1 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.

2 Package installation and data sets

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

#-- Call packages

3 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 '' package

#-- 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)

4 Evolutionary analysis of regulons generated from breast cancer samples

4.1 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
## 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
## Target      TF
##   6308     307

4.2 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.

##      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
##          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)

#-- (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( = 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){

#-- Compute root distances between a TF and its targets
rootdist <- sapply(names(targetroots), function(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
boxplot(rootdist, horizontal= F, outline=FALSE, las=2, axes=FALSE, add=T,
        pars = list(boxwex = 0.6, boxcol=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)
axis(side=1, cex.axis=1.2, padj=0.5, hadj=0.5, las=1, lwd=1.5, tcl= -0.2)
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")