The input for omics network objects is an igraph object. It can be
undirected, directed, or weighted however the emphasis of these methods
are on undirected networks.
data(omics)
print(omics)#> IGRAPH d2faa53 UN-- 331 362 --
#> + attr: name (v/c), label (v/c), is_tf (v/l), lfc_rna (v/n), snp_frq
#> | (v/n), cor (e/n)
#> + edges from d2faa53 (vertex names):
#> [1] 749--751 749--109 743--692 740--737 740--452 737--454 737--637 735--702
#> [9] 733--398 717--256 715--288 711--442 708--747 708--707 704--177 704--176
#> [17] 702--529 692--694 692--212 690--544 688--234 686--215 683-- 79 674--243
#> [25] 668--262 666--285 664--622 662--261 659--578 659--153 659--729 659--727
#> [33] 659--725 659--134 659--722 659--657 657--577 646--648 646--122 643--371
#> [41] 643--366 640--545 640--329 631--186 631--185 629--230 622--416 620--603
#> [49] 215--618 618--617 615-- 62 615--614 612--300 612--294 285--612 609--325
#> + ... omitted several edges
Vertex names must be unique.
head(igraph::V(omics)$name)#> [1] "749" "751" "109" "743" "692" "740"
Optionally you can annotate the edge and node properties of your input. These can be used to filter nodes, edges, or subset your network in downstream methods.
head(igraph::V(omics)$label)#> [1] "MTH1" "SNF3" "LSM8" "ASN1" "SPC24" "GIP2"
head(igraph::E(omics)$cor)#> [1] -0.5491788 -0.1286078 0.4775522 -0.1169985 0.2564625 0.3313050
To create an omics network object, you can simply pass it your igraph
object.
n <- omics.network$new(omics)
n$peek()#> Omics Network Object
#> IGRAPH d2faa53 UN-- 331 362 --
#> + attr: name (v/c), label (v/c), is_tf (v/l), lfc_rna (v/n), snp_frq
#> | (v/n), cor (e/n), name (e/c)
#> + edges from d2faa53 (vertex names):
#> [1] 749--751 749--109 743--692 740--737 740--452 737--454 737--637 735--702
#> [9] 733--398 717--256 715--288 711--442 708--747 708--707 704--177 704--176
#> [17] 702--529 692--694 692--212 690--544 688--234 686--215 683-- 79 674--243
#> [25] 668--262 666--285 664--622 662--261 659--578 659--153 659--729 659--727
#> [33] 659--725 659--134 659--722 659--657 657--577 646--648 646--122 643--371
#> [41] 643--366 640--545 640--329 631--186 631--185 629--230 622--416 620--603
#> [49] 215--618 618--617 615-- 62 615--614 612--300 612--294 285--612 609--325
#> + ... omitted several edges
Optionally you can provide functions for computing node/edge properties like centrality measures. Rather than annotating your igraph with these measures beforehand, by providing the functions directly, they can be used to update node/edge measures if the graph changes. For example, if you delete edges, remove nodes, or subset your graph, you may want to recompute all of your centrality measures.
# Really simple
node.degree <- function(ig) igraph::degree(ig)
# Specific parameters
node.eigen <- function(ig) igraph::eigen_centrality(ig, directed=FALSE)$vector
edge.betweenness <- function(ig) igraph::edge_betweenness(ig, directed=FALSE)
# More complicated
edge.routes <- function(ig) {
paths <- suppressWarnings(shortest_paths(ig, V(ig), V(ig), output="epath")$epath)
counts <- mapply(function(p) {
igraph::as_ids(p)
}, paths, SIMPLIFY=FALSE, USE.NAMES=TRUE) %>%
unlist() %>%
table()
return(unname(replace_nas(counts[as_ids(E(ig))], 0)))
}
node.fn <-list("degree"=node.degree, "eigen"=node.eigen)
edge.fn <- list("betweenness"=edge.betweenness, "routes"=edge.routes)
n <- omics.network$new(omics, node.fn=node.fn, edge.fn=edge.fn)
n$peek()#> Omics Network Object
#> IGRAPH d2faa53 UN-- 331 362 --
#> + attr: name (v/c), label (v/c), is_tf (v/l), lfc_rna (v/n), snp_frq
#> | (v/n), degree (v/n), eigen (v/n), cor (e/n), name (e/c), betweenness
#> | (e/n), routes (e/n)
#> + edges from d2faa53 (vertex names):
#> [1] 749--751 749--109 743--692 740--737 740--452 737--454 737--637 735--702
#> [9] 733--398 717--256 715--288 711--442 708--747 708--707 704--177 704--176
#> [17] 702--529 692--694 692--212 690--544 688--234 686--215 683-- 79 674--243
#> [25] 668--262 666--285 664--622 662--261 659--578 659--153 659--729 659--727
#> [33] 659--725 659--134 659--722 659--657 657--577 646--648 646--122 643--371
#> [41] 643--366 640--545 640--329 631--186 631--185 629--230 622--416 620--603
#> + ... omitted several edges
The way to think about node and edge properties is that there is an attributes data frame that has a one-to-one mapping to nodes and edges in the graph. These data frames or attributes tables are not data elements in the object but they are created, manipulated, and destroyed on the fly.
head(n$nodes.attributes())#> name label is_tf lfc_rna snp_frq degree eigen
#> 749 749 MTH1 FALSE -0.4385177 0.035319994 2 5.773461e-07
#> 751 751 SNF3 FALSE 0.1285503 0.003035238 1 1.136718e-07
#> 109 109 LSM8 FALSE -0.5849400 0.062844766 7 2.818706e-06
#> 743 743 ASN1 FALSE 1.1166966 0.229042875 1 2.246342e-04
#> 692 692 SPC24 FALSE 0.2306554 0.009771783 3 1.140931e-03
#> 740 740 GIP2 FALSE -0.5743279 0.060585153 2 2.907297e-03
head(n$edges.attributes())#> name from to cor betweenness routes
#> 1 749|751 749 751 -0.5491788 248 1
#> 2 749|109 749 109 -0.1286078 494 247
#> 3 743|692 743 692 0.4775522 248 1
#> 4 740|737 740 737 -0.1169985 1373 12
#> 5 740|452 740 452 0.2564625 1587 13
#> 6 737|454 737 454 0.3313050 1481 0
The name attribute is really important and will always be there. These
are unique identifiers that you can use to query the igraph object
with if necessary.
igraph::shortest_paths(n$ig, from="387", to="109")$vpath#> [[1]]
#> + 7/331 vertices, named, from d2faa53:
#> [1] 387 391 99 122 646 119 109
Querying with the node and edge identifiers is not very practical, you usually want to use symbols or other recognizable labels. Don’t worry about the next few lines of code, it’s just to demonstrate how to use the attribute getter functions to figure out that we just found the shortest path between genes GCR1 and LSM8.
n$nodes("label")[match("387", n$nodes("name"))]#> [1] "GCR1"
n$nodes("label")[match("109", n$nodes("name"))]#> [1] "LSM8"
You can also do edges
head(n$edges("name"))#> [1] "749|751" "749|109" "743|692" "740|737" "740|452" "737|454"
head(n$edges("betweenness"))#> [1] 248 494 248 1373 1587 1481
You can also add annotations. Note: These will not be updated if the graph is changed becuase the object will not have access to an updating function.
clutering.coefficients <- igraph::transitivity(omics, type="localundirected")
clutering.coefficients[is.na(clutering.coefficients)] <- NA
n$nodes.annotate(clutering.coefficients, "clustering")
head(n$nodes.attributes())#> name label is_tf lfc_rna snp_frq degree eigen clustering
#> 749 749 MTH1 FALSE -0.4385177 0.035319994 2 5.773461e-07 0
#> 751 751 SNF3 FALSE 0.1285503 0.003035238 1 1.136718e-07 NA
#> 109 109 LSM8 FALSE -0.5849400 0.062844766 7 2.818706e-06 0
#> 743 743 ASN1 FALSE 1.1166966 0.229042875 1 2.246342e-04 NA
#> 692 692 SPC24 FALSE 0.2306554 0.009771783 3 1.140931e-03 0
#> 740 740 GIP2 FALSE -0.5743279 0.060585153 2 2.907297e-03 0
With the omics network object, you can use other attributes to query which is more practical…
You have complete control over the internal igraph plotting function,
but the key arguments are explicitly defined and default to values for
good looking plots for small-medium sized networks (10-2.5K nodes).
n$plot()
You can also use the attributes in your visualization.
n$plot(vertex.size=n$nodes("degree"))
The network can be filtered by both node and 7 attributes. Because we’re
using a data frame mindset, you can filter the same way you would if you
were using dplyr::filter().
head(n$nodes.attributes())#> name label is_tf lfc_rna snp_frq degree eigen clustering
#> 749 749 MTH1 FALSE -0.4385177 0.035319994 2 5.773461e-07 0
#> 751 751 SNF3 FALSE 0.1285503 0.003035238 1 1.136718e-07 NA
#> 109 109 LSM8 FALSE -0.5849400 0.062844766 7 2.818706e-06 0
#> 743 743 ASN1 FALSE 1.1166966 0.229042875 1 2.246342e-04 NA
#> 692 692 SPC24 FALSE 0.2306554 0.009771783 3 1.140931e-03 0
#> 740 740 GIP2 FALSE -0.5743279 0.060585153 2 2.907297e-03 0
head(n$nodes.filter("degree >= 8"))#> [1] "215" "285" "659" "230" "224" "127"
Here are all the nodes with at least seven edges. You can return any attribute, as you might want to know, for example, which genes are highly connected or how many highly connected nodes are transcription factors?
n$nodes.filter("degree >= 8", attr="label")#> [1] "STE11" "MCM1" "SXM1" "GAL4" "MIG1" "RAP1"
n$nodes.filter("degree >= 8", attr="is_tf")#> [1] FALSE TRUE FALSE TRUE TRUE TRUE
What about finding which highly connect genes are also transcription factors together?
n$nodes.filter("degree >= 8 & is_tf", attr="label")#> [1] "MCM1" "GAL4" "MIG1" "RAP1"
n$edges.filter("cor > 0 & betweenness > 5000", attr="name")#> [1] "452|167" "224|136"
You can use filtering methods to subset the network or add/remove nodes and edges. When the network is modified, a clone of the object is modified and returned. Node and edge attributes are updated based on the new graph structure.
head(n$nodes.attributes())#> name label is_tf lfc_rna snp_frq degree eigen clustering
#> 749 749 MTH1 FALSE -0.4385177 0.035319994 2 5.773461e-07 0
#> 751 751 SNF3 FALSE 0.1285503 0.003035238 1 1.136718e-07 NA
#> 109 109 LSM8 FALSE -0.5849400 0.062844766 7 2.818706e-06 0
#> 743 743 ASN1 FALSE 1.1166966 0.229042875 1 2.246342e-04 NA
#> 692 692 SPC24 FALSE 0.2306554 0.009771783 3 1.140931e-03 0
#> 740 740 GIP2 FALSE -0.5743279 0.060585153 2 2.907297e-03 0
n.s <- n$graph.delete.nodes(c("MTH1","SNF3"), attr="label")
head(n.s$nodes.attributes())#> name label is_tf lfc_rna snp_frq degree eigen clustering
#> 109 109 LSM8 FALSE -0.5849400 0.062844766 6 2.664458e-06 0
#> 743 743 ASN1 FALSE 1.1166966 0.229042875 1 2.246342e-04 NA
#> 692 692 SPC24 FALSE 0.2306554 0.009771783 3 1.140931e-03 0
#> 740 740 GIP2 FALSE -0.5743279 0.060585153 2 2.907297e-03 0
#> 737 737 GIP1 FALSE 0.3412003 0.021382837 3 1.222090e-03 0
#> 452 452 GLC7 FALSE 0.5168273 0.049061103 6 1.354426e-02 0
There are some useful functions for removing multiple edges or loops.
n.s <- n$graph.simplify(remove.multiple=TRUE, remove.loops=TRUE)Or deleting disconnected nodes.
n.s <- n$graph.delete.isolates()Before subsetting nodes, you can query neighbors at various degrees.
n$nodes.neighbors(ids="SLX5", attr="label", neighbors.only=TRUE, degree=1)#> [1] "PRP9" "SNP1" "SLX8"
n$nodes.neighbors(ids="SLX5", attr="label", neighbors.only=TRUE, degree=2)#> [1] "PRP9" "SNP1" "SLX8" "TAF4" "NOG2" "IOC4" "GCR2" "ACS2" "YCH1"
n$nodes.neighbors(ids="SLX5", attr="label", neighbors.only=TRUE, degree=3)#> [1] "PRP9" "SNP1" "SLX8" "TAF4" "NOG2" "IOC4" "GCR2" "ACS2" "YCH1"
#> [10] "LSM8" "PRP21" "TAH18" "GCR1"
Or just subset directly.
n.s <- n$graph.subset.nodes("SLX5", attr="label", degree=0)
n.s$plot(vertex.label=n.s$nodes("label"), vertex.label.dist=1)
n.s <- n$graph.subset.nodes("SLX5", attr="label", degree=1)
n.s$plot(vertex.label=n.s$nodes("label"), vertex.label.dist=1)
n.s <- n$graph.subset.nodes("SLX5", attr="label", degree=2)
n.s$plot(vertex.label=n.s$nodes("label"), vertex.label.dist=1)
n.s <- n$graph.subset.nodes("SLX5", attr="label", degree=3)
n.s$plot(vertex.label=n.s$nodes("label"), vertex.label.dist=1)
n.s <- n$graph.subset.nodes("SLX5", attr="label", degree=4)
n.s$plot(vertex.label=n.s$nodes("label"), vertex.label.dist=1)
Lets split the graph in half.
# Get the node identifiers
n.s$nodes.map(c("SLX5", "PRP9"), "label", "name")#> [1] "122" "646"
# Filter out the edges
n.s.s <- n.s$edges.filter("name != '122|646' & name != '646|122'") %>%
n.s$graph.subset.edges()
# We just dleted the SLX5-PRP9 edge
n.s.s$plot(vertex.label=n.s.s$nodes("label"))
We can make the visualizations a bit fancier.
n.s <- n$nodes.filter("degree > 5", attr="label") %>%
n$graph.subset.nodes(attr="label", degree=1)
n.s$plot(vertex.label=n.s$nodes("label"))
n.s$plot(vertex.label=ifelse(n.s$nodes("degree") > 8, n.s$nodes("label"), ""),
vertex.size=normalize.range(n.s$nodes("degree"), 5, 15),
vertex.color=colorize(n.s$nodes("eigen")),
vertex.shape=c("circle", "square")[as.numeric(n.s$nodes("is_tf"))+1],
vertex.label.color="black",
vertex.label.dist=0,
layout=igraph::layout_nicely(n.s$ig),
seed=1)
n.s$plot(vertex.size=normalize.range(n.s$nodes("degree"), 5, 15),
vertex.color=colorize(n.s$nodes("eigen")),
vertex.shape=c("circle", "square")[as.numeric(n.s$nodes("is_tf"))+1],
edge.width=normalize.range(abs(n.s$edges("cor")), 1, 7),
edge.color=c("red", "green")[as.numeric(n.s$edges("cor") > 0)+1])