## ----LoadFunctions, echo=FALSE, message=FALSE, warning=FALSE, results='hide'----
library(knitr)
opts_chunk$set(error = FALSE)
library(TraRe)
## ----style, echo = FALSE, results = 'asis'------------------------------------
##BiocStyle::markdown()
## ---- eval=FALSE--------------------------------------------------------------
#
# if (!requireNamespace("BiocManager"))
# install.packages("BiocManager")
# BiocManager::install("TraRe")
#
## ---- eval=TRUE, warning=FALSE, collapse=TRUE---------------------------------
# For this example, we are going to join 70 drivers and 1079 targets from our package's folder.
lognorm_est_counts <- utils::read.delim(paste0(system.file("extdata",package="TraRe"),
'/VignetteFiles/expression_rewiring_vignette.txt'))
lognorm_est_counts <- as.matrix(lognorm_est_counts)
# Here we have two options: we can create the index and pass them to run LINKER method or
# we can work with Summarized experiments.
# In order to create index, in this example regulators are placed in the first 200 positions.
R <- 70
P <- 1079
regulator_filtered_idx <- seq_len(R)
target_filtered_idx <- R+seq(P)
# If a SummarizedExperiment is desired to be provided,
# the expression matrix should be saved as an assay and
# gene info should be located in SummarizedExperiment::rowData().
# From this object, target and regulators indexes will be automatically
# generated.
geneinfo <- utils::read.delim(paste0(system.file("extdata",package="TraRe"),
'/geneinfo_rewiring_example.txt'),row.names = 1)
# We have to make sure geneinfo and lognorm
# have same rows (same genes).
geneinfo <- geneinfo[rownames(lognorm_est_counts),,drop=FALSE]
# Now we can generate the SE object.
SeObject <- SummarizedExperiment::SummarizedExperiment(assays = list(counts = lognorm_est_counts),
rowData = geneinfo)
# We recommend VBSR.
# For the sake of time, we will be running only 1 bootstrap, but the following results
# will show a 10 bootstraps LINKER run.
#Regular expression matrix
#linkeroutput <- LINKER_run(lognorm_est_counts = lognorm_est_counts,
# target_filtered_idx = target_filtered_idx,
# regulator_filtered_idx = regulator_filtered_idx,link_mode="VBSR",
# graph_mode="VBSR",NrModules=100,Nr_bootstraps=1)
#SummarizedExperiment
linkeroutput <- LINKER_run(lognorm_est_counts = SeObject,link_mode="VBSR",
graph_mode="VBSR",NrModules=100,Nr_bootstraps=1)
## ----eval=TRUE,warning=FALSE,collapse=TRUE------------------------------------
# Assume we have run the rewiring method and we have discovered a rewired module.
# After we have selected the drivers and targets from that modules, we can build
# a single GRN to study it separately.
# Imagine our rewired module consists of 200 driver genes and 1000 target genes.
# We will be using same lognorm_est_counts we have already generated
# Same as before, we can provide indexes or built a SummarizedExperiment and pass it to NET_run()
# We will be using same indexes we have already generated.
# As SeObject has been already generated, we can directly provide it to NET_run()
# Here we rewind that, if not SummarizedExperiment is passed, lognorm_est_counts will
# be the expression matrix, and target_filtered_idx and regulator_filtered_idx wil be
# the ones we have just generated for the example.
# Again, we recommend VBSR
#Regular expression matrix
#graph <- NET_run(lognorm_est_counts = lognorm_est_counts,
# target_filtered_idx = target_filtered_idx,
# regulator_filtered_idx = regulator_filtered_idx,
# graph_mode="VBSR")
#SummarizedExperiment
graph <- NET_run(lognorm_est_counts = SeObject,graph_mode="VBSR")
## ---- eval=TRUE, warning=FALSE, collapse=TRUE---------------------------------
# In order to prepare the rewiring, we can provide individual file paths:
lognorm_est_counts_p <- paste0(system.file("extdata",package="TraRe"),
'/VignetteFiles/expression_rewiring_vignette.txt')
gene_info_p <- paste0(system.file("extdata",package="TraRe"),
'/geneinfo_rewiring_example.txt')
linker_output_p <- paste0(system.file("extdata",package="TraRe"),
'/VignetteFiles/linker_output_vignette.rds')
phenotype_p <- paste0(system.file("extdata",package="TraRe"),
'/phenotype_rewiring_example.txt')
# Or we can provide a SummarizedExperiment containing expression matrix,
# gene info (as rowData) and phenotype info (colData) plus the linker output.
# We are going to reload geneinfo, this time having old geneinfo rownames
# as a new column, as preparerewiring will place that column into rownames,
# so we dont have to use the row.names=1 in the read.delim
geneinfo <- utils::read.delim(gene_info_p)
# We again make sure they have same features (genes)
geneinfo <- geneinfo[geneinfo$uniq_isos%in%rownames(lognorm_est_counts),]
# We also load phenotype
phenotype<- utils::read.delim(phenotype_p)
# select only samples from the phenotype file.
lognorm_est_counts <- lognorm_est_counts[,phenotype$Sample.ID]
PrepareSEObject <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=lognorm_est_counts),
rowData = geneinfo,
colData = phenotype)
# This file is stored in the VignettesFiles TraRe's package folder.
PrepareSEObject_p <- paste0(system.file("extdata",package="TraRe"),
'/VignetteFiles/SEObject.rds')
# We can now generate the prepare rewiring file:
# With individual paths
#prepared <- preparerewiring(name="Vignette",linker_output_p = linker_output_p,
# lognorm_est_counts_p = lognorm_est_counts_p,gene_info_p = gene_info_p,
# phenotype_p=phenotype_p,final_signif_thresh=0.01)
# or with a SummarizedExperiment object
prepared <- preparerewiring(name="Vignette",linker_output_p = linker_output_p,
SEObject_p = PrepareSEObject_p,final_signif_thresh=0.01)
# NOTE: The reason why we are working with paths instead of files is that
# rewiring is allowed to compare more than 1 dataset, so it is easier
# to provide a vector of paths than a complex structure of files.
# This is, we can provide , for instance, a vector of 2 SummarizedExperiments
# that will be compared. SEObject_p <- c(SEObject1,SEObject2)..
# linker_output_p <- c(linker_output_p_1,linker_output_p_2)
## ----eval=TRUE,warning=FALSE,collapse=TRUE------------------------------------
# Assume we have run the rewiring method and the `NET_run()` method to generate the
# igraph object. We are going to generate and plot both layouts for the example.
# We are going to generate all the files we need except for the igraph object, which
# is included as an example file in this package.
# We load the igraph object that we generated from the `NET_run()` example.
# Note: the igraph object is inside the list `NET_run()` generates.
graph <- readRDS(paste0(system.file("extdata",package="TraRe"),
'/VignetteFiles/graph_netrun_vignette.rds'))
graph <- graph$graphs$VBSR
# We first generate the normal layout for the plot.
# We need the drivers and target names.
lognorm_est_counts <- utils::read.delim(paste0(system.file("extdata",package="TraRe"),
'/VignetteFiles/expression_rewiring_vignette.txt'))
drivers <- lognorm_est_counts[seq(70),]
targets <- lognorm_est_counts[70+seq(1079),]
# Note that the generated graph may not have the same drivers and targets we used
# for generating it, so we will extract those genes and check in the gene_info file
# if they are drivers or targets.
geneinfo <- utils::read.delim(paste0(system.file("extdata",package="TraRe"),
'/geneinfo_rewiring_example.txt'))
R<-intersect(geneinfo[geneinfo$regulator==1,1],names(igraph::V(graph)))
P<-intersect(geneinfo[geneinfo$regulator==0,1],names(igraph::V(graph)))
drivers_n <- rownames(drivers[R,])
targets_n <- rownames(targets[P,])
# As for this example we are working at gene level (we do not have transcripts inside genes),
# we wont need namehash parameter (see param `namehash`)
normal_layout <- return_layout(drivers_n,targets_n)
# We need to separate our expression matrix by a binary phenotype.
# This is what the clinical file is used for.
gnames <- c(drivers_n,targets_n)
expmat <-rbind(drivers,targets)
clinic <- utils::read.delim(paste0(system.file("extdata",package="TraRe"),
'/phenotype_rewiring_example.txt'))
expmat_R <- expmat[,clinic$Sample.ID[clinic$Class=='R']]
expmat_NR <- expmat[,clinic$Sample.ID[clinic$Class=='NR']]
# We now generate the phenotype layout and the `varfile` we need for this layout.
# (I leave here a way to generate)
varfile <- t(as.matrix(sapply(gnames,
function(x) c(stats::t.test(expmat_R[x,],expmat_NR[x,])$statistic,
if(x%in%drivers_n) 1 else 0))))
colnames(varfile)<-c("t-stat","is-regulator")
phenotype_layout <- return_layout_phenotype(drivers_n,targets_n,varfile)
plot_igraph(graph,mytitle="Normal Layout",titlecol="black",mylayout=normal_layout)
plot_igraph(graph,mytitle="Phenotype Layout",titlecol="black",mylayout=phenotype_layout)
## ----eval=TRUE,warning=FALSE,collapse=TRUE------------------------------------
## For this example, we are going to use a generated 'refinedsumm.rds' from the su2c dataset
## (see vignette of TraRe for more information), which is at the external data folder
## of this package.
gpath <- paste0(system.file("extdata",package="TraRe"),
'/refinedsumm.rds')
## We are going to use 1k driver genes from the TraRe's installation folder.
## For more information about generatecliques() please check the corresponding help page.
dataset<- readRDS(paste0(system.file("extdata",package="TraRe"),
'/tfs_linker_example_eg.rds'))
# As we have mentioned, by default it will write a 'grnsumm.xlsx' file to the temporary directory.
html_from_graph(gpath=gpath,dataset=dataset)