Supporting Material for Trefflich2019

Context

Fletcher et al. (2013) reconstructed regulons for 809 transcription factors (TFs) using microarray transcriptomic data from breast tissue, either from cancer or normal samples (Curtis et al. 2012). Our goal here is to assess the evolutionary root of the regulons reconstructed by Fletcher et al. (2013) using the geneplast package. This script reproduces the main observations described in Trefflich et al. (2019), which proposed a framework to explore the evolutionary roots of regulons.

Package installation and data sets

Please make sure to install all required packages. Installing and then loading the geneplast.data.string.v91 and Fletcher2013b data packages will make available all data required for this case study.

#-- Call packages
library(geneplast)
library(geneplast.data.string.v91)
library(RTN)
library(Fletcher2013b)
library(ggplot2)
library(ggpubr)
library(plyr)

Inferring evolutionary roots

This analysis will determine the evolutionary root of a gene based on the distribution of its orthologs in a given species tree. We will need two data objects, cogdata and phyloTree, both loaded with the gpdata_string_v91 call. The cogdata is a data.frame object listing orthologous groups (OGs) predicted for 121 eukaryotic species, while the phyloTree is a phylogenetic tree object of class phylo. The groot.preprocess function will check the input data and build an object of class OGR, which will be used in the subsequent steps of the analysis pipeline.

#-- Load orthology data from the 'geneplast.data.string.v91' package
data(gpdata_string_v91)

#-- Create an object of class 'OGR' for a reference 'spid'
ogr <- groot.preprocess(cogdata=cogdata, phyloTree=phyloTree, spid="9606")

The groot function addresses the problem of finding the evolutionary root of a feature in an phylogenetic tree. The method infers the probability that such feature was present in the Last Common Ancestor (LCA) of a given lineage. The groot function assesses the presence and absence of the orthologs in the extant species of the phylogenetic tree in order to build a probability distribution, which is used to identify vertical heritage patterns. The spid=9606 parameter sets Homo sapiens as the reference species, which defines the ancestral lineage assessed in the query (i.e. each ortholog of the reference species will be rooted in an ancestor of the reference species).

#-- Run the 'groot' function and infer the evolutionary roots
ogr <- groot(ogr, nPermutations=1000, verbose=TRUE)

Evolutionary analysis of regulons generated from breast cancer samples

Mapping root-to-gene annotation

In this section we will map the inferred evolutionary roots (available in the ogr object) to genes annotated in the regulons reconstructed by Fletcher et al. (2013) from breast cancer samples (available in the rtni1st object; for the same analysis using normal samples, please see section 5). For a summary of the regulons in the rtni1st object we recommend using the tni.regulon.summary function, which shows that there are 809 regulatory elements (TFs) and 14131 targets.

#-- Load regulons
data("rtni1st")
tni.regulon.summary(rtni1st)
## This regulatory network comprised of 809 regulons. 
## -- DPI-filtered network: 
## regulatoryElements            Targets              Edges 
##                809              14131              47012 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0    10.0    37.0    58.1    80.0   523.0 
## -- Reference network: 
## regulatoryElements            Targets              Edges 
##                809              14131             617672 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0      43     449     764    1245    4148 
## ---

We will transform the rtni1st into a graph object using the tni.graph function. The resulting graph will be assessed by the ogr2igraph function, which will map the root-to-gene annotation; the results will be available in the roots_df data frame for subsequent analysis.

#-- Put regulons into an 'igraph' object 
#-- Note: small regulons (n<15 targets) are romeved in this step.
graph <- tni.graph(rtni1st, gtype = "rmap")

#-- Map the 'ogr' object to the 'igraph' object
graph <- ogr2igraph(ogr, cogdata, graph, idkey = "ENTREZ")

#-- Make a data frame with the gene roots
roots_df <- data.frame(COGID = V(graph)$COGID,
                       SYMBOL = V(graph)$SYMBOL,
                       ENTREZ = V(graph)$ENTREZ, 
                       Root = V(graph)$Root,
                       TRN_element = c("Target","TF")[V(graph)$tfs+1],
                       stringsAsFactors = FALSE)

Please note that some level of missing annotation is expected, as not all gene ids listed in the cogdata might be available in the graph object. Also, small regulons (n < 15 targets) are removed by the tni.graph function. As a final pre-processing step, we will remove genes rooted at the base of the phylogenetic tree, for which the predictions can not discriminate from earlier ancestor roots. Here, 307 TFs and 6308 targets were retained.

#-- Remove NAs from missing annotation
roots_df <- roots_df[complete.cases(roots_df),]

#-- Remove genes rooted at the base of the phylogenetic tree
roots_df <- roots_df[roots_df$Root<max(roots_df$Root),]
rownames(roots_df) <- 1:nrow(roots_df)

#-- Check TF and target counts
table(roots_df$TRN_element)
## Target      TF
##   6308     307

Comparing regulators and targets

A transcriptional regulatory network (TRN) is formed by regulators (TFs) and target genes. The roots_df data frame lists the evolutionary roots inferred for each TRN element, including whether the TRN element is annotated as TF or target.

head(roots_df)
##      COGID  SYMBOL ENTREZ Root  TRN_element
## 1  KOG3119   CEBPG   1054   19        TF
## 2  KOG4217   NR4A2   4929   17        TF
## 3  KOG0493     EN1   2019   17        TF
## 4 NOG80479    TP53   7157   20        TF
## 5  KOG3740 GATAD2A  54815   19        TF
## 6  COG5150     DR1   1810   23        TF
tail(roots_df)
##          COGID   SYMBOL ENTREZ Root TRN_element
## 6610   COG5640      F11   2160   19    Target
## 6611   KOG1418   KCNK18 338567   24    Target
## 6612  NOG39443  TMEM220 388335   14    Target
## 6613  NOG43522 C1orf170  84808    7    Target
## 6614 NOG127335 C16orf96 342346    6    Target
## 6615  NOG27843    PANX3 116337   13    Target

For example, CEBPG gene is placed at root 19 while PANX3 gene is placed at root 13, indicating that the evolutionary root inferred for CEBPG is more ancestral than the evolutionary root inferred for PANX3. Please note that the evolutionary roots are enumerated from the most recent to the most ancestral node of the phylogenetic tree. Also, as the aim of the analysis is to find the root of the orthologs of the reference species, the root enumeration is related to the ancestral lineage of the reference species (for details of the phylogenetic tree, see Figure S4 of the Geneplast’s vignette).

Here we will compare the distribution of the evolutionary roots inferred for TFs and target genes using the Wilcoxon-Mann-Whitney test, and then generate violin plots (please refer to Trefflich et al. (2019) for additional details).

#-- Assess root distribution by TRN_element
wilcox.test(Root ~ TRN_element, data=roots_df)
## Wilcoxon rank sum test with continuity correction
## data:  Root by TRN_element
## W = 812534, p-value = 1.6e-06
## alternative hypothesis: true location shift is not equal to 0
#-- Set roots to display in y-axis
roots <- c(4,8,11,13,19,21,25)

#-- Set a summary function to display dispersion within the violins
data_summary <- function(x) {
  y <- mean(x); ymin <- y-sd(x); ymax <- y+sd(x)
  return(c(y=y,ymin=ymin,ymax=ymax))
}

#-- (Figure S1) Generate violin plots showing root distribution by TRN_element
p <- ggplot(roots_df, aes(x=TRN_element, y=Root)) + 
  geom_violin(aes(fill=TRN_element), adjust=2, show.legend=F) +
  scale_y_continuous(breaks=roots, labels=paste("root",roots)) +
  scale_fill_manual(values=c("#c7eae5","#dfc27d")) +
  labs(x="TRN elements", y="Root distribution") +
  scale_x_discrete(limits=c("TF","Target"), labels=c("TFs","Targets")) +
  theme_classic() +
  theme(text=element_text(size=20)) + 
  stat_summary(fun.data = data_summary)
p + stat_compare_means(method="wilcox.test",
                       comparisons =list(c("TF","Target")),
                       label = "p.signif")

title Figure S1. Distribution of the inferred evolutionary roots of TFs and target genes using regulons available from the rtni1st data object. ****P-value = 1.6e-06 (Wilcoxon-Mann-Whitney test).

Next we compute the root distance between a TF and its targets, and then generate a pie chart and a boxplot that reproduce the evolutionary scenarios discussed in Trefflich et al. (2019).

#-- Get roots for TFs
idx <- roots_df$TRN_element=="TF"
tfroots <- roots_df$Root[idx]
names(tfroots) <- roots_df$SYMBOL[idx]

#-- Get roots for target genes
regulonlist <- tni.get(rtni1st, what = "regulons", idkey = "ENTREZ")[names(tfroots)]
targetroots <- lapply(regulonlist, function(reg){
  roots_df$Root[roots_df$ENTREZ%in%reg]
})

#-- Compute root distances between a TF and its targets
rootdist <- sapply(names(targetroots), function(reg){
  targetroots[[reg]]-tfroots[reg]
})

#-- Compute median root distances and sort related objects
rootdist_med <- sort(unlist(lapply(rootdist, median)), decreasing = T)
rootdist <- rootdist[names(rootdist_med)]
tfroots <- tfroots[names(rootdist_med)]
targetroots <- targetroots[names(rootdist_med)]
regulonlist <- regulonlist[names(rootdist_med)]

#-- Set regulon groups based on the median root distances
regulon_grouplist <- -sign(rootdist_med)+2
regulon_groupnames <- c("group_a","group_b","group_c")
regulon_groupcolors = c("#98d1f2","grey","#1c92d5")
names(regulon_groupcolors) <- regulon_groupnames
#-- (Figure S2) Generate a pie chart showing regulons grouped based on
#-- the median distance between a TF's root and its targets' roots
n <- as.numeric(table(regulon_grouplist))
pie(n, labels = paste(n,"regulons"), col = regulon_groupcolors, 
    border="white", cex=1.5, clockwise = TRUE, init.angle=0)
labs <- c("TF-target genes rooted before the TF (group-a)",
          "TF-target genes rooted with the TF (group-b)", 
          "TF-target genes rooted after the TF (group-c)")
legend("bottomleft", fill = regulon_groupcolors, bty = "n", legend = labs)

title Figure S2. Regulons grouped based on the median distance between a TF’s root and its targets’ roots.

#-- (Figure S3) Generate a boxplot showing individual regulons 
#-- sorted by the median distance to TF root
plot.new()
par(usr=c(c(0,length(rootdist)),range(rootdist)))
boxplot(rootdist, horizontal= F, outline=FALSE, las=2, axes=FALSE, add=T,
        pars = list(boxwex = 0.6, boxcol=regulon_groupcolors[regulon_grouplist], 
                    whiskcol=regulon_groupcolors[regulon_grouplist]),
        pch="|", lty=1, lwd=0.75,
        col = regulon_groupcolors[regulon_grouplist])
abline(h=0, lmitre=5, col="#E69F00", lwd=3, lt=2)
par(mgp=c(2,0.1,0))
axis(side=1, cex.axis=1.2, padj=0.5, hadj=0.5, las=1, lwd=1.5, tcl= -0.2)
par(mgp=c(2.5,1.2,0.5))
axis(side=2, cex.axis=1.2, padj=0.5, hadj=0.5, las=1, lwd=1.5, tcl= -0.2)
legend("topright",legend = labs, fill = regulon_groupcolors, bty = "n")
title(xlab = "Regulons sorted by the median distance to TF root", ylab = "Distance to TF root")

title Figure S3. Regulons sorted by the median distance to TF root.

Comparing transcription factors and transcription co-factors

Transcription co-factors (TcoFs) are critical determinants of TF activities. TcoFs do not bind directly to DNA, but influence the transcriptional regulation by forming protein complexes with TFs. Next we will compare these two classes of regulators using the same approach described by Trefflich et al. (2019), but now assessing the distribution of the evolutionary roots inferred for TFs and TcoFs. In order to run the subsequent snippets we will require the list of human TcoFs avaiable at the TcoF-DB Database (Schmeier et al. 2016) (please, download the ‘TcoF-DB.xlsx’ file as indicated below).

#-- Please, download the 'TcoF-DB.xlsx' file from
#-- https://tools.sschmeier.com/tcof/browse/?type=tcof&species=human&class=all
#-- and then load it with the 'read_excel' function
library(readxl)
TcoF_DB <- read_excel("TcoF-DB.xlsx")

#-- Select high-confidence TcoFs according to TcoF Database
TcoF_DB <- TcoF_DB[TcoF_DB$Type=="TcoF: class HC",]

#-- Map 'TcoF_DB' to 'roots_df'
roots_df_TcoF_DB <- roots_df
roots_df_TcoF_DB$TRN_element <- NA
roots_df_TcoF_DB$TRN_element[roots_df$SYMBOL %in% TcoF_DB$Symbol] <- "TcoF"
roots_df_TcoF_DB$TRN_element[roots_df$TRN_element%in%"TF"] <- "TF"
roots_df_TcoF_DB <- roots_df_TcoF_DB[!is.na(roots_df_TcoF_DB$TRN_element),]
table(roots_df_TcoF_DB$TRN_element)
## TcoF   TF
##  146  307
#-- Assess root distribution by TRN_element
wilcox.test(Root ~ TRN_element, data=roots_df_TcoF_DB)
## Wilcoxon rank sum test with continuity correction
## data:  Root by TRN_element
## W = 22226, p-value = 0.884
## alternative hypothesis: true location shift is not equal to 0
#-- (Figure S4) Generate violin plots showing root distribution by TRN_element
p <- ggplot(roots_df_TcoF_DB, aes(x=TRN_element, y=Root)) + 
  geom_violin(aes(fill=TRN_element), adjust=2, show.legend=F) +
  scale_y_continuous(breaks=roots, labels=paste("root",roots)) +
  scale_fill_manual(values=c("#c7eae5","#dfc27d")) +
  labs(x="TRN elements", y="Root distribution") +
  scale_x_discrete(limits=c("TF","TcoF"), labels=c("TFs","TcoFs")) +
  theme_classic() +
  theme(text=element_text(size=20)) + 
  stat_summary(fun.data = data_summary)
p + stat_compare_means(method="wilcox.test",
                       comparisons =list(c("TF","TcoF")),
                       label = "p.signif")

title Figure S4. Distribution of the inferred evolutionary roots of TFs and TcoFs (ns = not significant).

Exploring abundance, diversity and plasticity

In this section we show how to calculate the OG’s abundance, diversity and plasticity, and then map these three metrics to regulons (please, refer to Castro et al. (2008) and Dalmolin et al. (2011) for a detailed description). Briefly, the abundance metric represents the number of orthologs divided by the number of species annotated in a given OG; abundance = 1 indicates an one-to-one relationship between the number of orthologs and species, while abundance > 1 indicates that the number of orthologs exceeds the number of species. A large abundance value suggests a large number of paralogs annotated in the OG. The diversity metric represents the distribution of orthologs and paralogs in a given species tree; high diversity represents an homogeneous distribution (e.g. one ortholog in each species), while low diversity indicates that the orthologous genes are concentrated on few species (e.g. in a single branch of the species tree). The plasticity is the combination of abundance and diversity into a single metric. Low plasticity is observed in OGs of low abundance and high diversity (e.g. few orthologs distributed over many species), while high plasticity is observed in OGs of high abundance and low diversity (e.g. many orthologs concentrated on few species).

#-- Compute OG's abundance, diversity and plasticity
ogp <- gplast.preprocess(cogdata=cogdata, sspids=phyloTree$tip.label)
ogp <- gplast(ogp)
gpres <- gplast.get(ogp, what="results")
head(gpres)
##         abundance diversity plasticity
## COG0001    1.3871    0.6889     0.4150
## COG0002    1.1346    0.8110     0.2386
## COG0003    1.3243    0.9506     0.1739
## COG0004    4.1753    0.8880     0.5654
## COG0005    2.5455    0.9283     0.4182
## COG0006    4.3167    0.9769     0.5298
#-- Map OG's abundance, diversity and plasticity to the 'roots_df' data frame
idx <- match(roots_df$COGID,rownames(gpres))
roots_df$Abundance <- gpres$abundance[idx]
roots_df$Diversity <- gpres$diversity[idx]
roots_df$Plasticity <- gpres$plasticity[idx]

#-- Then map OG's abundance, diversity and plasticity to regulons
stats_df <- lapply(regulonlist, function(reg){
  temp <- roots_df[roots_df$ENTREZ%in%reg,]
  apply(temp[ , c("Abundance","Diversity","Plasticity")], 2, mean)
})
stats_df <- ldply(stats_df, .id="Regulon", stringsAsFactors=FALSE)
stats_df$regulon_groups <- regulon_grouplist[stats_df$Regulon]
stats_df$regulon_groups <- regulon_groupnames[stats_df$regulon_groups]
#-- (Figure S5a) Assess OG's abundance by regulon groups
p <- ggplot(stats_df, aes(x=regulon_groups, y=Abundance, fill=regulon_groups)) + 
  geom_boxplot(show.legend=F) +
  scale_y_continuous(limits = c(0,60)) +
  scale_x_discrete(limits=c("group_a","group_c")) +
  scale_fill_manual(values=regulon_groupcolors[c("group_a","group_c")]) +
  labs(x="Regulon groups", y="OG's abundance") +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=20), axis.line.x=element_blank())
p + stat_compare_means(method="wilcox.test",
                       comparisons =list(c("group_a","group_c")),
                       label = "p.signif")
#-- (Figure S5b) Assess OG's diversity by regulon groups
p <- ggplot(stats_df, aes(x=regulon_groups, y=Diversity, fill=regulon_groups)) + 
  geom_boxplot(show.legend=F) +
  scale_y_continuous(limits = c(0.5,1)) +
  scale_x_discrete(limits=c("group_a","group_c")) +
  scale_fill_manual(values=regulon_groupcolors[c("group_a","group_c")]) +
  labs(x="Regulon groups", y="OG's diversity") +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=20), axis.line.x=element_blank())
p + stat_compare_means(method="wilcox.test",
                       comparisons =list(c("group_a","group_c")),
                       label = "p.signif")
#-- (Figure S5c) Assess OG's plasticity by regulon groups
p <- ggplot(stats_df, aes(x=regulon_groups, y=Plasticity, fill=regulon_groups)) + 
  geom_boxplot(show.legend=F) +
  scale_y_continuous(limits = c(0,1)) +
  scale_x_discrete(limits=c("group_a","group_c")) +
  scale_fill_manual(values=regulon_groupcolors[c("group_a","group_c")]) +
  labs(x="Regulon groups", y="OG's plasticity") +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=20), axis.line.x=element_blank())
p + stat_compare_means(method="wilcox.test",
                       comparisons =list(c("group_a","group_c")),
                       label = "p.signif")

title Figure S5. OG’s abundance (a), diversity (b) and plasticity (c) mapped to regulons grouped based on the distance between the evolutionary roots of TFs and targets. ****P-value = 5.1e-5 (Wilcoxon-Mann-Whitney test); ns = not significant.

The abundance mapped to regulons whose TF-target genes are rooted before the TF (Group-a) is the same from that mapped to regulons whose TF-target genes are rooted after the TF (Group-c) (Figure S5a), suggesting that the number of orthologs per species is similar between the two groups. In contrast, the OG’s diversity mapped to Group-a is higher comparing with Group-c (P-value = 5.1e-5; Wilcoxon-Mann-Whitney test) (Figure S5b). As diversity estimates the dispersion of the orthologous genes in the species tree, this suggests that regulons in Group-a have orthologs more evenly distributed, which is usually observed for OGs rooted at the base of the phylogenetic tree (Castro et al. 2008). We did not detect any difference between Group-a and Group-c using the plasticity scores mapped to regulons (Figure S5c).

Evolutionary analysis of regulons generated from normal breast tissue samples

Regulons are constructed based on a gene’s expression varying across a cohort. Large cohorts of tumour samples typically contain multiple molecular subtypes, and typically provide good expression variability for building regulons. In contrast, sample sets that are more homogeneous may be more challenging to explore with regulons, and this may be the case with sets of normal, non-cancerous samples. Despite this challenging, Fletcher et al. (2013) generated regulons using normal breast tissue samples in order to observe regulatory differences between cancer and normal cells. Here we will run the same evolutionary analysis described in section 4, but now using regulons generated from normal breast tissue samples. In the next steps we show how to reproduce the previous results using a diferent TRN.

Mapping root-to-gene annotation

data("rtniNormals")
graph_normals  <- tni.graph(rtniNormals, gtype = "rmap")
graph_normals <- ogr2igraph(ogr, cogdata, graph_normals, idkey = "ENTREZ")
roots_df_normals <- data.frame(COGID = V(graph_normals)$COGID,
                       SYMBOL = V(graph_normals)$SYMBOL,
                       ENTREZ = V(graph_normals)$ENTREZ, 
                       Root = V(graph_normals)$Root,
                       TRN_element = c("Target","TF")[V(graph_normals)$tfs+1])
roots_df_normals <- roots_df_normals[complete.cases(roots_df_normals),]
roots_df_normals <- roots_df_normals[roots_df_normals$Root<max(roots_df_normals$Root),]
rownames(roots_df_normals) <- 1:nrow(roots_df_normals)
table(roots_df_normals$TRN_element)
## Target      TF
##   2818     130

Comparing regulators and targets

#-- Assess root distribution by TRN_element
wilcox.test(Root ~ TRN_element, data=roots_df_normals)
## Wilcoxon rank sum test with continuity correction
## data:  Root by TRN_element
## W = 152522, p-value = 0.001148
## alternative hypothesis: true location shift is not equal to 0
#-- Set roots to display in y-axis
roots <- c(4,8,11,13,19,21,25)

#-- Set a summary function to display dispersion within the violins
data_summary <- function(x) {
  y <- mean(x); ymin <- y-sd(x); ymax <- y+sd(x)
  return(c(y=y,ymin=ymin,ymax=ymax))
}

#-- (Figure S6) Generate violin plots showing root distribution by TRN_element
p <- ggplot(roots_df_normals, aes(x=TRN_element, y=Root)) + 
  geom_violin(aes(fill=TRN_element), adjust=2, show.legend=F) +
  scale_y_continuous(breaks=roots, labels=paste("root",roots)) +
  scale_fill_manual(values=c("#c7eae5","#dfc27d")) +
  labs(x="TRN elements", y="Root distribution") +
  scale_x_discrete(limits=c("TF","Target"), labels=c("TFs","Targets")) +
  theme_classic() +
  theme(text=element_text(size=20)) + 
  stat_summary(fun.data = data_summary)
p + stat_compare_means(method="wilcox.test",
                       comparisons =list(c("TF","Target")),
                       label = "p.signif")

title Figure S6. Distribution of the inferred evolutionary roots of TFs and target genes using regulons available from the rtniNormals data object. **P-value = 0.001148 (Wilcoxon-Mann-Whitney test).

Session information

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## 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       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] plyr_1.8.9                       ggpubr_0.6.0                    
##  [3] ggplot2_3.5.1                    Fletcher2013b_1.42.0            
##  [5] igraph_2.1.1                     RedeR_3.3.0                     
##  [7] Fletcher2013a_1.42.0             limma_3.63.2                    
##  [9] RTN_2.31.0                       geneplast.data.string.v91_0.99.6
## [11] geneplast_1.33.0                 BiocStyle_2.35.0                
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9                formatR_1.14               
##   [3] rlang_1.1.4                 magrittr_2.0.3             
##   [5] minet_3.65.0                matrixStats_1.4.1          
##   [7] e1071_1.7-16                compiler_4.4.2             
##   [9] vctrs_0.6.5                 pkgconfig_2.0.3            
##  [11] crayon_1.5.3                fastmap_1.2.0              
##  [13] backports_1.5.0             XVector_0.47.0             
##  [15] caTools_1.18.3              utf8_1.2.4                 
##  [17] rmarkdown_2.29              UCSC.utils_1.3.0           
##  [19] purrr_1.0.2                 xfun_0.49                  
##  [21] zlibbioc_1.52.0             cachem_1.1.0               
##  [23] GenomeInfoDb_1.43.2         jsonlite_1.8.9             
##  [25] DelayedArray_0.33.2         broom_1.0.7                
##  [27] parallel_4.4.2              R6_2.5.1                   
##  [29] bslib_0.8.0                 RColorBrewer_1.1-3         
##  [31] car_3.1-3                   GenomicRanges_1.59.1       
##  [33] jquerylib_0.1.4             Rcpp_1.0.13-1              
##  [35] SummarizedExperiment_1.37.0 knitr_1.49                 
##  [37] snow_0.4-4                  mixtools_2.0.0             
##  [39] VennDiagram_1.7.3           IRanges_2.41.1             
##  [41] viper_1.41.0                Matrix_1.7-1               
##  [43] splines_4.4.2               tidyselect_1.2.1           
##  [45] abind_1.4-8                 yaml_2.3.10                
##  [47] gplots_3.2.0                lattice_0.22-6             
##  [49] tibble_3.2.1                withr_3.0.2                
##  [51] Biobase_2.67.0              evaluate_1.0.1             
##  [53] pwr_1.3-0                   lambda.r_1.2.4             
##  [55] survival_3.7-0              futile.logger_1.4.3        
##  [57] proxy_0.4-27                kernlab_0.9-33             
##  [59] pillar_1.9.0                BiocManager_1.30.25        
##  [61] MatrixGenerics_1.19.0       carData_3.0-5              
##  [63] KernSmooth_2.23-24          stats4_4.4.2               
##  [65] plotly_4.10.4               generics_0.1.3             
##  [67] S4Vectors_0.45.2            munsell_0.5.1              
##  [69] scales_1.3.0                gtools_3.9.5               
##  [71] class_7.3-22                glue_1.8.0                 
##  [73] pheatmap_1.0.12             lazyeval_0.2.2             
##  [75] maketools_1.3.1             tools_4.4.2                
##  [77] sys_3.4.3                   data.table_1.16.2          
##  [79] ggsignif_0.6.4              buildtools_1.0.0           
##  [81] grid_4.4.2                  tidyr_1.3.1                
##  [83] ape_5.8                     colorspace_2.1-1           
##  [85] nlme_3.1-166                GenomeInfoDbData_1.2.13    
##  [87] Formula_1.2-5               cli_3.6.3                  
##  [89] futile.options_1.0.1        fansi_1.0.6                
##  [91] segmented_2.1-3             S4Arrays_1.7.1             
##  [93] viridisLite_0.4.2           dplyr_1.1.4                
##  [95] gtable_0.3.6                rstatix_0.7.2              
##  [97] sass_0.4.9                  digest_0.6.37              
##  [99] BiocGenerics_0.53.3         SparseArray_1.7.2          
## [101] htmlwidgets_1.6.4           htmltools_0.5.8.1          
## [103] lifecycle_1.0.4             httr_1.4.7                 
## [105] statmod_1.5.0               MASS_7.3-61

References

Castro, Mauro AA, Rodrigo JS Dalmolin, Jose CF Moreira, Jose CM Mombach, and Rita MC de Almeida. 2008. “Evolutionary Origins of Human Apoptosis and Genome-Stability Gene Networks.” Nucleic Acids Research 36 (19): 6269–83. https://doi.org/10.1093/nar/gkn636.
Curtis, C, S P Shah, S Chin, G Turashvili, O M Rueda, M J Dunning, D Speed, et al. 2012. “The Genomic and Transcriptomic Architecture of 2,000 Breast Tumours Reveals Novel Subgroups.” Nature 486 (7403): 346–52. https://doi.org/10.1038/nature10983.
Dalmolin, Rodrigo JS, Mauro AA Castro, Jose Rybarczyk-Filho, Luis Souza, Rita MC de Almeida, and Jose CF Moreira. 2011. “Evolutionary Plasticity Determination by Orthologous Groups Distribution.” Biology Direct 6 (1): 22. https://doi.org/10.1186/1745-6150-6-22.
Fletcher, Michael, Mauro Castro, Suet-Feung Chin, Oscar Rueda, Xin Wang, Carlos Caldas, Bruce Ponder, Florian Markowetz, and Kerstin Meyer. 2013. “Master Regulators of FGFR2 Signalling and Breast Cancer Risk.” Nature Communications 4: 2464. https://doi.org/10.1038/ncomms3464.
Schmeier, Sebastian, Tanvir Alam, Magbubah Essack, and Vladimir B. Bajic. 2016. “TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions.” Nucleic Acids Research 45 (D1): D145–50. https://doi.org/10.1093/nar/gkw1007.
Trefflich, Sheyla, Rodrigo JS Dalmolin, José M Ortega, and Mauro AA Castro. 2019. “Which Came First, the Transcriptional Regulator or Its Target Genes? An Evolutionary Perspective into the Construction of Eukaryotic Regulons.” Biochimica Et Biophysica Acta [under review].