iGraph data management
Emil Pedersen
2026-01-09
Source:vignettes/igraph_data_management.Rmd
igraph_data_management.RmdWe will here demonstrate some basic data management operations using
igraph objects created with the prepare_graph() function in
LTFGRS.
# hand curated trio information, taken from LTFHPlus vignette:
# https://emilmip.github.io/LTFHPlus/articles/FromTrioToFamilies.html
family = tribble(
~id, ~momcol, ~dadcol,
"pid", "mom", "dad",
"sib", "mom", "dad",
"mhs", "mom", "dad2",
"phs", "mom2", "dad",
"mom", "mgm", "mgf",
"dad", "pgm", "pgf",
"dad2", "pgm2", "pgf2",
"paunt", "pgm", "pgf",
"pacousin", "paunt", "pauntH",
"hspaunt", "pgm", "newpgf",
"hspacousin", "hspaunt", "hspauntH",
"puncle", "pgm", "pgf",
"pucousin", "puncleW", "puncle",
"maunt", "mgm", "mgf",
"macousin", "maunt", "mauntH",
"hsmuncle", "newmgm", "mgf",
"hsmucousin", "hsmuncleW", "hsmuncle"
)
# creating a graph for the family
graph = prepare_graph(.tbl = family, icol = "id", mcol = "momcol", fcol = "dadcol")Below, we print an igraph object. For additional details on the print of an igraph object, please see this documentation. It contains some summary information about the graph:
print(graph)## IGRAPH 27a41d7 DN-- 31 44 --
## + attr: name (v/c)
## + edges from 27a41d7 (vertex names):
## [1] dad ->pid mom ->pid dad ->sib
## [4] mom ->sib dad2 ->mhs mom ->mhs
## [7] dad ->phs mom2 ->phs mgf ->mom
## [10] mgm ->mom pgf ->dad pgm ->dad
## [13] pgf2 ->dad2 pgm2 ->dad2 pgf ->paunt
## [16] pgm ->paunt pauntH ->pacousin paunt ->pacousin
## [19] newpgf ->hspaunt pgm ->hspaunt hspauntH->hspacousin
## [22] hspaunt ->hspacousin pgf ->puncle pgm ->puncle
## + ... omitted several edgesThe first row contains “DN– 31 44 –”. This means that the graph is
directed (D) and named (N), meaning the vertices are named. The graph
contains 31 vertices (individuals in the graph) and 44 edges (familial
links). However, the construction of the graph in
prepare_graph() adds dummy connections between siblings to
assist with relatedness calculations and to ensure neighbourhood graphs
will contain all relevant individuals. The dummy relations can be
removed with the following command, which removes all undirected edges
and the total number can be read from the print:
delete_edges(graph, E(graph)[which_mutual(graph)])## IGRAPH ceb1127 DN-- 31 34 --
## + attr: name (v/c)
## + edges from ceb1127 (vertex names):
## [1] dad ->pid mom ->pid dad ->sib
## [4] mom ->sib dad2 ->mhs mom ->mhs
## [7] dad ->phs mom2 ->phs mgf ->mom
## [10] mgm ->mom pgf ->dad pgm ->dad
## [13] pgf2 ->dad2 pgm2 ->dad2 pgf ->paunt
## [16] pgm ->paunt pauntH ->pacousin paunt ->pacousin
## [19] newpgf ->hspaunt pgm ->hspaunt hspauntH->hspacousin
## [22] hspaunt ->hspacousin pgf ->puncle pgm ->puncle
## + ... omitted several edgesHence, there are 34 true familial relations in the graph. The number
of vertices can also be extracted directly with
vcount(graph) and the number of edges can be extracted with
ecount(graph).
The second row is + attr: name (v/c), which indicates
that the graph has an attribute, i.e. a piece of information attached to
each vertex (v). The attribute is called name and is of
type character (c). The name attribute contains the
individual IDs. The third row onwards list (a subset of) the edges in
the graph.
An igraph object is a hidden list structure. To see the full
structure of the igraph object, the str() function can be
used:
str(graph)## Class 'igraph' hidden list of 10
## $ : num 31
## $ : logi TRUE
## $ : num [1:44] 0 1 0 1 2 1 0 3 4 5 ...
## $ : num [1:44] 23 23 22 22 24 24 25 25 1 1 ...
## $ : NULL
## $ : NULL
## $ : NULL
## $ : NULL
## $ :List of 4
## ..$ : num [1:3] 1 0 1
## ..$ : Named list()
## ..$ :List of 1
## .. ..$ name: chr [1:31] "dad" "mom" "dad2" "mom2" ...
## ..$ : Named list()
## $ :<environment: 0x55f0cfd162c8>Manupulations of the graph can therefore be done with suitable list operations, however, igraph also contains a number of helper functions to make common graph operations easier.
Manipulating attributes
Below is an example of how an attribute can be added to a vertex in an igraph object:
graph = set_vertex_attr(graph = graph, # graph to add attribute to
name = "status", # name of attribute to be added
index = V(graph), # which vertices to add attribute to, V(graph) adds to all vertices
value = sample(c(FALSE, TRUE), size = vcount(graph), replace = TRUE, prob = c(0.95, 0.05))) # value of the attiribute to be added
print(graph)## IGRAPH 27a41d7 DN-- 31 44 --
## + attr: name (v/c), status (v/l)
## + edges from 27a41d7 (vertex names):
## [1] dad ->pid mom ->pid dad ->sib
## [4] mom ->sib dad2 ->mhs mom ->mhs
## [7] dad ->phs mom2 ->phs mgf ->mom
## [10] mgm ->mom pgf ->dad pgm ->dad
## [13] pgf2 ->dad2 pgm2 ->dad2 pgf ->paunt
## [16] pgm ->paunt pauntH ->pacousin paunt ->pacousin
## [19] newpgf ->hspaunt pgm ->hspaunt hspauntH->hspacousin
## [22] hspaunt ->hspacousin pgf ->puncle pgm ->puncle
## + ... omitted several edgesNotice that the status attribute has been added to the
print of the graph above. The attribute is a vector (v) of type logical
(l) and contains either FALSE or TRUE.
All attributes can be extracted with the vertex_attr()
function, since attributes can be any type of data format it is stored
as a list:
vertex_attr(graph)## $name
## [1] "dad" "mom" "dad2" "mom2" "mgf"
## [6] "mgm" "pgf" "pgm" "pgf2" "pgm2"
## [11] "pauntH" "paunt" "newpgf" "hspauntH" "hspaunt"
## [16] "puncle" "puncleW" "mauntH" "maunt" "newmgm"
## [21] "hsmuncle" "hsmuncleW" "sib" "pid" "mhs"
## [26] "phs" "pacousin" "hspacousin" "pucousin" "macousin"
## [31] "hsmucousin"
##
## $status
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [25] TRUE FALSE FALSE FALSE FALSE FALSE FALSEAttributes can also be added in a list-style assignment:
# adding another vertex attribute with a list-style assignment
V(graph)$age = sample(20:80, size = vcount(graph), replace = TRUE)
vertex_attr(graph)## $name
## [1] "dad" "mom" "dad2" "mom2" "mgf"
## [6] "mgm" "pgf" "pgm" "pgf2" "pgm2"
## [11] "pauntH" "paunt" "newpgf" "hspauntH" "hspaunt"
## [16] "puncle" "puncleW" "mauntH" "maunt" "newmgm"
## [21] "hsmuncle" "hsmuncleW" "sib" "pid" "mhs"
## [26] "phs" "pacousin" "hspacousin" "pucousin" "macousin"
## [31] "hsmucousin"
##
## $status
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [25] TRUE FALSE FALSE FALSE FALSE FALSE FALSE
##
## $age
## [1] 23 67 53 54 44 41 62 26 51 72 25 28 41 29 51 72 53 36 71 75 41 50 20 25 53
## [26] 70 65 20 44 66 69If an attribute is no longer needed, it can be removed with the
delete_vertex_attr() function:
delete_vertex_attr(graph = graph, name = "age") %>% # age is not removed from graph, since it is not stored.
vertex_attr()## $name
## [1] "dad" "mom" "dad2" "mom2" "mgf"
## [6] "mgm" "pgf" "pgm" "pgf2" "pgm2"
## [11] "pauntH" "paunt" "newpgf" "hspauntH" "hspaunt"
## [16] "puncle" "puncleW" "mauntH" "maunt" "newmgm"
## [21] "hsmuncle" "hsmuncleW" "sib" "pid" "mhs"
## [26] "phs" "pacousin" "hspacousin" "pucousin" "macousin"
## [31] "hsmucousin"
##
## $status
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [25] TRUE FALSE FALSE FALSE FALSE FALSE FALSEUsing attributes for calculations
First, we create family graphs for all probands in the population
graph created above. Here, we set ndegree = 1, meaning that
only first-degree relatives (parents, siblings, children) will be
included in the family graphs.
fam_graphs = get_family_graphs(pop_graph = graph, ndegree = 1, proband_vec = V(graph)$name)Below is a function that takes a proband ID and a family graph as input arguments. The function is intended to calculate a logical variable indicating whether any family member in the (neighobourhood) graph is diagnosed with a phenotype of interest. This is equivalent to calculating a binary family history indicator based on all identified family members.
get_FH = function(cur_proband, cur_graph) {
fam_indx = which(V(cur_graph)$name != cur_proband)
any(vertex_attr(cur_graph, name = "status", index = V(cur_graph)$name[fam_indx]))
}We can then use this function to calculate family history for all
probands in the fam_graphs tibble created above:
fam_graphs %>%
mutate(FH = purrr::map2_lgl(.x = fid, .y = fam_graph,
.f = ~ get_FH(cur_proband = .x, cur_graph = .y)))## # A tibble: 31 × 3
## fid fam_graph FH
## <chr> <list> <lgl>
## 1 dad <igraph> FALSE
## 2 mom <igraph> TRUE
## 3 dad2 <igraph> TRUE
## 4 mom2 <igraph> FALSE
## 5 mgf <igraph> FALSE
## 6 mgm <igraph> FALSE
## 7 pgf <igraph> FALSE
## 8 pgm <igraph> FALSE
## 9 pgf2 <igraph> FALSE
## 10 pgm2 <igraph> FALSE
## # ℹ 21 more rowsRemoving vertices with certain attribute values
We can also remove vertices based on attribute values. For example, to remove all individuals older than 50 years from the graph above, we can do:
delete_vertices(graph = graph,
v = V(graph)[which(vertex_attr(graph, name = "age") > 50)])## IGRAPH 702a1c0 DN-- 15 10 --
## + attr: name (v/c), status (v/l), age (v/n)
## + edges from 702a1c0 (vertex names):
## [1] dad ->pid dad ->sib pgm ->dad
## [4] pgm ->paunt hspauntH->hspacousin mgf ->hsmuncle
## [7] sib ->pid pid ->sib paunt ->dad
## [10] dad ->paunt