1 Overview

Recurrent patterns in biological networks may reflect critical roles in multiple biological processes (Bracken, Scott, and Goodall 2016), for example, regulatory loops between transcription factors and microRNAs (Zhang et al. 2015). RTNduals searches for regulatory patterns between pairs of regulators, using regulatory networks generated by the RTN package (for details, please refer to the RTN documentation) (Castro et al. 2016). In such a network, each regulator has an associated set of target genes (i.e. a regulon), and when we assess the shared targets between a pair of regulators, we find triplets that may be regulated in a positive or negative direction, whith regulators either cooperating or competing in the regulatory network. The inference of dual regulons requires three complementary statistics: (1) Targets are assigned to regulons based on mutual information (MI) between the regulator and the target. The significance of the MI statistics is assessed by permutation and bootstrap analysis. (2) Shared targets between any two regulons are identified and the similarity in regulation (i.e. positive or negative direction) is assessed by correlation analysis. (3) A test is carried out to determine if the number of shared targets is higher than expected by chance. The schematics in Figure 1 show two triplets formed between regulators. In (a) the two regulators co-operate by influencing shared targets in the same direction (co-activation or co-repression), while in (b) they compete, influencing targets in opposite directions. For gene expression data, typical regulators might include transcription factors, miRNAs, eRNAs and lncRNAs.

Figure 1. Examples of regulators and predicted associations. This figure illustrates four triplets formed between regulators (a). Regulators R1 and R2 co-activate or co-repress shared targets. (b) Regulators R1 and R2 compete, influencing targets in opposite directions.

2 Quick Start

The RTNduals workflow starts with a preprocessing step that generates a TNI-class object from an expression matrix and a list of regulators. The expression matrix is typically obtained from multiple samples (e.g. transcriptomes from a cancer cohort), while the list of regulators represents some prior biological information indicating which genes in the expression matrix should be regarded as regulators. The input data can also deal with different classes of regulators; for example, genes and microRNAs. In this case, the expression matrix should comprise mRNA and miRNA expression values.

##--- load package and dataset for demonstration
library(RTNduals)
data("dt4rtn", package = "RTN")
gexp <- dt4rtn$gexp
annot <- dt4rtn$gexpIDs
tfs <- dt4rtn$tfs[c("IRF8","IRF1","PRDM1","E2F3","STAT4","LMO4","ZNF552")]

##--- generate a pre-processed TNI-class object
rtni <- tni.constructor(gexp, regulatoryElements = tfs, rowAnnotation=annot)

##--- compute a regulatory network (set nPermutations>=1000)
rtni <- tni.permutation(rtni, nPermutations=100, pValueCutoff=0.05)

##--- check stability of the regulatory network (set nBootstrap>=100)
rtni <- tni.bootstrap(rtni, nBootstrap=10)

##--- Compute the DPI-filtered regulatory network
# Note: we recommend setting 'eps = NA' in order to 
# estimate the threshold from the empirical null 
# distribution computed in the permutation and 
# bootstrap steps.
rtni <- tni.dpi.filter(rtni, eps = NA)

##--- construct an mbr object and apply DPI algorithm
rmbr <- tni2mbrPreprocess(rtni)

##--- test associations for dual regulons
rmbr <- mbrAssociation(rmbr)

##--- check summary
mbrGet(rmbr, what="summary")

## $MBR
## $MBR$Duals
##       Tested Predicted
## Duals     21         2
## 
## 
## $TNI
## $TNI$tnet
##          Regulators Targets Edges
## tnet.ref          7    2434  6912
## tnet.dpi          7    2434  3675
## 
## $TNI$regulonSize
##          Min. 1st Qu. Median     Mean 3rd Qu. Max.
## tnet.ref  825   841.0   1000 987.4286  1059.5 1286
## tnet.dpi  315   364.5    477 525.0000   649.0  856

##--- get results
overlap <- mbrGet(rmbr, what="dualsOverlap")
correlation <- mbrGet(rmbr, what="dualsCorrelation")

3 Session information

## R version 3.5.3 (2019-03-11)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.6 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] RTNduals_1.6.2   RTN_2.6.3        BiocStyle_2.10.0
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.1                  XVector_0.22.0             
##  [3] GenomeInfoDb_1.18.2         compiler_3.5.3             
##  [5] BiocManager_1.30.4          zlibbioc_1.28.0            
##  [7] bitops_1.0-6                class_7.3-15               
##  [9] mixtools_1.1.0              tools_3.5.3                
## [11] minet_3.40.0                digest_0.6.18              
## [13] evaluate_0.13               lattice_0.20-38            
## [15] pkgconfig_2.0.2             Matrix_1.2-17              
## [17] igraph_1.2.4                DelayedArray_0.8.0         
## [19] yaml_2.2.0                  parallel_3.5.3             
## [21] xfun_0.5                    GenomeInfoDbData_1.2.0     
## [23] e1071_1.7-1                 stringr_1.4.0              
## [25] knitr_1.22                  S4Vectors_0.20.1           
## [27] IRanges_2.16.0              stats4_3.5.3               
## [29] segmented_0.5-3.0           grid_3.5.3                 
## [31] data.table_1.12.0           Biobase_2.42.0             
## [33] snow_0.4-3                  BiocParallel_1.16.6        
## [35] survival_2.43-3             rmarkdown_1.12             
## [37] bookdown_0.9                limma_3.38.3               
## [39] RedeR_1.30.0                viper_1.16.0               
## [41] magrittr_1.5                matrixStats_0.54.0         
## [43] GenomicRanges_1.34.0        htmltools_0.3.6            
## [45] BiocGenerics_0.28.0         MASS_7.3-51.1              
## [47] splines_3.5.3               SummarizedExperiment_1.12.0
## [49] KernSmooth_2.23-15          stringi_1.4.3              
## [51] RCurl_1.95-4.12