Nothing
## ---- echo=FALSE---------------------------------------------------------
library('BTR')
## ---- eval = FALSE-------------------------------------------------------
# install.packages('BTR')
## ---- eval = FALSE-------------------------------------------------------
# install.packages('devtools')
# devtools::install_github("cheeyeelim/BTR", ref="main")
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# data(wilson_raw_data)
# round(wilson_raw_data[1:5,1:5], 4)
## ---- echo=FALSE---------------------------------------------------------
data(wilson_raw_data)
knitr::kable(round(wilson_raw_data[1:5,1:5], 4))
## ---- eval=FALSE---------------------------------------------------------
# edata = initialise_raw_data(wilson_raw_data, max_expr='low') #max_expr='low' because this is qPCR data.
## ---- eval=FALSE---------------------------------------------------------
# data(krum_bmodel)
# head(krum_bmodel)
## ---- echo=FALSE---------------------------------------------------------
data(krum_bmodel)
knitr::kable(head(krum_bmodel))
## ---- eval=FALSE---------------------------------------------------------
# bmodel = initialise_model(krum_bmodel)
## ---- eval=FALSE---------------------------------------------------------
# data(krum_istate)
# head(krum_istate)
## ---- echo=FALSE---------------------------------------------------------
data(krum_istate)
knitr::kable(head(krum_istate))
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# set.seed(0) #use to ensure reproducibility. remove in actual use.
#
# #(1) Setup paths and environment.
# library(BTR)
#
# #If intending to use parallel processing, uncomment the following lines.
# #library(doParallel)
# #num_core = 4 #specify the number of cores to be used.
# #doParallel::registerDoParallel(cores=num_core)
#
# #(2) Load data.
# data(wilson_raw_data) #load a data frame of expression data.
# cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low')
#
# #(3) Filter cell types.
# cell_ind = grepl('cmp', rownames(cdata)) | grepl('gmp', rownames(cdata)) | grepl('mep', rownames(cdata))
# fcdata = cdata[cell_ind,] #select only relevant cells.
#
# #(4) Filter genes.
# gene_ind = c('fli1', 'gata1', 'gata2', 'gfi1', 'scl', 'sfpi1') #select genes to be included.
# fcdata = fcdata[, gene_ind]
#
# #(5) Inferring Boolean model.
# final_model = model_train(cdata=fcdata, max_varperrule=3, verbose=T)
#
# #(6) Visualise the Boolean model generated.
# plotBM(final_model)
## ---- tidy=TRUE----------------------------------------------------------
set.seed(0) #use to ensure reproducibility. remove in actual use.
#(1) Setup paths and environment.
library(BTR)
#If intending to use parallel processing, uncomment the following lines.
#library(doParallel)
#num_core = 4 #specify the number of cores to be used.
#doParallel::registerDoParallel(cores=num_core)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(2) Load data.
# data(wilson_raw_data) #load a data frame of expression data.
# cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low')
## ---- tidy=TRUE , eval=FALSE, tidy=TRUE----------------------------------
# #(3) Filter cell types.
# cell_ind = grepl('cmp', rownames(cdata)) | grepl('gmp', rownames(cdata)) | grepl('mep', rownames(cdata))
# fcdata = cdata[cell_ind,] #select only relevant cells.
#
# #(4) Filter genes.
# gene_ind = c('fli1', 'gata1', 'gata2', 'gfi1', 'scl', 'sfpi1') #select genes to be included.
# fcdata = fcdata[, gene_ind]
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(5) Inferring Boolean model.
# final_model = model_train(cdata=fcdata, max_varperrule=3, verbose=T)
#
# #(6) Visualise the Boolean model generated.
# plotBM(final_model)
## ---- echo=FALSE, fig.show='hold', message=FALSE, dpi=75, fig.width=6, fig.height=6----
data(example_models)
plotBM(emodel1)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# set.seed(0) #use to ensure reproducibility. remove in actual use.
#
# #(1) Setup paths and environment.
# library(BTR)
#
# #If intending to use parallel processing, uncomment the following lines.
# #library(doParallel)
# #num_core = 4 #specify the number of cores to be used.
# #doParallel::registerDoParallel(cores=num_core)
#
# #(2) Load data.
# data(krum_bmodel) #load a data frame of Boolean model.
# data(krum_istate) #load a data frame of initial state.
# data(wilson_raw_data) #load a data frame of expression data.
#
# bmodel = initialise_model(krum_bmodel)
# istate = krum_istate
# cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low')
#
# #(3) Filter cell types.
# cell_ind = grepl('cmp', rownames(cdata)) | grepl('gmp', rownames(cdata)) | grepl('mep', rownames(cdata))
# fcdata = cdata[cell_ind,] #select only relevant cells.
#
# #(4) Inferring Boolean model.
# final_model = model_train(cdata=fcdata, bmodel=bmodel, istate=istate, max_varperrule=3, verbose=T)
#
# #(5) Visualise the Boolean model generated.
# plotBM(final_model)
## ---- tidy=TRUE----------------------------------------------------------
set.seed(0) #use to ensure reproducibility. remove in actual use.
#(1) Setup paths and environment.
library(BTR)
#If intending to use parallel processing, uncomment the following lines.
#library(doParallel)
#num_core = 4 #specify the number of cores to be used.
#doParallel::registerDoParallel(cores=num_core)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(2) Load data.
# #(2) Load data.
# data(krum_bmodel) #load a data frame of Boolean model.
# data(krum_istate) #load a data frame of initial state.
# data(wilson_raw_data) #load a data frame of expression data.
#
# bmodel = initialise_model(krum_bmodel)
# istate = krum_istate
# cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low')
## ---- tidy=TRUE , eval=FALSE, tidy=TRUE----------------------------------
# #(3) Filter cell types.
# cell_ind = grepl('cmp', rownames(cdata)) | grepl('gmp', rownames(cdata)) | grepl('mep', rownames(cdata))
# fcdata = cdata[cell_ind,] #select only relevant cells.
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(4) Inferring Boolean model.
# final_model = model_train(cdata=fcdata, bmodel=bmodel, istate=istate, max_varperrule=3, verbose=T)
#
# #(5) Visualise the Boolean model generated.
# plotBM(final_model)
## ---- echo=FALSE, fig.show='hold', message=FALSE, dpi=75, fig.width=6, fig.height=6----
data(example_models)
plotBM(emodel2)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# set.seed(0) #use to ensure reproducibility. remove in actual use.
#
# #(1) Setup paths and environment.
# library(BTR)
#
# #If intending to use parallel processing, uncomment the following lines.
# #library(doParallel)
# #num_core = 4 #specify the number of cores to be used.
# #doParallel::registerDoParallel(cores=num_core)
#
# #(2) Load data.
# data(krum_bmodel) #load a data frame of Boolean model.
# data(krum_istate) #load a data frame of initial state.
# data(wilson_raw_data) #load a data frame of expression data.
#
# bmodel = initialise_model(krum_bmodel)
# istate = krum_istate
# cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low')
#
# #(3) Filter cell types.
# cell_ind = grepl('cmp', rownames(cdata)) | grepl('gmp', rownames(cdata)) | grepl('mep', rownames(cdata))
# fcdata = cdata[cell_ind,] #select only relevant cells.
#
# #(4) Adding extra genes to the initial Boolean model.
# #extra_genes = setdiff(colnames(wilson_raw_data), bmodel@target) #to view available genes to be added.
# #print(extra_genes) #to view available genes to be added.
# add_gene = 'ldb1' #genes to be added: ldb1
# grown_bmodel = grow_bmodel(add_gene, bmodel)
#
# #(5) Estimating initial state for the extra genes. (estimating from CMPs)
# tmp_istate = mean(cdata[grepl('cmp', rownames(cdata)), add_gene])
# tmp_istate = matrix(round(tmp_istate), nrow=1)
# colnames(tmp_istate) = add_gene
# grown_istate = cbind(istate, tmp_istate)
# grown_istate = initialise_data(grown_istate)
#
# #(6) Inferring Boolean model.
# final_model = model_train(cdata=fcdata, bmodel=grown_bmodel, istate=grown_istate, verbose=T)
#
# #(7) Visualise the Boolean model generated.
# plotBM(final_model)
## ---- tidy=TRUE----------------------------------------------------------
set.seed(0) #use to ensure reproducibility. remove in actual use.
#(1) Setup paths and environment.
library(BTR)
#If intending to use parallel processing, uncomment the following lines.
#library(doParallel)
#num_core = 4 #specify the number of cores to be used.
#doParallel::registerDoParallel(cores=num_core)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(2) Load data.
# data(krum_bmodel) #load a data frame of Boolean model.
# data(krum_istate) #load a data frame of initial state.
# data(wilson_raw_data) #load a data frame of expression data.
#
# bmodel = initialise_model(krum_bmodel)
# istate = krum_istate
# cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low')
## ---- tidy=TRUE , eval=FALSE---------------------------------------------
# #(3) Filter cell types.
# cell_ind = grepl('cmp', rownames(cdata)) | grepl('gmp', rownames(cdata)) | grepl('mep', rownames(cdata))
# fcdata = cdata[cell_ind,] #select only relevant cells.
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(4) Adding extra genes to the initial Boolean model.
# #extra_genes = setdiff(colnames(wilson_raw_data), bmodel@target)
# #print(extra_genes) #to view available genes to be added.
# add_gene = 'ldb1' #genes to be added: ldb1
# grown_bmodel = grow_bmodel(add_gene, bmodel)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(5) Estimating initial state for the extra genes. (estimating from CMPs)
# tmp_istate = mean(cdata[grepl('cmp', rownames(cdata)), add_gene])
# tmp_istate = matrix(round(tmp_istate), nrow=1)
# colnames(tmp_istate) = add_gene
# grown_istate = cbind(istate, tmp_istate)
# grown_istate = initialise_data(grown_istate)
## ---- eval=FALSE, tidy=TRUE----------------------------------------------
# #(6) Inferring Boolean model.
# final_model = model_train(cdata=fcdata, bmodel=grown_bmodel, istate=grown_istate, verbose=T)
#
# #(7) Visualise the Boolean model generated.
# plotBM(final_model)
## ---- echo=FALSE, fig.show='hold', message=FALSE, dpi=75, fig.width=6, fig.height=6----
data(example_models)
plotBM(emodel3)
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