Snippets

Dénes Türei Combining the SARS-CoV2-human interactions from Gordon et al. 2020 with OmniPath

Updated by Dénes Türei
View revision

File sarscov2_omnipath.r Modified

  • Ignore whitespace
  • Hide word diff 
 
 # Copyright Saez Lab 2020
 #
-# Author: Denes Turei
+# Authors: Denes Turei and Alberto Valdeolivas
 # Contact: turei.denes@gmail.com
 #
 # License: MIT License https://opensource.org/licenses/MIT
 require(openxlsx)
 require(igraph)
 require(qgraph)
+require(tidyr)
 
 if(!'OmnipathR' %in% installed.packages()[,"Package"]){
     require(devtools)
 
 # URLs for supplementary tables S2 and S3 from Gordon et al. 2020
 # https://www.biorxiv.org/content/10.1101/2020.03.22.002386v2
-url_s2 <- paste0(
-    'https://www.biorxiv.org/content/biorxiv/early/',
-    '2020/03/23/2020.03.22.002386/DC4/embed/media-4.xlsx?download=true'
-)
-url_s3 <- paste0(
-    'https://www.biorxiv.org/content/biorxiv/early/',
-    '2020/03/23/2020.03.22.002386/DC5/embed/media-5.xlsx?download=true'
-)
+url_s2 <-
+    paste0(
+        'https://www.biorxiv.org/content/biorxiv/early/',
+        '2020/03/23/2020.03.22.002386/DC4/embed/media-4.xlsx?download=true'
+    )
+url_s3 <-
+    paste0(
+        'https://www.biorxiv.org/content/biorxiv/early/',
+        '2020/03/23/2020.03.22.002386/DC5/embed/media-5.xlsx?download=true'
+    )
 
 # the OmniPath PPI interaction network
 ia_omnipath <- import_Omnipath_Interactions() %>% as_tibble()
 human_sarscov2_compounds_raw <- read.xlsx(url_s3) %>% as_tibble()
 
 # combining all the interactions to one data frame
-ia_human_sarscov2 <- human_sarscov2_raw %>%
+ia_human_sarscov2 <-
+    human_sarscov2_raw %>%
     select(
         source = Bait,
         target = Preys,
         target_genesymbol = 'BSG'
     )
 
-interactions <- as_tibble(
-    bind_rows(
-        ia_omnipath %>% mutate(type = 'ppi'),
-        ia_pwextra %>% mutate(type = 'ppi'),
-        ia_kinaseextra %>% mutate(type = 'ppi'),
-        ia_ligrec %>% mutate(type = 'ppi'),
-        ia_transcriptional %>% mutate(type = 'transcriptional'),
-        ia_human_sarscov2 %>% mutate(
-            source_genesymbol = sub('SARS-CoV2 ', '', source_genesymbol),
-            type = 'host_pathogen',
-            # the direction or effect of these interactions is unknown
-            is_directed = 0,
-            is_stimulation = 0,
-            is_inhibition = 0,
-            consensus_direction = 0,
-            consensus_stimulation = 0,
-            consensus_inhibition = 0,
+interactions <-
+    as_tibble(
+        bind_rows(
+            ia_omnipath %>% mutate(type = 'ppi'),
+            ia_pwextra %>% mutate(type = 'ppi'),
+            ia_kinaseextra %>% mutate(type = 'ppi'),
+            ia_ligrec %>% mutate(type = 'ppi'),
+            ia_transcriptional %>% mutate(type = 'transcriptional'),
+            ia_human_sarscov2 %>% mutate(
+                source_genesymbol = sub('SARS-CoV2 ', '', source_genesymbol),
+                type = 'host_pathogen',
+                # the direction or effect of these interactions is unknown
+                is_directed = 0,
+                is_stimulation = 0,
+                is_inhibition = 0,
+                consensus_direction = 0,
+                consensus_stimulation = 0,
+                consensus_inhibition = 0
+            )
         )
-    )
-)
+    ) %>%
+    group_by(
+        source_genesymbol,
+        target_genesymbol,
+        type,
+        is_directed,
+        is_inhibition,
+        is_stimulation
+    ) %>%
+    summarize_all(first) %>%
+    ungroup() %>%
+    mutate(
+        curation_effort = ifelse(
+            type == 'host_pathogen',
+            # for our purpose these interactions are of high importance
+            # hence we assign a high "weight"
+            1000,
+            ifelse(
+                is.na(curation_effort),
+                # no curation effort means no literature evidence available
+                # here we assign a small weight < 1
+                .5,
+                curation_effort
+            )
+        )
+    ) %>%
+    # remove any ambiguity between gene symbols and uniprot ids
+    group_by(source) %>%
+    mutate(source_genesymbol = first(source_genesymbol)) %>%
+    ungroup() %>%
+    group_by(target) %>%
+    mutate(target_genesymbol = first(target_genesymbol)) %>%
+    ungroup()
+
+# Obtaining pneumocyte protein expression data from the Human Protein Atlas
+# using the annotations database of OmniPath
+# From the annotations database you can access a broad variety of data about
+# proteins: function, localization, structure, expression, etc, overall
+# from more than 40 resources. See a table of resources, properties and
+# values at http://omnipathdb.org/annotations_summary or use the
+# `get_annotation_databases` method from the OmnipathR package.
+human_proteins <-
+    c(
+        interactions %>% filter(type != 'host_pathogen') %>% pull(source),
+        interactions %>% pull(target)
+    ) %>%
+    unique()
+
+pneumocyte_expression <-
+    import_Omnipath_annotations(
+        filter_databases = c('HPA_tissue'),
+        select_genes = human_proteins
+    ) %>%
+    spread(label, value) %>%
+    as_tibble() %>%
+    filter(organ == 'lung', tissue == 'pneumocytes')
 
 # creating an igraph network from the interactions data frame
-net_human_sarscov2 <- interaction_graph(interactions = interactions) %>%
-    simplify(remove.multiple = FALSE, remove.loops = TRUE)
+net_human_sarscov2 <-
+    interaction_graph(interactions = interactions) %>%
+    igraph::simplify(remove.multiple = FALSE, remove.loops = TRUE)
 
 # labeling the proteins by organism
 sarscov2_proteins <- ia_human_sarscov2 %>% pull(source) %>% unique()
 
-V(net_human_sarscov2)$organism <- ifelse(
-    V(net_human_sarscov2)$up_ids %in% sarscov2_proteins,
-    'SARS-CoV2',
-    'human'
-)
+V(net_human_sarscov2)$organism <-
+    ifelse(
+        V(net_human_sarscov2)$up_ids %in% sarscov2_proteins,
+        'SARS-CoV2',
+        'human'
+    )
 
 # labeling direct viral targets and transcription factors
-V(net_human_sarscov2)$viral_target <- (
+V(net_human_sarscov2)$viral_target <-
     V(net_human_sarscov2)$up_ids %in% (
         ia_human_sarscov2 %>% pull(target) %>% unique()
     )
-)
 
-V(net_human_sarscov2)$tf <- (
+V(net_human_sarscov2)$tf <-
     V(net_human_sarscov2)$up_ids %in% (
         ia_transcriptional %>% pull(source) %>% unique()
     )
-)
 
 # node indices of the SARS-CoV2 proteins
-nodes_sarscov2 <- which(V(net_human_sarscov2)$organism == 'SARS-CoV2')
+nodes_sarscov2 <- (V(net_human_sarscov2)$organism == 'SARS-CoV2') %>% which()
 
+# adding pneumocyte expression levels as a vertex attribute
+expression_by_protein <-setNames(
+    pneumocyte_expression %>% pull(level),
+    pneumocyte_expression %>% pull(uniprot)
+)
+
+V(net_human_sarscov2)$expression <-
+    V(net_human_sarscov2)$up_ids %>%
+    map(
+        function(uniprot){
+            `if`(
+                uniprot %in% names(expression_by_protein),
+                expression_by_protein[[uniprot]],
+                'Not available'
+            )
+        }
+    ) %>%
+    unlist()
 
 # creating subgraphs of the neighborhood of each virus protein
-neighborhoods_human_sarscov2_1 <- make_ego_graph(
-    net_human_sarscov2,
-    order = 1,
-    nodes = nodes_sarscov2,
-    mode = 'all'
-)
+neighborhoods_human_sarscov2_1 <-
+    make_ego_graph(
+        net_human_sarscov2,
+        order = 1,
+        nodes = nodes_sarscov2,
+        mode = 'all'
+    )
 
-nodes_in_neighborhoods <- neighborhoods_human_sarscov2_1 %>%
+nodes_in_neighborhoods <-
+    neighborhoods_human_sarscov2_1 %>%
     map(function(g){V(g)$name}) %>%
     unlist() %>%
     unique()
 
 # reducing the network to the neighborhoods
-net_human_sarscov2_1 <- net_human_sarscov2 %>%
+net_human_sarscov2_1 <-
+    net_human_sarscov2 %>%
     delete_vertices(
         which(
             !V(net_human_sarscov2)$name %in% nodes_in_neighborhoods
         )
     )
 
-net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
+# reducing the network to the viral targets having PPI among
+# themselves
+net_human_sarscov2_1 <-
+    net_human_sarscov2_1 %>%
     delete_vertices(
         which(!(
             V(net_human_sarscov2_1)$viral_target |
         ))
     )
 
-nodes_having_ppi <- net_human_sarscov2_1 %>%
+nodes_having_ppi <-
+    net_human_sarscov2_1 %>%
     get.edges(
         E(net_human_sarscov2_1)[
             E(net_human_sarscov2_1)$type %in% c('ppi', 'transcriptional')
     c() %>%
     unique()
 
-sarscov2_nodes <- which(V(net_human_sarscov2_1)$organism == 'SARS-CoV2')
+sarscov2_nodes <-
+    (V(net_human_sarscov2_1)$organism == 'SARS-CoV2') %>%
+    which()
 
 nodes_to_keep <- c(nodes_having_ppi, sarscov2_nodes) %>% unique()
 
-net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
+net_human_sarscov2_1 <-
+    net_human_sarscov2_1 %>%
     induced_subgraph(nodes_to_keep)
 
-net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
+net_human_sarscov2_1 <-
+    net_human_sarscov2_1 %>%
     delete_vertices(degree(.) == 0)
 
 # creating a figure of the direct neighborhood network
-fr_layout <- qgraph.layout.fruchtermanreingold(
-    get.edgelist(net_human_sarscov2_1, names = FALSE),
-    vcount = vcount(net_human_sarscov2_1),
-    area = vcount(net_human_sarscov2_1) ** 2.3,
-    repulse.rad = vcount(net_human_sarscov2_1) ** 2.1,
-    niter = 3000
-)
+fr_layout <-
+    net_human_sarscov2_1 %>%
+    get.edgelist(names = FALSE) %>%
+    qgraph.layout.fruchtermanreingold(
+        vcount = vcount(net_human_sarscov2_1),
+        area = vcount(net_human_sarscov2_1) ** 2.3,
+        repulse.rad = vcount(net_human_sarscov2_1) ** 2.1,
+        niter = 500
+    )
 
 png(
     filename = 'SARS-CoV2_OmniPath_neighborhood.png',
     res = 600
 )
 
+# igraph is not able to plot arrows with and without head at the same time
+# plotting first only the undirected edges, then the directed
+plot(
+    net_human_sarscov2_1,
+    layout = fr_layout,
+    vertex.size = 0,
+    vertex.label = NA,
+    edge.arrow.size = 0,
+    edge.width = .2,
+    edge.color = ifelse(
+        E(net_human_sarscov2_1)$is_directed,
+        NA,
+        '#646567AA'
+    )
+)
+
 plot(
     net_human_sarscov2_1,
     layout = fr_layout,
     vertex.size = 5,
-    vertex.label.cex = .33,
+    vertex.label.cex = .66,
+    vertex.label.dist = 1,
+    vertex.label.degree = -pi * .75,
     vertex.color = ifelse(
         V(net_human_sarscov2_1)$organism == 'SARS-CoV2',
         '#FCCC06',
             ifelse(
                 E(net_human_sarscov2_1)$is_stimulation,
                 '#49969AAA',
-                '#646567AA'
+                ifelse(
+                    E(this_community)$is_directed,
+                    '#646567AA',
+                    NA
+                )
             )
         )
     ),
-    edge.arrow.size = ifelse(
+    edge.arrow.size = .2,
+    edge.width = .2,
+    add = TRUE
+)
+
+dev.off()
+
+# creating the same plot with showing the pneumocyte expression levels
+png(
+    filename = 'SARS-CoV2_OmniPath_neighborhood_pneumocytes.png',
+    width = 7,
+    height = 7,
+    units = 'in',
+    res = 600
+)
+
+plot(
+    net_human_sarscov2_1,
+    layout = fr_layout,
+    vertex.size = 0,
+    vertex.label = NA,
+    edge.arrow.size = 0,
+    edge.width = .2,
+    edge.color = ifelse(
         E(net_human_sarscov2_1)$is_directed,
-        .2,
-        .0
+        NA,
+        '#646567AA'
+    )
+)
+
+plot(
+    net_human_sarscov2_1,
+    layout = fr_layout,
+    vertex.size = 5,
+    vertex.label.cex = .66,
+    vertex.label.dist = 1,
+    vertex.label.degree = -pi * .75,
+    vertex.color = ifelse(
+        V(net_human_sarscov2_1)$organism == 'SARS-CoV2',
+        '#FCCC06',
+        ifelse(
+            V(net_human_sarscov2_1)$expression == 'High',
+            '#ff1414',
+            ifelse(
+                V(net_human_sarscov2_1)$expression == 'Medium',
+                '#ff7676',
+                ifelse(
+                    V(net_human_sarscov2_1)$expression == 'Low',
+                    '#ffc4c4',
+                    'whitesmoke'
+                )
+            )
+        )
+    ),
+    vertex.label.family = 'DINPro',
+    vertex.label.color = '#454447',
+    vertex.frame.width = 0,
+    vertex.frame.color = NA,
+    edge.color = ifelse(
+        E(net_human_sarscov2_1)$type == 'host_pathogen',
+        '#FCCC06',
+        ifelse(
+            E(net_human_sarscov2_1)$is_inhibition,
+            '#E25C49AA',
+            ifelse(
+                E(net_human_sarscov2_1)$is_stimulation,
+                '#49969AAA',
+                ifelse(
+                    E(this_community)$is_directed,
+                    '#646567AA',
+                    NA
+                )
+            )
+        )
     ),
-#     edge.lty = ifelse(
-#         E(net_human_sarscov2_1)$type == 'transcriptional',
-#         'dashed',
-#         ifelse(
-#             E(net_human_sarscov2_1)$type == 'host_pathogen',
-#             'dotted',
-#             'solid'
-#         )
-#     ),
-    edge.width = .2
+    edge.arrow.size = .2,
+    edge.width = .2,
+    add = TRUE
 )
 
+dev.off()
+
+
+# creating a function for recursive clustering, i.e. to call the methods
+# repeatedly until also large clusters are chopped into pieces of the
+# desired maximum size
+cluster_recursive <- function(
+        graph,
+        membership = NULL,
+        max_size = 100,
+        method = cluster_louvain,
+        verbose = FALSE,
+        ...
+    ){
+    
+    if(is.null(membership)){
+        
+        membership <- rep('1', vcount(graph))
+        
+    }
+    
+    cl_sizes <- table(membership)
+    
+    for(cl_idx in names(cl_sizes)[which(cl_sizes > max_size)]){
+        
+        this_cluster_idx <- which(membership == cl_idx)
+        
+        if(verbose){
+            message(
+                sprintf(
+                    'Cluster %s has a size of %d, clustering further.',
+                    cl_idx,
+                    length(this_cluster_idx)
+                )
+            )
+        }
+        
+        this_cluster_graph <-
+            graph %>%
+            induced_subgraph(this_cluster_idx)
+        
+        subclusters <- method(this_cluster_graph, ...)
+        
+        membership[this_cluster_idx] <-
+            subclusters$membership %>%
+            map(function(m){sprintf('%s.%d', cl_idx, m)}) %>%
+            unlist()
+        
+    }
+    
+    if(max(table(membership)) <= max_size){
+        
+        return(membership)
+        
+    }else{
+        
+        return(
+            cluster_recursive(
+                graph,
+                membership,
+                max_size = max_size,
+                method = method,
+                ...
+            )
+        )
+        
+    }
+    
+}
+
+
+# using curation effort as weight for a better clustering
+E(net_human_sarscov2)$weight <- E(net_human_sarscov2)$curation_effort
+
+# some clustering methods work only with undirected and simple graphs
+net_human_sarscov2_undir <-
+    net_human_sarscov2 %>%
+    as.undirected() %>%
+    simplify()
+
+# clustering the network by the louvain algorithm
+human_sarscov2_louvain <-
+    net_human_sarscov2_undir %>%
+    cluster_recursive(method = cluster_louvain, max_size = 150)
+
+names(human_sarscov2_louvain) <- V(net_human_sarscov2_undir)$name
+
+# creating a vertex attribute from cluster memberships
+V(net_human_sarscov2)$cluster <-
+    V(net_human_sarscov2)$name %>%
+    map(
+        function(name){
+            `if`(
+                name %in% names(human_sarscov2_louvain),
+                human_sarscov2_louvain[[name]],
+                NA
+            )
+        }
+    ) %>%
+    unlist()
+
+sarscov2_proteins <- interactions %>%
+    filter(type == 'host_pathogen') %>%
+    pull(source) %>%
+    unique()
+
+human_target_proteins <- interactions %>%
+    filter(type == 'host_pathogen') %>%
+    pull(target) %>%
+    unique()
+
+# shortcut for the vertex sequence of the large network
+vs <- V(net_human_sarscov2)
+
+# plotting each cluster containing viral targets
+dir.create('clusters', showWarnings = FALSE)
+list.files('clusters') %>%
+walk(function(f){file.remove(file.path('clusters', f))})
+
+cairo_pdf(
+    'SARS-CoV2_OmniPath_clusters_pneumocytes.pdf',
+    width = 7,
+    height = 7,
+    onefile = TRUE
+)
+
+for(cl_id in unique(vs$cluster)){
+    
+    has_target_proteins <-
+        vs$up_ids[which(vs$cluster == cl_id)] %>%
+        intersect(human_target_proteins) %>%
+        length() %>%
+        `>`(0)
+    
+    if(has_target_proteins){
+        
+        this_community <-
+            net_human_sarscov2 %>%
+            induced_subgraph(
+                which(
+                    # the members of the cluster and all virus proteins
+                    vs$cluster == cl_id |
+                    vs$up_ids %in% sarscov2_proteins
+                )
+            )
+        
+        # removing nodes with no connections
+        this_community <-
+            this_community %>%
+            delete_vertices(which(degree(this_community) == 0))
+        
+        if(vcount(this_community) == 0) next
+        
+        fr_layout <-
+            this_community %>%
+            get.edgelist(names = FALSE) %>%
+            qgraph.layout.fruchtermanreingold(
+                vcount = vcount(this_community),
+                area = vcount(this_community) ** 2.3,
+                repulse.rad = vcount(this_community) ** 2.1,
+                niter = 500
+            )
+        
+        pngpath <- file.path(
+            'clusters',
+            sprintf(
+                'SARS-CoV2_OmniPath_cluster-%s_pneumocytes.png',
+                cl_id
+            )
+        )
+        
+        message(
+            sprintf(
+                'Community %s with %d members and %d connections',
+                cl_id,
+                vcount(this_community),
+                ecount(this_community)
+            )
+        )
+        
+        # in case you want png
+        # png(
+        #     filename = pngpath,
+        #     width = 7,
+        #     height = 7,
+        #     units = 'in',
+        #     res = 600
+        # )
+        
+        plot(
+            this_community,
+            layout = fr_layout,
+            vertex.size = 0,
+            vertex.label = NA,
+            edge.arrow.size = 0,
+            edge.width = .2,
+            edge.color = ifelse(
+                E(this_community)$is_directed,
+                NA,
+                '#646567AA'
+            )
+        )
+
+        plot(
+            this_community,
+            layout = fr_layout,
+            vertex.size = 5,
+            vertex.label.cex = .66,
+            vertex.label.dist = 1,
+            vertex.label.degree = -pi * .75,
+            vertex.color = ifelse(
+                V(this_community)$organism == 'SARS-CoV2',
+                '#FCCC06',
+                ifelse(
+                    V(this_community)$expression == 'High',
+                    '#ff1414',
+                    ifelse(
+                        V(this_community)$expression == 'Medium',
+                        '#ff7676',
+                        ifelse(
+                            V(this_community)$expression == 'Low',
+                            '#ffc4c4',
+                            'whitesmoke'
+                        )
+                    )
+                )
+            ),
+            vertex.label.family = 'DINPro',
+            vertex.label.color = '#454447',
+            vertex.frame.width = 0,
+            vertex.frame.color = NA,
+            edge.color = ifelse(
+                E(this_community)$type == 'host_pathogen',
+                '#FCCC06',
+                ifelse(
+                    E(this_community)$is_inhibition,
+                    '#E25C49AA',
+                    ifelse(
+                        E(this_community)$is_stimulation,
+                        '#49969AAA',
+                        ifelse(
+                            E(this_community)$is_directed,
+                            '#646567AA',
+                            NA
+                        )
+                    )
+                )
+            ),
+            edge.arrow.size = .2,
+            edge.width = .2,
+            add = TRUE
+        )
+
+        #dev.off()
+        
+    }
+    
+}
+
 dev.off()
Updated by Dénes Türei
View revision

File sarscov2_omnipath.r Modified

  • Ignore whitespace
  • Hide word diff 
 net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
     induced_subgraph(nodes_to_keep)
 
+net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
+    delete_vertices(degree(.) == 0)
+
 # creating a figure of the direct neighborhood network
 fr_layout <- qgraph.layout.fruchtermanreingold(
     get.edgelist(net_human_sarscov2_1, names = FALSE),
Updated by Dénes Türei
View revision

File sarscov2_omnipath.r Modified

  • Ignore whitespace
  • Hide word diff 
         ))
     )
 
+nodes_having_ppi <- net_human_sarscov2_1 %>%
+    get.edges(
+        E(net_human_sarscov2_1)[
+            E(net_human_sarscov2_1)$type %in% c('ppi', 'transcriptional')
+        ]
+    ) %>%
+    c() %>%
+    unique()
+
+sarscov2_nodes <- which(V(net_human_sarscov2_1)$organism == 'SARS-CoV2')
+
+nodes_to_keep <- c(nodes_having_ppi, sarscov2_nodes) %>% unique()
+
+net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
+    induced_subgraph(nodes_to_keep)
+
 # creating a figure of the direct neighborhood network
 fr_layout <- qgraph.layout.fruchtermanreingold(
     get.edgelist(net_human_sarscov2_1, names = FALSE),
Created by Dénes Türei
View revision

File sarscov2_omnipath.r Added

  • Ignore whitespace
  • Hide word diff 
+#!/usr/bin/env Rscript
+
+# Copyright Saez Lab 2020
+#
+# Author: Denes Turei
+# Contact: turei.denes@gmail.com
+#
+# License: MIT License https://opensource.org/licenses/MIT
+#
+# Combines the OmniPath PPI and transcriptional regulatory network
+# with the human-SARS CoV-2 interactions from Gordon et al. 2020
+
+require(dplyr)
+require(tibble)
+require(purrr)
+require(openxlsx)
+require(igraph)
+require(qgraph)
+
+if(!'OmnipathR' %in% installed.packages()[,"Package"]){
+    require(devtools)
+    install_github('saezlab/OmnipathR')
+}
+
+require(OmnipathR)
+
+# URLs for supplementary tables S2 and S3 from Gordon et al. 2020
+# https://www.biorxiv.org/content/10.1101/2020.03.22.002386v2
+url_s2 <- paste0(
+    'https://www.biorxiv.org/content/biorxiv/early/',
+    '2020/03/23/2020.03.22.002386/DC4/embed/media-4.xlsx?download=true'
+)
+url_s3 <- paste0(
+    'https://www.biorxiv.org/content/biorxiv/early/',
+    '2020/03/23/2020.03.22.002386/DC5/embed/media-5.xlsx?download=true'
+)
+
+# the OmniPath PPI interaction network
+ia_omnipath <- import_Omnipath_Interactions() %>% as_tibble()
+
+# in the example we don't use these but if you need higher coverage on PPI
+# add these with `bind_rows` below
+ia_ligrec <- import_LigrecExtra_Interactions() %>% as_tibble()
+ia_pwextra <- import_PathwayExtra_Interactions() %>% as_tibble()
+ia_kinaseextra <- import_KinaseExtra_Interactions() %>% as_tibble()
+
+# transcriptional regulation
+# if you want more interactions, pass the argument
+# `confidence_level = c('A', 'B', 'C', 'D')`
+ia_transcriptional <- import_TFregulons_Interactions() %>% as_tibble()
+
+# post-transcriptional regulation
+# here we don't use it but if you are interested
+# add these by `bind_rows` below
+ia_post_transcriptional <- import_miRNAtarget_Interactions() %>% as_tibble()
+
+# downloading the host-pathogen interactions from the paper
+human_sarscov2_raw <- read.xlsx(url_s2, startRow = 2) %>% as_tibble()
+# downloading the compound-host-pathogen interactions from the paper
+# here we don't use it but if you are interested combine with the other
+# interactions a similar way as the host-pathogen ones
+human_sarscov2_compounds_raw <- read.xlsx(url_s3) %>% as_tibble()
+
+# combining all the interactions to one data frame
+ia_human_sarscov2 <- human_sarscov2_raw %>%
+    select(
+        source = Bait,
+        target = Preys,
+        source_genesymbol = Bait,
+        target_genesymbol = PreyGene,
+        MIST,
+        Saint_BFDR,
+        AvgSpec,
+        FoldChange
+    ) %>%
+    # manually adding the known S--ACE2 and S--BSG interactions
+    add_row(
+        source = 'SARS-CoV2 Spike',
+        target = 'Q9BYF1',
+        source_genesymbol = 'Spike',
+        target_genesymbol = 'ACE2'
+    ) %>%
+    add_row(
+        source = 'SARS-CoV2 Spike',
+        target = 'P35613',
+        source_genesymbol = 'Spike',
+        target_genesymbol = 'BSG'
+    )
+
+interactions <- as_tibble(
+    bind_rows(
+        ia_omnipath %>% mutate(type = 'ppi'),
+        ia_pwextra %>% mutate(type = 'ppi'),
+        ia_kinaseextra %>% mutate(type = 'ppi'),
+        ia_ligrec %>% mutate(type = 'ppi'),
+        ia_transcriptional %>% mutate(type = 'transcriptional'),
+        ia_human_sarscov2 %>% mutate(
+            source_genesymbol = sub('SARS-CoV2 ', '', source_genesymbol),
+            type = 'host_pathogen',
+            # the direction or effect of these interactions is unknown
+            is_directed = 0,
+            is_stimulation = 0,
+            is_inhibition = 0,
+            consensus_direction = 0,
+            consensus_stimulation = 0,
+            consensus_inhibition = 0,
+        )
+    )
+)
+
+# creating an igraph network from the interactions data frame
+net_human_sarscov2 <- interaction_graph(interactions = interactions) %>%
+    simplify(remove.multiple = FALSE, remove.loops = TRUE)
+
+# labeling the proteins by organism
+sarscov2_proteins <- ia_human_sarscov2 %>% pull(source) %>% unique()
+
+V(net_human_sarscov2)$organism <- ifelse(
+    V(net_human_sarscov2)$up_ids %in% sarscov2_proteins,
+    'SARS-CoV2',
+    'human'
+)
+
+# labeling direct viral targets and transcription factors
+V(net_human_sarscov2)$viral_target <- (
+    V(net_human_sarscov2)$up_ids %in% (
+        ia_human_sarscov2 %>% pull(target) %>% unique()
+    )
+)
+
+V(net_human_sarscov2)$tf <- (
+    V(net_human_sarscov2)$up_ids %in% (
+        ia_transcriptional %>% pull(source) %>% unique()
+    )
+)
+
+# node indices of the SARS-CoV2 proteins
+nodes_sarscov2 <- which(V(net_human_sarscov2)$organism == 'SARS-CoV2')
+
+
+# creating subgraphs of the neighborhood of each virus protein
+neighborhoods_human_sarscov2_1 <- make_ego_graph(
+    net_human_sarscov2,
+    order = 1,
+    nodes = nodes_sarscov2,
+    mode = 'all'
+)
+
+nodes_in_neighborhoods <- neighborhoods_human_sarscov2_1 %>%
+    map(function(g){V(g)$name}) %>%
+    unlist() %>%
+    unique()
+
+# reducing the network to the neighborhoods
+net_human_sarscov2_1 <- net_human_sarscov2 %>%
+    delete_vertices(
+        which(
+            !V(net_human_sarscov2)$name %in% nodes_in_neighborhoods
+        )
+    )
+
+net_human_sarscov2_1 <- net_human_sarscov2_1 %>%
+    delete_vertices(
+        which(!(
+            V(net_human_sarscov2_1)$viral_target |
+            V(net_human_sarscov2_1)$organism == 'SARS-CoV2'
+        ))
+    )
+
+# creating a figure of the direct neighborhood network
+fr_layout <- qgraph.layout.fruchtermanreingold(
+    get.edgelist(net_human_sarscov2_1, names = FALSE),
+    vcount = vcount(net_human_sarscov2_1),
+    area = vcount(net_human_sarscov2_1) ** 2.3,
+    repulse.rad = vcount(net_human_sarscov2_1) ** 2.1,
+    niter = 3000
+)
+
+png(
+    filename = 'SARS-CoV2_OmniPath_neighborhood.png',
+    width = 7,
+    height = 7,
+    units = 'in',
+    res = 600
+)
+
+plot(
+    net_human_sarscov2_1,
+    layout = fr_layout,
+    vertex.size = 5,
+    vertex.label.cex = .33,
+    vertex.color = ifelse(
+        V(net_human_sarscov2_1)$organism == 'SARS-CoV2',
+        '#FCCC06',
+        ifelse(
+            V(net_human_sarscov2_1)$viral_target,
+            '#97BE73',
+            '#B6B7B9'
+        )
+    ),
+    vertex.label.family = 'DINPro',
+    vertex.label.color = '#454447',
+    vertex.frame.width = 0,
+    vertex.frame.color = NA,
+    edge.color = ifelse(
+        E(net_human_sarscov2_1)$type == 'host_pathogen',
+        '#FCCC06',
+        ifelse(
+            E(net_human_sarscov2_1)$is_inhibition,
+            '#E25C49AA',
+            ifelse(
+                E(net_human_sarscov2_1)$is_stimulation,
+                '#49969AAA',
+                '#646567AA'
+            )
+        )
+    ),
+    edge.arrow.size = ifelse(
+        E(net_human_sarscov2_1)$is_directed,
+        .2,
+        .0
+    ),
+#     edge.lty = ifelse(
+#         E(net_human_sarscov2_1)$type == 'transcriptional',
+#         'dashed',
+#         ifelse(
+#             E(net_human_sarscov2_1)$type == 'host_pathogen',
+#             'dotted',
+#             'solid'
+#         )
+#     ),
+    edge.width = .2
+)
+
+dev.off()
HTTPS SSH

You can clone a snippet to your computer for local editing. Learn more.