LINKER_runPhase1 {TraRe} | R Documentation |
Run first phase of the linker method where K modules of similarly expressed target genes and
relate them to a linear combination of very few regulators, according to the selected model. LINKER_init()
evaluate kmeans on a train set to generate a initial set of clusters containing drivers and target genes.
LINKER_ReassignGenesToClusters()
reassigning genes based on closed match to new regulatory programs.
This functions takes place inside the linkerrun function, so it is not recommended to run it on its own.
LINKER_corrClust()
go through two steps within a loop, learning regulatory program of modules and reassigning
genes. LINKER_extract_modules()
extract all the modules, genes and relevant information. LINKER_EvaluateTestSet()
fits the selected model with the test data. LINKER_LearnRegulatoryPrograms()
learns the regulatory program of the modules.
LINKER_runPhase1( lognorm_est_counts, target_filtered_idx, regulator_filtered_idx, nassay = 1, regulator = "regulator", NrModules, Lambda = 1e-04, alpha = 1 - 1e-06, pmax = 10, mode = "VBSR", used_method = "MEAN", NrCores = 1, corrClustNrIter = 100, Nr_bootstraps = 1 ) LINKER_init( MA_matrix_Var, RegulatorData, NrModules, NrCores = 3, corrClustNrIter = 21, Parameters ) LINKER_ReassignGenesToClusters( Data, RegulatorData, Beta, Clusters, NrCores = 1 ) LINKER_corrClust(LINKERinit) LINKER_extract_modules(results) LINKER_EvaluateTestSet( LINKERresults, MA_Data_TestSet, RegulatorData_TestSet, used_method = "MEAN" ) LINKER_LearnRegulatoryPrograms( Data, Clusters, RegulatorData, Lambda, alpha, pmax, mode, used_method = "MEAN", NrCores = 1 )
lognorm_est_counts |
Matrix of log-normalized estimated counts of the gene expression data (Nr Genes x Nr samples) |
target_filtered_idx |
Index array of the target genes on the lognorm_est_counts matrix if SummarizedExperiment object is not provided. |
regulator_filtered_idx |
Index array of the regulatory genes on the lognorm_est_counts matrix if SummarizedExperiment object is not provided. |
nassay |
if SummarizedExperiment object is passed as input to lognorm_est_counts, name of the assay containing the desired matrix. Default: 1 (first item in assay's list). |
regulator |
if SummarizedExperiment object is passed as input to lognorm_est_counts, name of the rowData() variable to build target_filtered_idx and regulator_filtered_idx. This variable must be one for driver genes and zero for target genes. Default: 'regulator' |
NrModules |
Number of modules that are a priori to be found (note that the final number of modules discovered may differ from this value). By default, 100 modules. |
Lambda |
Lambda variable for Lasso models. |
alpha |
Alpha variable for Lasso models. |
pmax |
Maximum numbers of regulators that we want. |
mode |
Chosen method(s) to link module eigengenes to regulators. The available options are 'VBSR', 'LASSOmin', 'LASSO1se' and 'LM'. Default set to 'VBSR' |
used_method |
Method selected for use. Default set to MEAN. |
NrCores |
Nr of computer cores for the parallel parts of the method. Note that the parallelization is NOT initialized in any of the functions. By default, 2. |
corrClustNrIter |
Number of iteration for the phase I part of the method. |
Nr_bootstraps |
Number of bootstrap of Phase I. By default, 1. |
MA_matrix_Var |
Matrix of log-normalized estimated counts of the gene expression data, centered and scaled, containing only the train samples. |
RegulatorData |
Expression matrix containing only the regulators of the train samples. |
Parameters |
List of parameters containig lambda, pmax, alpha, mode and used method. |
Data |
Matrix of log-normalized estimated counts of the gene expression data, centered and scaled, containing only the train samples. |
Beta |
Coefficient on which the decision of reassigning genes is based. |
Clusters |
Clusters generated from the linkerinit function. |
LINKERinit |
Initialization object obtained from |
results |
Matrix of log-normalized estimated counts of the gene expression data (Nr Genes x Nr samples). |
LINKERresults |
List containing the number of clusters, regulatoryprogram, name of regulators and all genes and module membership. |
MA_Data_TestSet |
Matrix of log-normalized estimated counts of the gene expression data, centered and scaled, containing only the test samples. |
RegulatorData_TestSet |
Expression matrix containing only the regulators of the test samples. |
igraph object containing the modules containing the related drivers and targets within bootstraps.
## This example is very similar to the `LINKER_run()` function. ## Again, we are going to join drivers and targets genes to create the working dataset. drivers <- readRDS(paste0(system.file('extdata',package='TraRe'),'/tfs_linker_example.rds')) targets <- readRDS(paste0(system.file('extdata',package='TraRe'),'/targets_linker_example.rds')) lognorm_est_counts <- rbind(drivers,targets) ## We create the index for drivers and targets in the log-normalized gene expression matrix. R<-60 T<-200 regulator_filtered_idx <- seq_len(R) target_filtered_idx <- R+c(seq_len(T)) ## We recommend to use the default values of the function. ## For the sake of time, we will select faster (and worse) ones. linkeroutput <- LINKER_runPhase1(lognorm_est_counts,target_filtered_idx=target_filtered_idx, regulator_filtered_idx=regulator_filtered_idx, NrModules=2, mode='LASSOmin',NrCores=2, corrClustNrIter=10,Nr_bootstraps=1)