inst/doc/NIMEB.R

In MEB: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq data

## ---- eval=FALSE--------------------------------------------------------------
#  install.packages("BiocManager")
#  BiocManager::install("MEB")

## -----------------------------------------------------------------------------
library(MEB)

## -----------------------------------------------------------------------------
data(sim_data_sp)
sim_data_sp

## -----------------------------------------------------------------------------
data(real_data_sp)
real_data_sp

## -----------------------------------------------------------------------------
data(sim_data_dsp)
sim_data_dsp

## -----------------------------------------------------------------------------
data(real_data_dsp)
real_data_dsp

## ---- message = FALSE, warning = FALSE----------------------------------------
library(SummarizedExperiment)

## -----------------------------------------------------------------------------
data(sim_data_sp)
gamma <- seq(1e-06,5e-05,1e-06)
sim_model_sp <- NIMEB(countsTable=assay(sim_data_sp), train_id=1:1000, gamma,
nu = 0.01, reject_rate = 0.05, ds = FALSE)

## -----------------------------------------------------------------------------
data(real_data_sp)
gamma <- seq(1e-06,5e-05,1e-06)
real_model_sp <- NIMEB(countsTable=assay(real_data_sp), train_id=1:530,
gamma, nu = 0.01, reject_rate = 0.1, ds = FALSE)

## -----------------------------------------------------------------------------
data(sim_data_dsp)
gamma <- seq(1e-07,2e-05,1e-06)
sim_model_dsp <- NIMEB(countsTable=assay(sim_data_dsp), train_id=1:1000, gamma,
nu = 0.01, reject_rate = 0.1, ds = TRUE)

## -----------------------------------------------------------------------------
data(real_data_dsp)
gamma <- seq(5e-08,5e-07,1e-08)
real_model_dsp <- NIMEB(countsTable=assay(real_data_dsp), train_id=1:143, gamma,
nu = 0.01, reject_rate = 0.1, ds = TRUE)

## -----------------------------------------------------------------------------
sim_model_sp_pred <- predict(sim_model_sp$model, assay(sim_data_sp))
summary(sim_model_sp_pred)