In drorberel/Bioc2mlr:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>")

library(dplyr)
library(magrittr)
library(purrr)
library(limma)

library(Bioc2mlr)
library(mlr)
library(mlrCPO)

Pre-processing: Feature-selection via limma filtering

A. SummarizedExperiment (SE)

data(Golub_Merge, package = 'golubEsets') # ExpressionSet 
smallG<-Golub_Merge[200:259,]
smallG

library(SummarizedExperiment)
smallG_SE<-makeSummarizedExperimentFromExpressionSet(smallG)

smallG_SE # from vignette V1
top_DE_analytes_present<-5

# functional:
task_SE_Functional<-Fun_SE_to_taskFunc(smallG_SE, param.Y.name = 'ALL.AML', param.covariates = NULL, param_positive_y_level = 'ALL', task_return_format = 'functional', task_type = 'classif') ## will work with either 1 or multiple assayS
task_SE_Functional


# non-functional:
## 1. directly, but into DF
extracted_DF_from_task_SE<-getTaskData(task_SE_Functional, functionals.as = "dfcols") # keep matrix
extracted_DF_from_task_SE %>% str


## 2. Fun_SE_to_taskFunc(..., task_return_format = 'dfcols')
task_SE_NON_Functional<-Fun_SE_to_taskFunc(smallG_SE, param.Y.name = 'ALL.AML', param.covariates = NULL, param_positive_y_level = 'ALL', task_return_format = 'dfcols', task_type = 'classif') ## will work with either 1 or multiple assayS

Direct (limma)

design<-cbind(Grp1=1, Grp2vs1=smallG_SE$ALL.AML)
fit<-lmFit(assay(smallG_SE), design)
fit<-eBayes(fit) #  Moderated t-statistic
SE_topDE_limma<-topTable(fit, coef=2, number = top_DE_analytes_present)
SE_topDE_limma

mlr

Wrapper (JDRF version)

CPO (univ only)

## cpoFilterUnivariate() may NOT implement functional! even though functional should automatically transfered to non-functional !!! check!!!

task_SE_NON_Functional_scaled<-task_SE_NON_Functional %>>% cpoScale() 

SE_topDE_CPO_univ<-task_SE_NON_Functional %>>% cpoFilterUnivariate(abs = top_DE_analytes_present) 
SE_topDE_CPO_univ

#SE_topDE_CPO_univ<-task_SE_Functional %>>% cpoFilterUnivariate(abs = top_DE_analytes_present) 
#SE_topDE_CPO_univ

#SE_topDE_CPO_limma<-