bootstrap_parallel {scGPS} | R Documentation

## BootStrap runs for both scGPS training and prediction with parallel option

### Description

same as bootstrap_prediction, but with an multicore option

### Usage

    bootstrap_parallel(ncores = 4, nboots = 1, genes = genes,
      mixedpop1 = mixedpop1, mixedpop2 = mixedpop2, c_selectID,
      listData = list(), cluster_mixedpop1 = NULL,
      cluster_mixedpop2 = NULL)

### Arguments

`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 `list` object, which contains trained results for the first mixed population
`cluster_mixedpop1` | a vector of cluster assignment for mixedpop1
`cluster_mixedpop2` | a vector of cluster assignment for mixedpop2

### Value

a `list` with prediction results written in to the index `out_idx`

### Author(s)

Quan Nguyen, 2017-11-25

### Examples

    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

[Package scGPS version 1.2.0 Index]