predicting: Main prediction function applying the optimal ElasticNet and...

In IMB-Computational-Genomics-Lab/scGPS: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

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)

IMB-Computational-Genomics-Lab/scGPS documentation built on Dec. 6, 2020, 3:20 p.m.