| Title: | Multi-Omics (time-course) network-based integration and interpretation |
|---|---|
| Description: | netOmics is a multi-omics networks builder and explorer. It uses a combination of network inference algorithms and and knowledge-based graphs to build multi-layered networks. The package can be combined with timeOmics to incorporate time-course expression data and build sub-networks from multi-omics kinetic clusters. Finally, from the generated multi-omics networks, propagation analyses allow the identification of missing biological functions (1), multi-omics mechanisms (2) and molecules between kinetic clusters (3). This helps to resolve complex regulatory mechanisms. |
| Authors: | Antoine Bodein [aut, cre] |
| Maintainer: | Antoine Bodein <[email protected]> |
| License: | GPL-3 |
| Version: | 1.11.0 |
| Built: | 2024-07-02 05:04:45 UTC |
| Source: | https://github.com/bioc/netOmics |
Return a merged graph from two graph layers.
combine_layers(graph1, graph2 = NULL, interaction.df = NULL)combine_layers(graph1, graph2 = NULL, interaction.df = NULL)
graph1 |
an igraph object or list of igraph ( |
graph2 |
an igraph object or list of igraph ( |
interaction.df |
(optional) a 2 colomns data.frame (from, to) describing the edges between vertices from both graphs. |
If graph2 is a single graph, it will be merged to each element of
graph1 (igraph or list.igraph).
If graph2 is a list of graph (list.igraph), each element of
graph1 and each element of graph2 are merged in pairs.
Optionally, interaction.df should be provide if any vertex are shared
between graphs. It can also be used to extend the first graph.
In both scenarios, vertex attributes are kept. If a vertex attribute is missing from graph1 or graph2, NULL value is added. Otherwise, if there is an overlap between attribute values for the same vertex, attribute from graph2 is dropped.
a merged graph with both vertex attributes from graph1 and graph2.
# with single graphs graph1 <- igraph::graph_from_data_frame(list(from = c('A', 'B'), to = c('B', 'C')), directed = FALSE) graph2 <- igraph::graph_from_data_frame(list(from = c(1), to = c(2)), directed = FALSE) res <- combine_layers(graph1 = graph1, graph2 = graph2) # with list of graphs graph1.list <- list(graph1, graph1) graph2.list <- list(graph2, graph2) class(graph1.list) <- class(graph2.list) <- 'list.igraph' res <- combine_layers(graph1 = graph1.list, graph2 = graph2) res <- combine_layers(graph1 = graph1.list, graph2 = graph2.list) # with interaction dataframe interaction.df1 <- as.data.frame(list(from = c('C', 'B'), to = c(1, 2))) res <- combine_layers(graph1 = graph1.list, graph2 = graph2, interaction.df = interaction.df1)# with single graphs graph1 <- igraph::graph_from_data_frame(list(from = c('A', 'B'), to = c('B', 'C')), directed = FALSE) graph2 <- igraph::graph_from_data_frame(list(from = c(1), to = c(2)), directed = FALSE) res <- combine_layers(graph1 = graph1, graph2 = graph2) # with list of graphs graph1.list <- list(graph1, graph1) graph2.list <- list(graph2, graph2) class(graph1.list) <- class(graph2.list) <- 'list.igraph' res <- combine_layers(graph1 = graph1.list, graph2 = graph2) res <- combine_layers(graph1 = graph1.list, graph2 = graph2.list) # with interaction dataframe interaction.df1 <- as.data.frame(list(from = c('C', 'B'), to = c(1, 2))) res <- combine_layers(graph1 = graph1.list, graph2 = graph2, interaction.df = interaction.df1)
From a GO terms (GOID), return definition, ontology and term values from GO.db
get_go_info(go)get_go_info(go)
go |
a character, GO term |
a data.frame with the following columns: 'GOID', 'DEFINITION', 'ONTOLOGY', 'TERM'
For a given igraph or list of igraph objects, this function summarize the number of vertices/edges and other vertex attributes.
get_graph_stats(X)get_graph_stats(X)
X |
an 'igraph' or 'list.igraph' object |
It returns a long data.frame with number of nodes/edges, and the count of the different attributes (if X is a list of graph, each row describes a graph)
graph1 <- igraph::graph_from_data_frame( list(from = c('A', 'B', 'A', 'D', 'C', 'A', 'C'), to = c('B', 'C', 'D', 'E', 'D', 'F', 'G')), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('A','B','C'), value = '1') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('D','E'), value = '2') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('F', 'G'), value = '-1') get_graph_stats(graph1) graph1.list <- list(graph1 = graph1, graph2 = graph1) get_graph_stats(graph1.list)graph1 <- igraph::graph_from_data_frame( list(from = c('A', 'B', 'A', 'D', 'C', 'A', 'C'), to = c('B', 'C', 'D', 'E', 'D', 'F', 'G')), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('A','B','C'), value = '1') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('D','E'), value = '2') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('F', 'G'), value = '-1') get_graph_stats(graph1) graph1.list <- list(graph1 = graph1, graph2 = graph1) get_graph_stats(graph1.list)
Get Gene Regulatory Network (GRN) from a data.frame. Optionally, if the gene are clustered, sub_network are build for each cluster.
get_grn(X, cluster = NULL, method = c("aracne"), type = "gene")get_grn(X, cluster = NULL, method = c("aracne"), type = "gene")
X |
a |
cluster |
(optional) clustering result from
|
method |
network building method, one of c('aracne') |
type |
character added to node metadata |
Methods of GRN reconstruction are as follows: 'aracne': use ARACNe algorithm on Mutual Information (MI) adjency matrix to remove low MI edges in triangles.
An igraph object if no cluster informations are given.
Otherwise, it returns a list of igraph object (list.igraph) with
a subgraph for each cluster and a global graph with all the genes.
data(hmp_T2D) # grn only on gene cluster.mRNA <- timeOmics::getCluster(hmp_T2D$getCluster.res, user.block = 'RNA') X <- hmp_T2D$raw$RNA grn.res <- get_grn(X = hmp_T2D$raw$RNA, cluster = cluster.mRNA, method = 'aracne')data(hmp_T2D) # grn only on gene cluster.mRNA <- timeOmics::getCluster(hmp_T2D$getCluster.res, user.block = 'RNA') X <- hmp_T2D$raw$RNA grn.res <- get_grn(X = hmp_T2D$raw$RNA, cluster = cluster.mRNA, method = 'aracne')
Compute correlation between two dataframe X and Y (or list of data.frame). An incidence graph is returned. A link between two features is produced if their correlation (absolute value) is above the threshold.
get_interaction_from_correlation(X, Y, threshold = 0.5)get_interaction_from_correlation(X, Y, threshold = 0.5)
X |
a data.frame or list of data.frame (with a similar number of row). |
Y |
a data.frame or list of data.frame (with a similar number of row). |
threshold |
a threshold to cut the correlation matrix above which a link is created between a feature from X and a feature from Y. |
an 'igraph' object
X <- matrix(rexp(200, rate=.1), ncol=20) Y <- matrix(rexp(200, rate=.1), ncol=20) get_interaction_from_correlation(X,Y) X <- list(matrix(rexp(200, rate=.1), ncol=20), matrix(rexp(200, rate=.1), ncol=20)) Y <- matrix(rexp(200, rate=.1), ncol=20) get_interaction_from_correlation(X,Y)X <- matrix(rexp(200, rate=.1), ncol=20) Y <- matrix(rexp(200, rate=.1), ncol=20) get_interaction_from_correlation(X,Y) X <- list(matrix(rexp(200, rate=.1), ncol=20), matrix(rexp(200, rate=.1), ncol=20)) Y <- matrix(rexp(200, rate=.1), ncol=20) get_interaction_from_correlation(X,Y)
Returns an interaction graph from a vector of nodes (or a list of vectors) and an interaction database (data.frame or igraph)
get_interaction_from_database(X, db = NULL, type = "db", user.ego = FALSE)get_interaction_from_database(X, db = NULL, type = "db", user.ego = FALSE)
X |
vector of nodes or list of vectors |
db |
data.frame (with two columns: from, to) or igraph |
type |
character added to node metadata |
user.ego |
logical, if user.ego == TRUE looks for first degree neighbors in db and add 'mode' metadata ('core'/'extended') |
a subset graph of db from X list of nodes
X <- letters[1:4] db <- as.data.frame(list(from = sample(letters[1:10], replace = TRUE), to = sample(letters[1:10], replace = TRUE))) sub <- get_interaction_from_database(X, db) db.graph <- igraph::graph_from_data_frame(db, directed=FALSE) sub <- get_interaction_from_database(X, db)X <- letters[1:4] db <- as.data.frame(list(from = sample(letters[1:10], replace = TRUE), to = sample(letters[1:10], replace = TRUE))) sub <- get_interaction_from_database(X, db) db.graph <- igraph::graph_from_data_frame(db, directed=FALSE) sub <- get_interaction_from_database(X, db)
Returns results of an ORA analysis as an interaction graph
get_interaction_from_ORA( query, sources = "GO", organism = "hsapiens", signif.value = TRUE )get_interaction_from_ORA( query, sources = "GO", organism = "hsapiens", signif.value = TRUE )
query |
a vector (or a list) of character with the ID to perform the ORA analysis |
sources |
(optional) a character in (GO, KEGG, REAC, TF, MIRNA, CORUM, HP, HPA, WP) |
organism |
(optional) a character (default = 'hsapiens') |
signif.value |
(optional) a logical, default = ” |
a graph object (or list of graph) containing the interaction between the query and the target terms.
query <- c('IL15', 'CDHR5', 'TGFA', 'C4B') get_interaction_from_ORA(query, sources = 'GO') query <- list('All' = c('IL15', 'CDHR5', 'TGFA', 'C4B'), 'c1' = c('IL15', 'CDHR5', 'TGFA')) get_interaction_from_ORA(query, sources = 'GO')query <- c('IL15', 'CDHR5', 'TGFA', 'C4B') get_interaction_from_ORA(query, sources = 'GO') query <- list('All' = c('IL15', 'CDHR5', 'TGFA', 'C4B'), 'c1' = c('IL15', 'CDHR5', 'TGFA')) get_interaction_from_ORA(query, sources = 'GO')
Returns results of an ORA analysis
get_ORA(query, sources = NULL, organism = "hsapiens")get_ORA(query, sources = NULL, organism = "hsapiens")
query |
a vector of character, a lit of ID |
sources |
a character or list of character |
organism |
a character (default = 'hsapiens') |
a data.frame containing the enrichment result
This dataset contained a list of data.frames.
Raw data is a subset of the data available at:
https://github.com/aametwally/ipop_seasonal
The package will be illustrated on longitudinal MO dataset to study the
seasonality of MO expression in patients with diabetes
(see netOmics vignette).
In this subset we focused on a single individual with 7 timepoints.
Briefly 6 different omics were sampled (RNA, proteins, cytokines,
gut microbiome, metabolites and clinical variables).
hmp_T2Dhmp_T2D
a list of data.frame
data.frame, raw data
data.frame, modelled data
data.frame, clustering results from timeOmics
data.frame, sparse clustering results from timeOmics
data.frame, interactions from BioGRID database
data.frame, TFome interactions from TTrust and TF2DNA
data.frame, medlineRanker enrichment results
list of igraph, gut graph obtained with SparCC
netOmics is a multi-omics networks builder and explorer. It uses a combination of network inference algorithms and and knowledge-based graphs to build multi-layered networks.
The package can be combined with
timeOmics to incorporate time-course expression data and build
sub-networks from multi-omics kinetic clusters.
Finally, from the generated multi-omics networks, propagation analyses allow the identification of missing biological functions (1), multi-omics mechanisms (2) and molecules between kinetic clusters (3). This helps to resolve complex regulatory mechanisms. Here are the main functions.
get_grnBased on expression matrix, this function build a gene gene regulatory network. Additionally, if clustering information is given, it builds cluster specific graph.
get_interaction_from_databaseFrom a database (graph or data.frame with interactions between 2 molecules), this function build the induced graph based on a list of molecules . Alternatively, the function can build a graph with the first degree neighbors.
get_interaction_from_correlationCompute correlation between two dataframe X and Y (or list of data.frame). An incidence graph is returned. A link between two features is produced if their correlation (absolute value) is above the threshold.
combine_layersCombine 2 (or list of) graphs based on given intersections.
random_walk_restartThis function performs a propagation analysis by random walk with restart in a multi-layered network from specific seeds.
rwr_find_seeds_between_attributesFrom rwr results, this function returns a subgraph if any vertex shares different attributes value. In biological context, this might be useful to identify vertex shared between clusters or omics types.
rwr_find_closest_typeFrom a rwr results, this function returns the closest nodes from a seed with a given attribute and value. In biological context, it might be useful to get the closest Gene Ontology annotation nodes from unannotated seeds.
summary_plot_rwr_attributes#' Based on the results of
rwr_find_seeds_between_attributes which identify the
closest k neighbors from a seed, this function returns a barplot of the node
types (layers) reached for each seed.
plot_rwr_subnetworkDisplay the subgraph from a RWR results. This function colors adds a specific color to each node based on their 'type' attribute. It also adds a legend including the number of vertices/edges and the number of nodes of specific type. Additionally, the function can display any igraph object.
Maintainer: Antoine Bodein [email protected]
Useful links:
Display the subgraph from a RWR results. This function colors adds a specific color to each node based on their 'type' attribute. It also adds a legend including the number of vertices/edges and the number of nodes of specific type. Additionally, the function can display any igraph object.
plot_rwr_subnetwork(X, color = NULL, plot = TRUE, legend = TRUE, ...)plot_rwr_subnetwork(X, color = NULL, plot = TRUE, legend = TRUE, ...)
X |
an igraph object |
color |
(optional) a named character vector or list, list of color to apply to each type |
plot |
logical, if TRUE then the plot is produced |
legend |
(optional) logical, if TRUE then the legend is displayed with number of veretices/edges and the number of nodes of specific type. |
... |
Arguments to be passed to the plot method |
X is returned with additional vertex attributes
graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A")) rwr_res_type <- rwr_find_seeds_between_attributes(X = rwr_res, attribute = "type") plot_rwr_subnetwork(rwr_res_type$A)graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A")) rwr_res_type <- rwr_find_seeds_between_attributes(X = rwr_res, attribute = "type") plot_rwr_subnetwork(rwr_res_type$A)
This function performs a propagation analysis by random walk with restart in a multi-layered network from specific seeds.
random_walk_restart(X, seed = NULL, r = 0.7)random_walk_restart(X, seed = NULL, r = 0.7)
X |
an igraph or list.igraph object. |
seed |
a character vector. Only seeds present in X are considered. |
r |
a numeric value between 0 and 1. It sets the probability of restarting to a seed node after each step. |
Each element of X returns a list (class = 'rwr') containing the following elements:
rwr |
a |
seed |
a character vector with the valid seeds |
graph |
|
If X is a list.igraph, the returned object is a list.rwr.
rwr_find_seeds_between_attributes,
rwr_find_closest_type
graph1 <- igraph::graph_from_data_frame( list(from = c('A', 'B', 'A', 'D', 'C', 'A', 'C'), to = c('B', 'C', 'D', 'E', 'D', 'F', 'G')), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('A','B','C'), value = '1') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('D','E'), value = '2') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('F', 'G'), value = '3') rwr_res <- random_walk_restart(X = graph1, seed = c('A', 'B', 'C', 'D', 'E'))graph1 <- igraph::graph_from_data_frame( list(from = c('A', 'B', 'A', 'D', 'C', 'A', 'C'), to = c('B', 'C', 'D', 'E', 'D', 'F', 'G')), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('A','B','C'), value = '1') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('D','E'), value = '2') graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c('F', 'G'), value = '3') rwr_res <- random_walk_restart(X = graph1, seed = c('A', 'B', 'C', 'D', 'E'))
From a rwr results, this function returns the closest nodes from a seed with a given attribute and value. In biological context, it might be useful to get the closest Gene Ontology annotation nodes from unannotated seeds.
rwr_find_closest_type(X, seed = NULL, attribute = NULL, value = NULL, top = 1)rwr_find_closest_type(X, seed = NULL, attribute = NULL, value = NULL, top = 1)
X |
a random walk result from |
seed |
a character vector or NULL. If NULL, all the seeds from X are considered. |
attribute |
a character value or NULL. If NULL, the closest node is returned. |
value |
a character value or NULL. If NULL, the closest node for a given attribute is returned. |
top |
a numeric value, the top closest nodes to extract |
A list of data.frame for each seed containing the closest nodes per
seed and their vertex attributes.
If X is list.rwr, the returned value is a list of list.
graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A", "B", "C", "D", "E")) rwr_find_closest_type(X=rwr_res, attribute = "type", seed = "A")graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A", "B", "C", "D", "E")) rwr_find_closest_type(X=rwr_res, attribute = "type", seed = "A")
From rwr results, this function returns a subgraph if any vertex shares different attributes value. In biological context, this might be useful to identify vertex shared between clusters or omics types.
rwr_find_seeds_between_attributes(X, seed = NULL, k = 15, attribute = "type")rwr_find_seeds_between_attributes(X, seed = NULL, k = 15, attribute = "type")
X |
a random walk result from |
seed |
a character vector or NULL. If NULL, all the seeds from X are considered. |
k |
a integer, k closest nodes to consider in the search |
attribute |
a character value or NULL. If NULL, the closest node is returned. |
A list of igraph object for each seed. If X is a list, it returns a list of list of graph.
graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A", "B", "C", "D", "E")) rwr_res_type <- rwr_find_seeds_between_attributes(X = rwr_res, attribute = "type", k = 3)graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A", "B", "C", "D", "E")) rwr_res_type <- rwr_find_seeds_between_attributes(X = rwr_res, attribute = "type", k = 3)
Based on the results of
rwr_find_seeds_between_attributes which identify the
closest k neighbors from a seed, this function returns a barplot of the node
types (layers) reached for each seed.
summary_plot_rwr_attributes( X, color = NULL, seed.id = NULL, seed.type = NULL, plot = TRUE )summary_plot_rwr_attributes( X, color = NULL, seed.id = NULL, seed.type = NULL, plot = TRUE )
X |
a 'rwr.attributes' or 'list.rwr.attributes' object from rwr_find_seeds_between_attributes() |
color |
(optional) a named character vector or list, list of color to apply to each type |
seed.id |
(optional) a character vector, to filter the results and filter on specific seeds IDs |
seed.type |
(optional) a character vector, to filter the results and filter on specific seeds types |
plot |
logical, if TRUE then the plot is produced |
a 'ggplot' object
random_walk_restart,
rwr_find_seeds_between_attributes
graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A", "B", "C", "D", "E")) rwr_res_type <- rwr_find_seeds_between_attributes(X = rwr_res, attribute = "type", k = 3) summary_plot_rwr_attributes(rwr_res_type)graph1 <- igraph::graph_from_data_frame( list(from = c("A", "B", "A", "D", "C", "A", "C"), to = c("B", "C", "D", "E", "D", "F", "G")), directed = FALSE) graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("A","B","C"), value = "1") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("D","E"), value = "2") graph1 <- igraph::set_vertex_attr(graph = graph1, name = 'type', index = c("F", "G"), value = "3") rwr_res <- random_walk_restart(X = graph1, seed = c("A", "B", "C", "D", "E")) rwr_res_type <- rwr_find_seeds_between_attributes(X = rwr_res, attribute = "type", k = 3) summary_plot_rwr_attributes(rwr_res_type)