Working with interactive network objects makes it easy to perform interactive analyses in Shiny and continue in the command line for extending analysis where you left off.

yeast.networks <- readRDS(file.path(system.file("extdata", package="bieulergy"), "yeast-networks.rds"))

Using Interactive Omics Network Objects

Network objects are essentially undirected igraph objects with pre-computed graph and node level properties. Additionally, they allow a mapping of unique node identifiers to human-readable symbols. These symbols are typically gene or protein symbols. This allows for integrating enrichment-based analysis or incorporating node-level information into network analyses.

network <- yeast.networks[[1]]

print(network)

IGRAPH bc54de4 UN-- 331 361 -- 
+ attr: name (v/c), symbol (v/c), is_tf (v/l), lfc_mrna (v/n), snp_frq
| (v/n)
+ edges from bc54de4 (vertex names):
 [1] 749--751 749--109 109--107 109--119 109--117 109--114 109--110 109--112
 [9] 743--692 692--694 692--212 740--737 740--452 737--454 737--637 452--454
[17] 452--460 452--458 452--456 452--167 454--401 637--601 735--702 702--529
[25] 733--398 398--167 717--256 256--251 715--288 288--285 711--442 442--443
[33] 708--747 708--707 704--177 704--176 177--176 529--530 212--215 212--217
[41] 212--213 690--544 544--545 688--234 234--241 234--235 234--239 234--237
[49] 686--215 686--298 215--618 215--581 215--414 215--309 215--217 215--192
+ ... omitted several edges

Object Structure

str(network)

Classes 'interactive.omics.network', 'R6' <interactive.omics.network>
  Public:
    clone: function (deep = FALSE) 
    edges: data.frame
    get.interactors: function (ids, degree = 1, remove.ids = TRUE, use.symbols = FALSE) 
    get.subnetwork: function (ids, degree = 1, indirect.edges = FALSE) 
    get.symbols: function (ids) 
    ig: igraph
    init.edges: function () 
    init.nodes: function (symbols = NULL, measures = c(), quiet = TRUE) 
    init.properties: function (measures = c(), quiet = TRUE) 
    initialize: function (ig, symbols = NULL, graph.measures = c("nodes", "edges", 
    nodes: data.frame
    pca: PCA, list
    plt.subnetwork: function (ids, degree = 1, indirect.edges = FALSE, use.symbols = FALSE, 
    print: function () 
    properties: list

head(network$nodes)

     id symbol is_tf   lfc_mrna     snp_frq label degree        eigen
749 749   MTH1 FALSE -0.4385177 0.035319994   749      2 2.661820e-07
751 751   SNF3 FALSE  0.1285503 0.003035238   751      1 5.257070e-08
109 109   LSM8 FALSE -0.5849400 0.062844766   109      7 1.295192e-06
743 743   ASN1 FALSE  1.1166966 0.229042875   743      1 2.299056e-04
692 692  SPC24 FALSE  0.2306554 0.009771783   692      3 1.164084e-03
740 740   GIP2 FALSE -0.5743279 0.060585153   740      2 2.958689e-03
    betweenness stress
749     247.000   1361
751       0.000      0
109    3680.751  23693
743       0.000      0
692     493.000    617
740    1356.000   5772

head(network$edges)

  from  to weight color
1  749 751      2  grey
2  749 109      2  grey
3  109 107      2  grey
4  109 119      2  grey
5  109 117      2  grey
6  109 114      2  grey

Converting unique nodes identifiers to mapped symbols

network$get.symbols(c("749", "109", "740"))

[1] "MTH1" "LSM8" "GIP2"

Find interactors or filter sub networks

network$get.interactors(c("109"))

[1] "749" "107" "119" "117" "114" "110" "112"

network$get.interactors(c("109", "107"))

[1] "749" "607" "119" "117" "114" "110" "112" "522" "106"

network$plt.subnetwork(ids=c("109", "107"))

network$plt.subnetwork(ids=c("109", "107"), indirect.edges=TRUE)

network$plt.subnetwork(ids=c("109", "107"), degree=3)

network$plt.subnetwork(ids=c("109", "107"), degree=3, use.symbols=TRUE)

network$plt.subnetwork(ids=c("109", "107"), degree=3, use.symbols=TRUE, node.color="degree")

network$plt.subnetwork(ids=c("109", "107"), degree=5, use.symbols=TRUE, node.color="degree", layout="layout_on_grid")

network$plt.subnetwork(ids=c("109", "107", "607"), degree=0)

Omics Accessory Data

What if we have lots of multi-omics data we’d like to incorporate into the node/edge properties?

network$plt.subnetwork(ids=c("285"),
                       degree=2,
                       use.symbols=TRUE,
                       node.size="eigen",
                       node.color=list("lfc_mrna", cfx(c("blue", "white", "red"))),
                       node.border=list("snp_frq", cfx(c("#e9e9e9", "black"))),
                       node.shape="is_tf")

Sub Network Enrichment

What if we wanted to test enrichment of biological pathways within a sub network?

library(hypeR)
genesets <- enrichr_download(genesets="KEGG_2019", db="YeastEnrichr")

signature <- network$get.interactors(ids=c("285"), degree=2, remove.ids=FALSE, use.symbols=TRUE)
head(signature)

[1] "MCM1"   "HSP150" "PIS1"   "CDC6"   "STE12"  "STE2"

hyp <- hypeR(signature, genesets, background=16454)
hyp_dots(hyp)

head(hyp$data[,2:6])