predicting {scGPS} | R Documentation

## Main prediction function applying the optimal ElasticNet and LDA models

### Description

Predict a new mixed population after training the model for a subpopulation in the first mixed population. All subpopulations in the new target mixed population will be predicted, where each targeted subpopulation will have a transition score from the orginal subpopulation to the new subpopulation.

### Usage

    predicting(
      listData = NULL,
      cluster_mixedpop2 = NULL,
      mixedpop2 = NULL,
      out_idx = NULL,
      standardize = TRUE,
      LDA_run = FALSE,
      c_selectID = NULL,
      log_transform = FALSE
    )

### Arguments

`listData` | a `list` object containing trained results for the selected subpopulation in the first mixed population
`cluster_mixedpop2` | a vector of cluster assignment for mixedpop2
`mixedpop2` | a SingleCellExperiment object from the target mixed population of importance, e.g. differentially expressed genes that are most significant
`out_idx` | a number to specify index to write results into the list output. This is needed for running bootstrap.
`standardize` | a logical of whether to standardize the data
`LDA_run` | logical, if the LDA prediction is added to compare to ElasticNet, the LDA model needs to be trained from the training before inputting to this prediction step
`c_selectID` | a number to specify the trained cluster used for prediction
`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

### Examples

    c_selectID<-1
    out_idx<-1
    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
    listData  <- training(genes,
        cluster_mixedpop1 = colData(mixedpop1)[, 1], mixedpop1 = mixedpop1,
        mixedpop2 = mixedpop2, c_selectID, listData =list(), out_idx=out_idx)
    listData  <- predicting(listData =listData,  mixedpop2 = mixedpop2,
        out_idx=out_idx, cluster_mixedpop2 = colData(mixedpop2)[, 1],
        c_selectID = c_selectID)

[Package scGPS version 1.8.0 Index]