bootstrap_parallel {scGPS} | R Documentation |
same as bootstrap_prediction, but with an multicore option
bootstrap_parallel( ncores = 4, nboots = 1, genes = genes, mixedpop1 = mixedpop1, mixedpop2 = mixedpop2, c_selectID, listData = list(), cluster_mixedpop1 = NULL, cluster_mixedpop2 = NULL )
ncores |
a number specifying how many cpus to be used for running |
nboots |
a number specifying how many bootstraps to be run |
genes |
a gene list to build the model |
mixedpop1 |
a SingleCellExperiment object from a mixed population for training |
mixedpop2 |
a SingleCellExperiment object from a target mixed population for prediction |
c_selectID |
the root cluster in mixedpop1 to becompared to clusters in mixedpop2 |
listData |
a |
cluster_mixedpop1 |
a vector of cluster assignment for mixedpop1 |
cluster_mixedpop2 |
a vector of cluster assignment for mixedpop2 |
a list
with prediction results written in to the index
out_idx
Quan Nguyen, 2017-11-25
day2 <- day_2_cardio_cell_sample mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters) day5 <- day_5_cardio_cell_sample mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters) genes <-training_gene_sample genes <-genes$Merged_unique #prl_boots <- bootstrap_parallel(ncores = 4, nboots = 1, genes=genes, # mixedpop1 = mixedpop2, mixedpop2 = mixedpop2, c_selectID=1, # listData =list()) #prl_boots[[1]]$ElasticNetPredict #prl_boots[[1]]$LDAPredict