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| #(1) Setup paths and environment. | |
| library(BTR) | |
| library(doParallel) | |
| path='~/btr_output/' | |
| setwd(path) | |
| #Setting up for parallel processing. | |
| registerDoParallel(cores=4) | |
| #(2)Load data. | |
| data(krum_bmodel) | |
| data(krum_istate) | |
| data(wilson_raw_data) | |
| bmodel = initialise_model(krum_bmodel) | |
| istate = initialise_data(krum_istate) | |
| #(3)Filtering expression data. | |
| cdata = initialise_raw_data(wilson_raw_data, max_expr = 'low') #do not filter data at this stage. keep the whole matrix. | |
| 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 into the model. | |
| extra_genes = setdiff(colnames(wilson_raw_data), bmodel@target) | |
| grown_bmodel = grow_bmodel(extra_genes[c(19, 20)], bmodel) #genes to be added: ldb1, lmo2 | |
| #(5)Re-estimate initial state. | |
| #Since CMPs are upsteam of GMPs, and MEPs, the initial state of extra genes can be estimated from CMPs. | |
| tmp_istate = colMeans(cdata[grepl('cmp', rownames(cdata)), extra_genes[c(19, 20)]]) | |
| tmp_istate = matrix(round(tmp_istate), nrow=1, dimnames=list(1, names(tmp_istate))) | |
| grown_istate = cbind(istate, tmp_istate) | |
| grown_istate = initialise_data(grown_istate) | |
| result = model_train(cdata=fcdata, bmodel=grown_bmodel, istate=grown_istate, verbose=T) | |
| filename = 'krum_wilson_myeloid_grow_trained_bmodel.rda' | |
| save(result, file=filename) | |
| #Cleaning up. | |
| stopImplicitCluster() |