PItools_vs_igraph.RmdThis article shows how to download protein interaction data using PItools, import it into igraph and calculate degree of each protein using PItools and igraph.
human_interactome = fullInteractome(taxid = "9606", database = "IntActFTP",
format = "tab27", clean = T, protein_only = TRUE)## ... looking for the date of the latest IntAct release ...
## ... looking for the date of the latest IntAct release ...## ... loading local copy ...## Warning in fread(file_name, header = T, stringsAsFactors = F): Found
## and resolved improper quoting out-of-sample. First healed line 25383:
## <<uniprotkb:P16054 uniprotkb:Q05769 intact:EBI-298451 intact:EBI-298933|
## uniprotkb:Q543K3 psi-mi:kpce_mouse(display_long)|uniprotkb:Prkce(gene
## name)|psi-mi:Prkce(display_short)|uniprotkb:Pkce(gene name synonym)|
## uniprotkb:Pkcea(gene name synonym)|uniprotkb:nPKC-epsilon(gene name
## synonym) psi-mi:pgh2_mouse(display_long)|uniprotkb:Ptgs2(gene name)|
## psi-mi:Ptgs2(display_short)|uniprotkb:Cox-2(gene name synonym)|
## uniprotkb:Cox2(gene name synonym)|uniprotkb:Pghs-b(gene name synonym)|
## uniprotkb:Tis10(gene na>>. If the fields are not quoted (e.g. field
## separator does not appear within any field), try quote="" to avoid this
## warning.human_interactome_igraph = graph_from_data_frame(d = human_interactome$data[,
.(IDs_interactor_A, IDs_interactor_B,
Publication_Identifiers, Confidence_values)],
directed = F)microbenchmark function from microbenchmark R package is the most convenient and accurate way to measure how long code runs. Let’s use this to find which, PItools or igraph will calculate node degree faster.
microbenchmark({edgelist2degree(human_interactome$data)})## Unit: milliseconds
## expr min lq
## { edgelist2degree(human_interactome$data) } 61.28673 67.14363
## mean median uq max neval
## 76.24125 69.63505 73.94154 259.0869 100# igraph is much faster than PItools
microbenchmark({degree(human_interactome_igraph)})## Unit: milliseconds
## expr min lq mean
## { degree(human_interactome_igraph) } 2.002679 2.054321 2.210758
## median uq max neval
## 2.110583 2.200914 5.471241 100# but only if you don't account for time it takes to create an igraph object
microbenchmark({
human_interactome_igraph = graph_from_data_frame(d = human_interactome$data[,
.(IDs_interactor_A, IDs_interactor_B,
Publication_Identifiers, Confidence_values)],
directed = F)
degree(human_interactome_igraph)
})## Unit: milliseconds
## expr
## { human_interactome_igraph = graph_from_data_frame(d = human_interactome$data[, .(IDs_interactor_A, IDs_interactor_B, Publication_Identifiers, Confidence_values)], directed = F) degree(human_interactome_igraph) }
## min lq mean median uq max neval
## 128.6042 136.3452 169.6594 138.6284 213.9711 546.6082 100Sys.Date. = Sys.Date()
Sys.Date.## [1] "2019-04-11"session_info. = devtools::session_info()
session_info.## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.5.1 (2018-07-02)
## os macOS High Sierra 10.13.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Europe/London
## date 2019-04-11
##
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##
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