summary_deviance: get percent deviance explained for Lasso model, from 'n'...

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

the training results from training were written to the object LSOLDA_dat, the summary_devidance summarises deviance 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

Usage

summary_deviance(object = NULL)

Arguments

object

is a list containing the training results from training

Value

a list containing three elements, with a vector of percent maximum deviance explained, a dataframe containg information for the full deviance, and a dataframe containing gene names and coefficients of the best model

Author(s)

Quan Nguyen, 2017-11-25

Examples

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 = 2,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_deviance(LSOLDA_dat)

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