Abstract
Thewppi package calculates context specific scores for genes in the network neighborhood of genes of interest. The context specificity is ensured by the selection of the genes of interest and potentially by using a more relevant subset of the ontology annotations, e.g. selecting only the diabetes related categories. The PPI network and the functional annotations are obtained automatically from public databases, though it’s possible to use custom databases. The network is limited to a user defined neighborhood of the genes of interest. The ontology annotations are also filtered to the genes in this subnetwork. Then the adjacency matrix is weighted according to the number of common neighbors and the similarity in functional annotations of each pair of interacting proteins. On this weighted adjacency matrix a random walk with returns is performed. The final score for the genes in the neighborhood is the sum of their scores (probabilities to be visited) in the random walk. The method can be fine tuned by setting the neighborhood range, the restart probability of the random walk and the threshold for the random walk.
The wppi package depends on the OmnipathR package. Since it relies on features more recent than the latest Bioconductor version (OmnipathR 2.0.0 in Bioconductor 3.12), until the release of Bioconductor 3.13, it is recommended to install OmnipathR from git.
The score_candidate_genes_from_PPI function executes the full wppi workflow. The only mandatory input is a set of genes of interest. As a return, an ordered table with the similarity scores of the new genes within the neighbourhood of the genes of interest is provided. A higher score stands for a higher functional similarity between this new gene and the given ones.
library(wppi)
# example gene set
genes_interest <- c(
'ERCC8', 'AKT3', 'NOL3', 'TTK',
'GFI1B', 'CDC25A', 'TPX2', 'SHE'
)
scores <- score_candidate_genes_from_PPI(genes_interest)## as(<dgCMatrix>, "dgTMatrix") is deprecated since Matrix 1.5-0; do as(., "TsparseMatrix") insteadscores
# # A tibble: 295 x 3
# score gene_symbol uniprot
# <dbl> <chr> <chr>
# 1 0.247 KNL1 Q8NG31
# 2 0.247 HTRA2 O43464
# 3 0.247 KAT6A Q92794
# 4 0.247 BABAM1 Q9NWV8
# 5 0.247 SKI P12755
# 6 0.247 FOXA2 Q9Y261
# 7 0.247 CLK2 P49760
# 8 0.247 HNRNPA1 P09651
# 9 0.247 HK1 P19367
# 10 0.180 SH3RF1 Q7Z6J0
# # . with 285 more rowsThe database knowledge is provided by wppi_data. By default all directed protein-protein interactions are used from OmniPath. By passing various options the network can be customized. See more details in the documentation of the OmnipathR package, especially the import_post_translational_interactions function. For example, to use only the literature curated interactions one can use the datasets = 'omnipath' parameter:
omnipath_data <- wppi_omnipath_data(datasets = 'omnipath')The wppi_data function retrieves all database data at once. Parameters to customize the network can be passed directly to this function.
db <- wppi_data(datasets = c('omnipath', 'kinaseextra'))
names(db)
# [1] "hpo" "go" "omnipath" "uniprot"Optionally, the Human Phenotype Ontology (HPO) annotations relevant in the context can be selected. For example, to select the annotations related to diabetes:
# example HPO annotations set
HPO_data <- wppi_hpo_data()
HPO_interest <- unique(dplyr::filter(HPO_data, grepl('Diabetes', Name))$Name)To work further with the interactions we first convert it to an igraph graph object:
graph_op <- graph_from_op(db$omnipath)Then we select a subgraph around the genes of interest. The size of the subgraph is determined by the range of this neighborhood (sub_level argument for the subgraph_op function).
graph_op_1 <- subgraph_op(graph_op, genes_interest)
igraph::vcount(graph_op_1)
# [1] 256The next step is to assign weights to each interaction. The weights are calculated based on the number of common neighbors and the similarities of the annotations of the interacting partners.
w_adj <- weighted_adj(graph_op_1, db$go, db$hpo)The random walk with restarts algorithm uses the edge weights to score the overall connections between pairs of genes. The result takes into accound also the indirect connections, integrating the information in the graph topology.
w_rw <- random_walk(w_adj)At the end we can summarize the scores for each protein, taking the sum of all adjacent connections. The resulted table provides us a list of proteins prioritized by their predicted importance in the context of interest (disease or condition).
scores <- prioritization_genes(graph_op_1, w_rw, genes_interest)
scores# # A tibble: 249 x 3
# score gene_symbol uniprot
# <dbl> <chr> <chr>
# 1 0.251 HTRA2 O43464
# 2 0.251 KAT6A Q92794
# 3 0.251 BABAM1 Q9NWV8
# 4 0.251 SKI P12755
# 5 0.251 CLK2 P49760
# 6 0.248 TUBB P07437
# 7 0.248 KNL1 Q8NG31
# 8 0.189 SH3RF1 Q7Z6J0
# 9 0.189 SRPK2 P78362
# 10 0.150 CSNK1D P48730
# # . with 239 more rowsThe top genes in the first order neighborhood of the genes of interest can be visualized in the PPI network:
{r fig1,dpi = 300, echo=FALSE, eval = FALSE, fig.cap="PPI network visualization of genes of interest (blue nodes) and their first neighbor with similarity scores (green nodes). "} idx_neighbors <- which(!V(graph_op_1)$Gene_Symbol %in% genes_interest) cols <- rep("lightsteelblue2",vcount(graph_op_1)) cols[idx_neighbors] <- "#57da83" scores.vertex <- rep(1,vcount(graph_op_1)) scores.vertex[idx_neighbors] <- 8*scores[na.omit(match(V(graph_op_1)$Gene_Symbol,scores$gene_symbol)),]$score par(mar=c(0.1,0.1,0.1,0.1)) plot(graph_op_1,vertex.label = ifelse(scores.vertex>=1,V(graph_op_1)$Gene_Symbol,NA), layout = layout.fruchterman.reingold,vertex.color=cols, vertex.size = 7*scores.vertex,edge.width = 0.5,edge.arrow.mode=0, vertex.label.font = 1, vertex.label.cex = 0.45)
library(knitr)
knitr::include_graphics("../figures/fig1.png")## R version 4.2.1 (2022-06-23)
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## attached base packages:
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