bootstrap_prediction {scGPS} | R Documentation

## BootStrap runs for both scGPS training and prediction

### Description

ElasticNet and LDA prediction for each of all the subpopulations in the new mixed population after training the model for a subpopulation in the first mixed population. The number of bootstraps to be run can be specified.

### Usage

    bootstrap_prediction(
      nboots = 1,
      genes = genes,
      mixedpop1 = mixedpop1,
      mixedpop2 = mixedpop2,
      c_selectID = NULL,
      listData = list(),
      cluster_mixedpop1 = NULL,
      cluster_mixedpop2 = NULL,
      trainset_ratio = 0.5,
      LDA_run = TRUE,
      verbose = FALSE,
      log_transform = FALSE
    )

### Arguments

`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
`trainset_ratio` | a number specifying the proportion of cells to be part of the training subpopulation
`LDA_run` | logical, if the LDA prediction is added to compare to ElasticNet
`verbose` | a logical whether to display additional messages
`log_transform` | boolean whether log transform should be computed

### Value

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

### Author(s)

Quan Nguyen, 2017-11-25

### See Also

`bootstrap_parallel` for parallel options

### 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$Merged_unique
    cluster_mixedpop1 <- colData(mixedpop1)[,1]
    cluster_mixedpop2 <- colData(mixedpop2)[,1]
    c_selectID <- 2
    test <- bootstrap_prediction(nboots = 1, mixedpop1 = mixedpop1,
        mixedpop2 = mixedpop2, genes=genes, listData =list(),
        cluster_mixedpop1 = cluster_mixedpop1,
        cluster_mixedpop2 = cluster_mixedpop2, c_selectID = c_selectID)
    names(test)
    test$ElasticNetPredict
    test$LDAPredict

[Package scGPS version 1.8.0 Index]