The aracne.networks data package provides context-specific transcriptional regulatory networks (also called interactomes or regulons) reverse engineered by the ARACNe algorithm from The Cancer Genome Atlas (TCGA) RNAseq expression profiles.
This package contains 25 Mutual Information-based networks assembled by ARACNe-AP (Giorgi et al., 2016) with default parameters (MI p-value = \(10^{-8}\), 100 bootstraps and permutation seed = 1). ARACNe is a network inference algorithm based on an Adaptive Partitioning (AP) Mutual Information (MI) approach (Giorgi et al., 2016). In short, ARACNe-AP estimates all pairwise Mutual Information scores between gene expression profiles, then assesses the significance of such Mutual Information by comparison to a null dataset. ARACNe then draws network edges between centroid genes (Transcription Factors and Signaling Proteins) and genes significantly associated with them (i.e. with significant MI). It then calculates Data Processing Inequality (DPI) to reduce the number of indirect connections.
ARACNe-AP was run on RNA-Seq datasets normalized using Variance-Stabilizing Transformation (Anders and Huber, 2010). The raw data was downloaded on April 15th, 2015 from the TCGA official website (Weinstein et al., 2013). We follow the TCGA naming convention (e.g. BRCA = Breast Carcinoma) to name the individual context-specific networks.
The networks are hosted on Zenodo
(doi:10.5281/zenodo.22918956).
The list of available networks is returned by listRegulons():
library(aracne.networks)
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listRegulons()
#> network object tumor tcga regulators
#> 1 blca regulonblca Bladder Carcinoma BLCA 6054
#> 2 brca regulonbrca Breast Carcinoma BRCA 6054
#> 3 cesc reguloncesc Cervical Squamous Carcinoma CESC 6056
#> 4 coad reguloncoad Colon Adenocarcinoma COAD 6056
#> 5 esca regulonesca Esophageal Carcinoma ESCA 5951
#> 6 gbm regulongbm Glioblastoma GBM 6056
#> 7 hnsc regulonhnsc Head and Neck Squamous Carcinoma HNSC 6055
#> 8 kirc regulonkirc Kidney Renal Clear Cell Carcinoma KIRC 6054
#> 9 kirp regulonkirp Kidney Papillary Carcinoma KIRP 6055
#> 10 laml regulonlaml Acute Myeloid Leukemia LAML 6007
#> 11 lihc regulonlihc Liver Hepatocellular Carcinoma LIHC 6056
#> 12 luad regulonluad Lung Adenocarcinoma LUAD 6055
#> 13 lusc regulonlusc Lung Squamous Carcinoma LUSC 6054
#> 14 net regulonnet Neuroendocrine Tumor 6129
#> 15 ov regulonov Ovarian Carcinoma OV 6007
#> 16 paad regulonpaad Pancreas Carcinoma PAAD 6056
#> 17 pcpg regulonpcpg Pheochromocytoma and Paraganglioma PCPG 6056
#> 18 prad regulonprad Prostate Carcinoma PRAD 6053
#> 19 read regulonread Rectal Adenocarcinoma READ 6056
#> 20 sarc regulonsarc Sarcoma SARC 6112
#> 21 stad regulonstad Stomach Adenocarcinoma STAD 6056
#> 22 tgct regulontgct Testicular Cancer TGCT 6056
#> 23 thca regulonthca Thyroid Carcinoma THCA 6053
#> 24 thym regulonthym Thymoma THYM 6056
#> 25 ucec regulonucec Uterine Corpus Endometrial Carcinoma UCEC 6055
#> interactions bytes
#> 1 489101 8600462
#> 2 331919 5961290
#> 3 583961 10061971
#> 4 413789 7329733
#> 5 529286 9121682
#> 6 563850 9594907
#> 7 423104 7523271
#> 8 350478 6203291
#> 9 452653 8140171
#> 10 531535 9215533
#> 11 469922 8369685
#> 12 399513 7171481
#> 13 455032 8121045
#> 14 666241 12640179
#> 15 647358 11021683
#> 16 520756 9172179
#> 17 603617 10372282
#> 18 330922 5893202
#> 19 557911 9804367
#> 20 526591 9206983
#> 21 561858 10213115
#> 22 432621 8065701
#> 23 317582 5435521
#> 24 387923 7028306
#> 25 469845 8620658A network is retrieved with getRegulon(), using either its short name or the
name of the data set distributed with previous versions of the package (e.g.
"regulonblca"). The first call downloads the network and stores it in a local
cache managed by BiocFileCache; subsequent calls read
it from the cache, without requiring an internet connection.
regulonblca <- getRegulon("blca")
#> Downloading regulonblca (8.6 MB), it will be cached for future use
class(regulonblca)
#> [1] "regulon"
length(regulonblca)
#> [1] 6054Code written for previous versions of the package, which used
data(regulonblca), can be updated by replacing that call with
regulonblca <- getRegulon("blca").
The package contains a function to print individual networks into a file. Four columns will be printed: the Regulator id, the Target id, the Mode of Action (MoA, inferred by Spearman correlation analysis (Alvarez et al., 2016)) that indicates the sign of the association between regulator and target gene and ranges between -1 and +1, the Likelihood (essentially an edge weight that indicates how strong the mutual information for an edge is when compared to the maximum observed MI in the network, it ranges between 0 and 1). Further details about the regulon object as a model for transcriptional regulation are present in the manuscript (Alvarez et al., 2016).
In the following example, we print the first 10 interactions from the bladder carcinoma (blca) network. The network genes are identified by Entrez Gene ids.
write.regulon(regulonblca, n = 10)
#> Regulator Target MoA likelihood
#> 10002 2648 0.994689591270463 0.886774633189913
#> 10002 677827 0.116175345640136 0.707841406455471
#> 10002 80152 0.999770437015603 0.950286744281199
#> 10002 284382 -0.0368424333564396 0.0419762049859333
#> 10002 9866 0.972066598154448 0.442238853411591
#> 10002 283422 -0.574084929385018 0.260828476620346
#> 10002 221613 -0.0959242601820319 0.717904706549976
#> 10002 348174 0.953943934091558 0.814491117578869
#> 10002 373509 0.704691385719852 0.244337186726846
#> 10002 8803 -0.959165656086931 0.831653033754096The user may want to analyze all the connections of a particular regulator (E.g. “399”, the RHOH gene).
write.regulon(regulonblca, regulator = "399")
#> Regulator Target MoA likelihood
#> 399 9595 1 0.999999439751274
#> 399 54440 1 0.999999439753891
#> 399 5788 1 0.999993691255193
#> 399 2124 1 0.999993972431349
#> 399 10563 0.999999999999987 0.999880973084544
#> 399 80342 1 0.999979237947268
#> 399 1840 0.999999959099145 0.994240739975982
#> 399 8875 0.999999999999397 0.999602389369848
#> 399 6689 0.999999999998723 0.999531614767901
#> 399 200186 0.154403590654008 0.948828817305409
#> 399 165631 0.999999999950565 0.998777586463862
#> 399 54509 0.999999981560018 0.997883918024065
#> 399 171389 0.999999994824044 0.996800613785205
#> 399 147929 -0.999154534552766 0.985197674740525
#> 399 23416 0.999929331217517 0.96812145442081
#> 399 26015 -0.992838466368412 0.834785111763068
#> 399 10148 0.999999999999872 0.999729153685544
#> 399 4951 -0.0504647730526015 0.544073601564966
#> 399 57003 -0.0751708929022855 0.714920200879607sessionInfo()
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