, 4.21, 0.20, -3.29,
#' -0.11),
#' logFC_cond2 = c(-2.73, 6.14, 1.98, 0.09, 1.57, 1.77, 3.08, 4.04,
#' -3.01, 1.22, -4.25, 0.39, 0.53, 3.30, 7.10, 2.81, -0.99, -0.09,
#' -8.25, 4.94, -3.54, -7.74, -1.98, 0.73, 2.36, 2.53, -0.62))
#'
#' ## apply the function
#' add_attributes(g, attribute_type = "vertex", attributes = attributes_df,
#' cols_vertex = "name")
#'
#' ## attribute_type: edges, attributes: data.frame
#' attributes <- data.frame(
#' rbind(
#' c("CoA(12:0)", "PA(12:0/0:0)", 0.5),
#' c("CoA(12:0)", "PA(14:0/12:0)", 0.8)
#' ))
#' names(attributes) <- c("from", "to", "weight")
#' attributes$weight <- as.numeric(attributes$weight)
#'
#' ## apply the function
#' add_attributes(g, attribute_type = "edges", attributes = attributes, cols_vertex = c("from", "to"))
#'
#' ## attribute_type:edges, attributes: matrix
#' attributes <- matrix(c(0, 0.5, 0.8, 0, 0, 0, 0, 0, 0), ncol = 3, byrow = TRUE,
#' dimnames = list(
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)"),
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)")))
#'
#' ## apply the function
#' add_attributes(g, attribute_type = "edges", attributes = attributes)
add_attributes <- function(g, attribute_type = c("edges", "vertex"),
attributes, cols_vertex = colnames(attributes)[1:2]) {
## information can be either submitted via edges or nodes
## edges: e.g. correlation between two features
## nodes: e.g. log-fold change
attribute_type <- match.arg(attribute_type)
if (!all(cols_vertex %in% colnames(attributes)))
stop("'cols_vertex' not in 'colnames(attributes)'.")
## for attribute_type == "edges"
if (attribute_type == "edges")
g <- add_edge_attributes(g = g, attributes = attributes,
cols_vertex = cols_vertex)
## for attribute_type == "vertex"
if (attribute_type == "vertex")
g <- add_vertex_attributes(g = g, attributes = attributes,
col_vertex = cols_vertex)
## return the graph
g
}
#' @name add_edge_attributes_from_data.frame
#'
#' @title Add edge attributes to \code{igraph} object from \code{data.frame}
#'
#' @description
#' The function adds edge weights to a \code{igraph} object \code{g}. The
#' weights are stored in the \code{attributes} object. The function will return
#' a \code{igraph} object with updated edge weights.
#'
#' The \code{attributes} object is a \code{data.frame}.
#'
#' The \code{data.frame} contains the columns \code{vertex}, a
#' \code{character} vector of length 2, specifying the out- and ingoing
#' vertices for the edge and the edge weights in the remaining columns. The
#' weights will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} of the returned \code{igraph} object.#'
#'
#' @details
#' \code{vertex} has to be adjusted to the \code{attributes} object.
#' The \code{character} of length 2 will specify the columns
#' containing the out- and ingoing vertices of the graph.
#'
#' @param g \code{igraph} object
#' @param attributes \code{data.frame} containing edge
#' attribute information
#' @param column_from \code{chararacter} of length 1, specifying the columns
#' containing the outgoing vertices in \code{attributes} of type
#' \code{data.frame}
#' @param column_to \code{chararacter} of length 1, specifying the columns
#' containing the ingoing vertices in \code{attributes} of type
#' \code{data.frame}
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @importFrom stats setNames
#' @importFrom igraph set_edge_attr
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' ## create graph
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, mode = "directed", weighted = TRUE)
#'
#' ## attributes: data.frame
#' attributes <- data.frame(
#' rbind(
#' c("CoA(12:0)", "PA(12:0/0:0)", 0.5),
#' c("CoA(12:0)", "PA(14:0/12:0)", 0.8)
#' ))
#' names(attributes) <- c("from", "to", "weight")
#' attributes$weight <- as.numeric(attributes$weight)
#'
#' ## apply the function
#' LipidNetworkPredictR:::add_edge_attributes_from_data.frame(g, attributes, column_from = "from",
#' column_to = "to")
add_edge_attributes_from_data.frame <- function(g, attributes,
column_from = "from", column_to = "to") {
if (!is.data.frame(attributes))
stop("'attributes' has to be a data.frame.")
if (length(column_from) != 1)
stop("'column_from' has to be of length 1.")
if (!column_from %in% colnames(attributes))
stop("'column_from' not in 'colnames(attributes)'.")
if (length(column_to) != 1)
stop("'column_to' has to be of length 1.")
if (!column_to %in% colnames(attributes))
stop("'column_to' not in 'colnames(attributes)'.")
if (ncol(attributes) < 3)
stop("The data.frame must have at least 3 columns: 'from', 'to' and attribute column(s).")
## subset attributes such that it does not contain entries that are not
## vertices of g
attributes <- attributes[
attributes[[column_from]] %in% names(V(g)) &
attributes[[column_to]] %in% names(V(g)), ]
## extract vertex names and attribute names
from_vertices <- attributes[[column_from]]
to_vertices <- attributes[[column_to]]
cols <- colnames(attributes)
attribute_names <- cols[!(cols %in% c(column_from, column_to))]
## iterate through each attribute column to update weights
for (attr in attribute_names) {
## create a names vector of new attribute values
new_values <- stats::setNames(attributes[[attr]],
paste(from_vertices, to_vertices, sep = "|"))
## assure that edges in g are found in new_values
new_values <- new_values[names(new_values) %in% attr(igraph::E(g), "vnames")]
## update the graph's edge attributes
## initialize the attribute of the graph
g <- igraph::set_edge_attr(g, attr, value = NA)
## match and update values
g <- igraph::set_edge_attr(g, attr, index = names(new_values),
value = as.vector(new_values))
}
## return the updated graph
g
}
#' @name add_edge_attributes
#'
#' @title Add edge attributes to \code{igraph} object
#'
#' @description
#' The function adds edge weights to a \code{igraph} object \code{g}. The
#' weights are stored in the \code{attributes} object. The function will return
#' a \code{igraph} object with updated edge weights.
#'
#' The \code{attributes} object can be either a \code{matrix} or a
#' \code{data.frame}.
#'
#' The \code{matrix} is an adjacency matrix containing as
#' entries the weights. The weights will be stored in the \code{E(g)$value}
#' slot of the returned \code{igraph} object.
#'
#' The \code{data.frame} contains the columns \code{vertex}, a
#' \code{character} vector of length 2, specifying the out- and ingoing
#' vertices for the edge and the edge weights in the remaining columns. The
#' weights will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} of the returned \code{igraph} object.
#'
#' @details
#' \code{cols_vertex} has to be adjusted only when \code{attributes} is a
#' \code{data.frame}. The \code{character} of length 2 will specify the columns
#' containing the out- and ingoing vertices of the graph.
#'
#' In case of \code{attributes} is a \code{matrix}, the matrix entries will be
#' stored in \code{E(g)$value}
#'
#' @param g \code{igraph} object
#' @param attributes \code{matrix} or \code{data.frame} containing edge
#' attribute information
#' @param cols_vertex \code{chararacter} of length 2, specifying the columns
#' containing the out- and ingoing vertices in \code{attributes} of type
#' \code{data.frame}
#'
#' @export
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @importFrom tidyr pivot_longer
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' ## create graph
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, mode = "directed", weighted = TRUE)
#'
#' ## attributes: data.frame
#' attributes <- data.frame(
#' rbind(
#' c("CoA(12:0)", "PA(12:0/0:0)", 0.5),
#' c("CoA(12:0)", "PA(14:0/12:0)", 0.8)
#' ))
#' names(attributes) <- c("from", "to", "weight")
#' attributes$weight <- as.numeric(attributes$weight)
#'
#' ## apply the function
#' add_edge_attributes(g, attributes, cols_vertex = c("from", "to"))
#'
#' ## attributes: matrix
#' attributes <- matrix(c(0, 0.5, 0.8, 0, 0, 0, 0, 0, 0), ncol = 3, byrow = TRUE,
#' dimnames = list(
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)"),
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)")))
#'
#' ## apply the function
#' add_edge_attributes(g, attributes)
add_edge_attributes <- function(g, attributes, cols_vertex = colnames(attributes)[1:2]) {
## information can be submitted via edges
## edges: e.g. correlation between two features
## there are two ways of storing the edge information: either in a long
## data.frame or in an adjacency matrix
## long data.frame: the columns values_from and values_to contain the edge
## information while the other columns (at least one) contain the weights
if (is.data.frame(attributes)) {
if (length(cols_vertex) != 2)
stop("'cols_vertex' has to be of length 2.")
if (!all(cols_vertex %in% colnames(attributes)))
stop("'cols_vertex' have to be in colnames(attributes).")
g <- add_edge_attributes_from_data.frame(g, attributes = attributes,
column_from = cols_vertex[1], column_to = cols_vertex[2])
}
## adjacency matrix: the matrix will be first converted into a long
## data.frame, the graph is updated using the long data.frame
if (is.matrix(attributes)) {
if (ncol(attributes) != nrow(attributes))
stop("'attributes' has to be a square matrix.")
## convert into long data.frame
attributes_df <- attributes |>
as.data.frame()
attributes_df[["Row"]] <- rownames(attributes)
attributes_df <- tidyr::pivot_longer(attributes_df,
cols = -c("Row"), names_to = "Col", values_to = "value") |>
as.data.frame()
## add edge attributes from long data.frame
g <- add_edge_attributes_from_data.frame(g,
attributes = attributes_df,
column_from = "Row", column_to = "Col")
}
## return the updated graph object
g
}
#' @name add_vertex_attributes
#'
#' @title Add vertex attributes to \code{igraph} object
#'
#' @description
#' The function adds vertex attributes to a \code{igraph} object \code{g}. The
#' values are stored in the \code{attributes} object. The function will return
#' a \code{igraph} object with updated vertex attributes.
#'
#' The \code{attributes} object is a \code{data.frame}.
#'
#' The \code{data.frame} contains the columns \code{col_vertex}, a
#' \code{character} vector of length 1, specifying the vertices and
#' the vertex attributes weights in the remaining columns. The
#' attributes will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} for each vertex
#' of the returned \code{igraph} object.
#'
#' @details
#' \code{col_vertex} has to be adjusted such that it specifies the vertices.
#' The \code{character} of length 1 will specify the column
#' containing the vertices of the graph.
#'
#' @param g \code{igraph} object
#' @param attributes \code{data.frame} containing vertex
#' attribute information
#' @param col_vertex \code{chararacter} of length 1, specifying the column
#' containing vertices in \code{attributes} of type \code{data.frame}
#'
#' @export
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @importFrom igraph set_vertex_attr V
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' ## create graph
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, mode = "directed", weighted = TRUE)
#'
#' ## attributes: data.frame
#' attributes_df <- data.frame(
#' name = c("CoA(12:0)", "CoA(14:0)", "CoA(16:0)", "DG(12:0/12:0/0:0)",
#' "DG(12:0/14:0/0:0)", "DG(12:0/16:0/0:0)", "DG(14:0/12:0/0:0)",
#' "DG(14:0/14:0/0:0)", "DG(14:0/16:0/0:0)", "DG(16:0/12:0/0:0)",
#' "DG(16:0/14:0/0:0)", "DG(16:0/16:0/0:0)", "FA(12:0)", "FA(14:0)",
#' "FA(16:0)", "PA(12:0/0:0)", "PA(12:0/12:0)", "PA(12:0/14:0)",
#' "PA(12:0/16:0)", "PA(14:0/0:0)", "PA(14:0/12:0)", "PA(14:0/14:0)",
#' "PA(14:0/16:0)", "PA(16:0/0:0)", "PA(16:0/12:0)", "PA(16:0/14:0)",
#' "PA(16:0/16:0)"),
#' logFC_cond1 = c(-5.08, 0.75, 5.43, -0.62, 2.35, 1.39, 2.91, 0.26,
#' -4.14, 0.19, 6.18, 0.78, -1.81, 4.66, -0.10, 2.84, -0.81,
#' -0.81, -0.32, 0.17, 2.25, -1.94, 0.80, 4.21, 0.20, -3.29,
#' -0.11),
#' logFC_cond2 = c(-2.73, 6.14, 1.98, 0.09, 1.57, 1.77, 3.08, 4.04,
#' -3.01, 1.22, -4.25, 0.39, 0.53, 3.30, 7.10, 2.81, -0.99, -0.09,
#' -8.25, 4.94, -3.54, -7.74, -1.98, 0.73, 2.36, 2.53, -0.62))
#'
#' ## apply the function
#' add_vertex_attributes(g = g, attributes = attributes_df, col_vertex = "name")
add_vertex_attributes <- function(g, attributes, col_vertex = colnames(attributes)[1]) {
## check arguments
if (!is.data.frame(attributes))
stop("'attributes' has to be a data.frame.")
if (length(col_vertex) != 1)
stop("'col_vertex' has to be of length 1.")
if (!col_vertex %in% colnames(attributes))
stop("'col_vertex' not in 'colnames(attributes)'.")
if (any(duplicated(attributes[[col_vertex]])))
stop("'attributes[[col_vertex]]' contains duplicated entries.")
## subset attributes such that it does not contain entries that are not
## vertices of g
attributes <- attributes[attributes[[col_vertex]] %in% names(igraph::V(g)), ]
## obtain the colnames, obtain the columns that store attribute information
cols_df <- colnames(attributes)
cols_df_attr <- cols_df[cols_df != col_vertex]
## iterate trough the attribute columns and add attribute information
for (attr_i in cols_df_attr) {
## set V(g) for cols_df_attr to NA
g <- igraph::set_vertex_attr(graph = g, name = attr_i, value = NA)
## set V(g) for cols_df_attr to attribute values
g <- igraph::set_vertex_attr(graph = g, name = attr_i,
index = attributes[[col_vertex]],
value = attributes[[attr_i]])
}
## return the graph
g
}
michaelwitting/wormLipidPredictR documentation built on Dec. 20, 2024, 5:35 a.m.
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Defines functions add_vertex_attributes add_edge_attributes add_edge_attributes_from_data.frame add_attributes
Documented in add_attributes add_edge_attributes add_edge_attributes_from_data.frame add_vertex_attributes
#' @name add_attributes
#'
#' @title Add attributes to igraph object
#'
#' @description
#' The function \code{add_attributes} adds attributes to an \code{igraph}
#' object. Attributes can be either added for edges
#' \code{attribute_type = "edges"} or vertices
#' \code{attribute_type = "vertices"}. The function will return a \code{igraph}
#' object.
#'
#' For \code{attribute_type = "edges"}, the function adds edge weights to a
#' \code{igraph} object \code{g}. The weights are stored in the
#' \code{attributes} object. The function will return a \code{igraph} object
#' with updated edge weights.
#' The \code{attributes} object can be either a \code{matrix} or a
#' \code{data.frame}.
#' The \code{matrix} is an adjacency matrix containing as
#' entries the weights. The weights will be stored in the \code{E(g)$value}
#' slot of the returned \code{igraph} object.
#' The \code{data.frame} contains the columns \code{vertex}, a
#' \code{character} vector of length 2, specifying the out- and ingoing
#' vertices for the edge and the edge weights in the remaining columns. The
#' weights will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} of the returned \code{igraph} object.
#'
#' For \code{attribute_type = "vertices"}, the function adds vertex attributes
#' to a \code{igraph} object \code{g}. The values are stored in the
#' \code{attributes} object. The function will return a \code{igraph} object
#' with updated vertex attributes.
#' The \code{attributes} object is a \code{data.frame}.
#' The \code{data.frame} contains the columns \code{col_vertex}, a
#' \code{character} vector of length 1, specifying the vertices and
#' the vertex attributes weights in the remaining columns. The
#' attributes will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} for each vertex
#' of the returned \code{igraph} object.
#' @details
#' For \code{attribute_type = "edges"}, \code{cols_vertex} has to be adjusted
#' only when \code{attributes} is a \code{data.frame}. The \code{character}
#' of length 2 will specify the columns
#' containing the out- and ingoing vertices of the graph.
#' For \code{attribute_type = "vertices"}, \code{cols_vertex} has to be
#' adjusted such that it specifies the vertices.
#' The \code{character} of length 1 will specify the column
#' containing the vertices of the graph.
#'
#' @param g igraph object
#' @param attribute_type \code{character} of length 1, either \code{"edges"} or
#' \code{"vertices"}
#' @param attributes \code{data.frame} or \code{matrix} containing edge or
#' vertex attribute information
#' @param cols_vertex \code{character} of length 1 or length 2 specifying the
#' columns in \code{attributes}
#'
#' @export
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, weighted = TRUE,
#' diag = FALSE)
#'
#' ## attribute_type: vertex
#' attributes_df <- data.frame(
#' name = c("CoA(12:0)", "CoA(14:0)", "CoA(16:0)", "DG(12:0/12:0/0:0)",
#' "DG(12:0/14:0/0:0)", "DG(12:0/16:0/0:0)", "DG(14:0/12:0/0:0)",
#' "DG(14:0/14:0/0:0)", "DG(14:0/16:0/0:0)", "DG(16:0/12:0/0:0)",
#' "DG(16:0/14:0/0:0)", "DG(16:0/16:0/0:0)", "FA(12:0)", "FA(14:0)",
#' "FA(16:0)", "PA(12:0/0:0)", "PA(12:0/12:0)", "PA(12:0/14:0)",
#' "PA(12:0/16:0)", "PA(14:0/0:0)", "PA(14:0/12:0)", "PA(14:0/14:0)",
#' "PA(14:0/16:0)", "PA(16:0/0:0)", "PA(16:0/12:0)", "PA(16:0/14:0)",
#' "PA(16:0/16:0)"),
#' logFC_cond1 = c(-5.08, 0.75, 5.43, -0.62, 2.35, 1.39, 2.91, 0.26,
#' -4.14, 0.19, 6.18, 0.78, -1.81, 4.66, -0.10, 2.84, -0.81,
#' -0.81, -0.32, 0.17, 2.25, -1.94, 0.80, 4.21, 0.20, -3.29,
#' -0.11),
#' logFC_cond2 = c(-2.73, 6.14, 1.98, 0.09, 1.57, 1.77, 3.08, 4.04,
#' -3.01, 1.22, -4.25, 0.39, 0.53, 3.30, 7.10, 2.81, -0.99, -0.09,
#' -8.25, 4.94, -3.54, -7.74, -1.98, 0.73, 2.36, 2.53, -0.62))
#'
#' ## apply the function
#' add_attributes(g, attribute_type = "vertex", attributes = attributes_df,
#' cols_vertex = "name")
#'
#' ## attribute_type: edges, attributes: data.frame
#' attributes <- data.frame(
#' rbind(
#' c("CoA(12:0)", "PA(12:0/0:0)", 0.5),
#' c("CoA(12:0)", "PA(14:0/12:0)", 0.8)
#' ))
#' names(attributes) <- c("from", "to", "weight")
#' attributes$weight <- as.numeric(attributes$weight)
#'
#' ## apply the function
#' add_attributes(g, attribute_type = "edges", attributes = attributes, cols_vertex = c("from", "to"))
#'
#' ## attribute_type:edges, attributes: matrix
#' attributes <- matrix(c(0, 0.5, 0.8, 0, 0, 0, 0, 0, 0), ncol = 3, byrow = TRUE,
#' dimnames = list(
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)"),
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)")))
#'
#' ## apply the function
#' add_attributes(g, attribute_type = "edges", attributes = attributes)
add_attributes <- function(g, attribute_type = c("edges", "vertex"),
attributes, cols_vertex = colnames(attributes)[1:2]) {
## information can be either submitted via edges or nodes
## edges: e.g. correlation between two features
## nodes: e.g. log-fold change
attribute_type <- match.arg(attribute_type)
if (!all(cols_vertex %in% colnames(attributes)))
stop("'cols_vertex' not in 'colnames(attributes)'.")
## for attribute_type == "edges"
if (attribute_type == "edges")
g <- add_edge_attributes(g = g, attributes = attributes,
cols_vertex = cols_vertex)
## for attribute_type == "vertex"
if (attribute_type == "vertex")
g <- add_vertex_attributes(g = g, attributes = attributes,
col_vertex = cols_vertex)
## return the graph
g
}
#' @name add_edge_attributes_from_data.frame
#'
#' @title Add edge attributes to \code{igraph} object from \code{data.frame}
#'
#' @description
#' The function adds edge weights to a \code{igraph} object \code{g}. The
#' weights are stored in the \code{attributes} object. The function will return
#' a \code{igraph} object with updated edge weights.
#'
#' The \code{attributes} object is a \code{data.frame}.
#'
#' The \code{data.frame} contains the columns \code{vertex}, a
#' \code{character} vector of length 2, specifying the out- and ingoing
#' vertices for the edge and the edge weights in the remaining columns. The
#' weights will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} of the returned \code{igraph} object.#'
#'
#' @details
#' \code{vertex} has to be adjusted to the \code{attributes} object.
#' The \code{character} of length 2 will specify the columns
#' containing the out- and ingoing vertices of the graph.
#'
#' @param g \code{igraph} object
#' @param attributes \code{data.frame} containing edge
#' attribute information
#' @param column_from \code{chararacter} of length 1, specifying the columns
#' containing the outgoing vertices in \code{attributes} of type
#' \code{data.frame}
#' @param column_to \code{chararacter} of length 1, specifying the columns
#' containing the ingoing vertices in \code{attributes} of type
#' \code{data.frame}
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @importFrom stats setNames
#' @importFrom igraph set_edge_attr
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' ## create graph
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, mode = "directed", weighted = TRUE)
#'
#' ## attributes: data.frame
#' attributes <- data.frame(
#' rbind(
#' c("CoA(12:0)", "PA(12:0/0:0)", 0.5),
#' c("CoA(12:0)", "PA(14:0/12:0)", 0.8)
#' ))
#' names(attributes) <- c("from", "to", "weight")
#' attributes$weight <- as.numeric(attributes$weight)
#'
#' ## apply the function
#' LipidNetworkPredictR:::add_edge_attributes_from_data.frame(g, attributes, column_from = "from",
#' column_to = "to")
add_edge_attributes_from_data.frame <- function(g, attributes,
column_from = "from", column_to = "to") {
if (!is.data.frame(attributes))
stop("'attributes' has to be a data.frame.")
if (length(column_from) != 1)
stop("'column_from' has to be of length 1.")
if (!column_from %in% colnames(attributes))
stop("'column_from' not in 'colnames(attributes)'.")
if (length(column_to) != 1)
stop("'column_to' has to be of length 1.")
if (!column_to %in% colnames(attributes))
stop("'column_to' not in 'colnames(attributes)'.")
if (ncol(attributes) < 3)
stop("The data.frame must have at least 3 columns: 'from', 'to' and attribute column(s).")
## subset attributes such that it does not contain entries that are not
## vertices of g
attributes <- attributes[
attributes[[column_from]] %in% names(V(g)) &
attributes[[column_to]] %in% names(V(g)), ]
## extract vertex names and attribute names
from_vertices <- attributes[[column_from]]
to_vertices <- attributes[[column_to]]
cols <- colnames(attributes)
attribute_names <- cols[!(cols %in% c(column_from, column_to))]
## iterate through each attribute column to update weights
for (attr in attribute_names) {
## create a names vector of new attribute values
new_values <- stats::setNames(attributes[[attr]],
paste(from_vertices, to_vertices, sep = "|"))
## assure that edges in g are found in new_values
new_values <- new_values[names(new_values) %in% attr(igraph::E(g), "vnames")]
## update the graph's edge attributes
## initialize the attribute of the graph
g <- igraph::set_edge_attr(g, attr, value = NA)
## match and update values
g <- igraph::set_edge_attr(g, attr, index = names(new_values),
value = as.vector(new_values))
}
## return the updated graph
g
}
#' @name add_edge_attributes
#'
#' @title Add edge attributes to \code{igraph} object
#'
#' @description
#' The function adds edge weights to a \code{igraph} object \code{g}. The
#' weights are stored in the \code{attributes} object. The function will return
#' a \code{igraph} object with updated edge weights.
#'
#' The \code{attributes} object can be either a \code{matrix} or a
#' \code{data.frame}.
#'
#' The \code{matrix} is an adjacency matrix containing as
#' entries the weights. The weights will be stored in the \code{E(g)$value}
#' slot of the returned \code{igraph} object.
#'
#' The \code{data.frame} contains the columns \code{vertex}, a
#' \code{character} vector of length 2, specifying the out- and ingoing
#' vertices for the edge and the edge weights in the remaining columns. The
#' weights will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} of the returned \code{igraph} object.
#'
#' @details
#' \code{cols_vertex} has to be adjusted only when \code{attributes} is a
#' \code{data.frame}. The \code{character} of length 2 will specify the columns
#' containing the out- and ingoing vertices of the graph.
#'
#' In case of \code{attributes} is a \code{matrix}, the matrix entries will be
#' stored in \code{E(g)$value}
#'
#' @param g \code{igraph} object
#' @param attributes \code{matrix} or \code{data.frame} containing edge
#' attribute information
#' @param cols_vertex \code{chararacter} of length 2, specifying the columns
#' containing the out- and ingoing vertices in \code{attributes} of type
#' \code{data.frame}
#'
#' @export
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @importFrom tidyr pivot_longer
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' ## create graph
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, mode = "directed", weighted = TRUE)
#'
#' ## attributes: data.frame
#' attributes <- data.frame(
#' rbind(
#' c("CoA(12:0)", "PA(12:0/0:0)", 0.5),
#' c("CoA(12:0)", "PA(14:0/12:0)", 0.8)
#' ))
#' names(attributes) <- c("from", "to", "weight")
#' attributes$weight <- as.numeric(attributes$weight)
#'
#' ## apply the function
#' add_edge_attributes(g, attributes, cols_vertex = c("from", "to"))
#'
#' ## attributes: matrix
#' attributes <- matrix(c(0, 0.5, 0.8, 0, 0, 0, 0, 0, 0), ncol = 3, byrow = TRUE,
#' dimnames = list(
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)"),
#' c("CoA(12:0)", "PA(12:0/0:0)", "PA(14:0/12:0)")))
#'
#' ## apply the function
#' add_edge_attributes(g, attributes)
add_edge_attributes <- function(g, attributes, cols_vertex = colnames(attributes)[1:2]) {
## information can be submitted via edges
## edges: e.g. correlation between two features
## there are two ways of storing the edge information: either in a long
## data.frame or in an adjacency matrix
## long data.frame: the columns values_from and values_to contain the edge
## information while the other columns (at least one) contain the weights
if (is.data.frame(attributes)) {
if (length(cols_vertex) != 2)
stop("'cols_vertex' has to be of length 2.")
if (!all(cols_vertex %in% colnames(attributes)))
stop("'cols_vertex' have to be in colnames(attributes).")
g <- add_edge_attributes_from_data.frame(g, attributes = attributes,
column_from = cols_vertex[1], column_to = cols_vertex[2])
}
## adjacency matrix: the matrix will be first converted into a long
## data.frame, the graph is updated using the long data.frame
if (is.matrix(attributes)) {
if (ncol(attributes) != nrow(attributes))
stop("'attributes' has to be a square matrix.")
## convert into long data.frame
attributes_df <- attributes |>
as.data.frame()
attributes_df[["Row"]] <- rownames(attributes)
attributes_df <- tidyr::pivot_longer(attributes_df,
cols = -c("Row"), names_to = "Col", values_to = "value") |>
as.data.frame()
## add edge attributes from long data.frame
g <- add_edge_attributes_from_data.frame(g,
attributes = attributes_df,
column_from = "Row", column_to = "Col")
}
## return the updated graph object
g
}
#' @name add_vertex_attributes
#'
#' @title Add vertex attributes to \code{igraph} object
#'
#' @description
#' The function adds vertex attributes to a \code{igraph} object \code{g}. The
#' values are stored in the \code{attributes} object. The function will return
#' a \code{igraph} object with updated vertex attributes.
#'
#' The \code{attributes} object is a \code{data.frame}.
#'
#' The \code{data.frame} contains the columns \code{col_vertex}, a
#' \code{character} vector of length 1, specifying the vertices and
#' the vertex attributes weights in the remaining columns. The
#' attributes will be stored in the respective slots with same names as the
#' \code{colnames} of \code{attributes} for each vertex
#' of the returned \code{igraph} object.
#'
#' @details
#' \code{col_vertex} has to be adjusted such that it specifies the vertices.
#' The \code{character} of length 1 will specify the column
#' containing the vertices of the graph.
#'
#' @param g \code{igraph} object
#' @param attributes \code{data.frame} containing vertex
#' attribute information
#' @param col_vertex \code{chararacter} of length 1, specifying the column
#' containing vertices in \code{attributes} of type \code{data.frame}
#'
#' @export
#'
#' @return igraph object
#'
#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
#'
#' @importFrom igraph set_vertex_attr V
#'
#' @examples
#' FA <- c("FA(12:0)", "FA(14:0)", "FA(16:0)")
#'
#' ## create data.frame with reactions and reaction order
#' reactions <- rbind(
#' c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),
#' c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),
#' c(3, "RHEA:19709", "M_AcylCoA + M_LPA = M_CoA + M_PA", FALSE),
#' c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)
#' )
#' reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],
#' reaction_formula = reactions[, 3], directed = reactions[, 4])
#' reactions$order <- as.numeric(reactions$order)
#' reactions$directed <- as.logical(reactions$directed)
#'
#' ## create the list with reactions
#' reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)
#'
#' ## create the adjacency matrix
#' reaction_adj <- create_reaction_adjacency_matrix(reaction_l)
#'
#' ## create graph
#' g <- igraph::graph_from_adjacency_matrix(reaction_adj, mode = "directed", weighted = TRUE)
#'
#' ## attributes: data.frame
#' attributes_df <- data.frame(
#' name = c("CoA(12:0)", "CoA(14:0)", "CoA(16:0)", "DG(12:0/12:0/0:0)",
#' "DG(12:0/14:0/0:0)", "DG(12:0/16:0/0:0)", "DG(14:0/12:0/0:0)",
#' "DG(14:0/14:0/0:0)", "DG(14:0/16:0/0:0)", "DG(16:0/12:0/0:0)",
#' "DG(16:0/14:0/0:0)", "DG(16:0/16:0/0:0)", "FA(12:0)", "FA(14:0)",
#' "FA(16:0)", "PA(12:0/0:0)", "PA(12:0/12:0)", "PA(12:0/14:0)",
#' "PA(12:0/16:0)", "PA(14:0/0:0)", "PA(14:0/12:0)", "PA(14:0/14:0)",
#' "PA(14:0/16:0)", "PA(16:0/0:0)", "PA(16:0/12:0)", "PA(16:0/14:0)",
#' "PA(16:0/16:0)"),
#' logFC_cond1 = c(-5.08, 0.75, 5.43, -0.62, 2.35, 1.39, 2.91, 0.26,
#' -4.14, 0.19, 6.18, 0.78, -1.81, 4.66, -0.10, 2.84, -0.81,
#' -0.81, -0.32, 0.17, 2.25, -1.94, 0.80, 4.21, 0.20, -3.29,
#' -0.11),
#' logFC_cond2 = c(-2.73, 6.14, 1.98, 0.09, 1.57, 1.77, 3.08, 4.04,
#' -3.01, 1.22, -4.25, 0.39, 0.53, 3.30, 7.10, 2.81, -0.99, -0.09,
#' -8.25, 4.94, -3.54, -7.74, -1.98, 0.73, 2.36, 2.53, -0.62))
#'
#' ## apply the function
#' add_vertex_attributes(g = g, attributes = attributes_df, col_vertex = "name")
add_vertex_attributes <- function(g, attributes, col_vertex = colnames(attributes)[1]) {
## check arguments
if (!is.data.frame(attributes))
stop("'attributes' has to be a data.frame.")
if (length(col_vertex) != 1)
stop("'col_vertex' has to be of length 1.")
if (!col_vertex %in% colnames(attributes))
stop("'col_vertex' not in 'colnames(attributes)'.")
if (any(duplicated(attributes[[col_vertex]])))
stop("'attributes[[col_vertex]]' contains duplicated entries.")
## subset attributes such that it does not contain entries that are not
## vertices of g
attributes <- attributes[attributes[[col_vertex]] %in% names(igraph::V(g)), ]
## obtain the colnames, obtain the columns that store attribute information
cols_df <- colnames(attributes)
cols_df_attr <- cols_df[cols_df != col_vertex]
## iterate trough the attribute columns and add attribute information
for (attr_i in cols_df_attr) {
## set V(g) for cols_df_attr to NA
g <- igraph::set_vertex_attr(graph = g, name = attr_i, value = NA)
## set V(g) for cols_df_attr to attribute values
g <- igraph::set_vertex_attr(graph = g, name = attr_i,
index = attributes[[col_vertex]],
value = attributes[[attr_i]])
}
## return the graph
g
}
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