| summary_prediction_lda {scGPS} | R Documentation |
n bootstrapsthe training results from training were written to
the object LSOLDA_dat, the summary_prediction summarises
prediction explained for n bootstrap runs and also returns the best
deviance matrix for plotting, as well as the best matrix with Lasso genes
and coefficients
summary_prediction_lda(LSOLDA_dat = NULL, nPredSubpop = NULL)
LSOLDA_dat |
is a list containing the training results from
|
nPredSubpop |
is the number of subpopulations in the target mixed population |
a dataframe containg information for the LDA prediction results, each column contains prediction results for all subpopulations from each bootstrap run
c_selectID<-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
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1,
mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
cluster_mixedpop1 = colData(mixedpop1)[,1],
cluster_mixedpop2=colData(mixedpop2)[,1])
summary_prediction_lda(LSOLDA_dat=LSOLDA_dat, nPredSubpop=4)