inst/doc/DEsingle.R

In DEsingle: DEsingle for detecting three types of differential expression in single-cell RNA-seq data

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  echo = TRUE,
  collapse = TRUE,
  comment = "#>"
)

## ----Installation from Bioconductor, eval = FALSE-----------------------------
#  if(!require(BiocManager)) install.packages("BiocManager")
#  BiocManager::install("DEsingle")

## ----Installation from GitHub, eval = FALSE-----------------------------------
#  if(!require(devtools)) install.packages("devtools")
#  devtools::install_github("miaozhun/DEsingle", build_vignettes = TRUE)

## ----Load DEsingle, eval = FALSE----------------------------------------------
#  library(DEsingle)

## ----Load TestData------------------------------------------------------------
library(DEsingle)
data(TestData)

## ----counts-------------------------------------------------------------------
dim(counts)
counts[1:6, 1:6]

## ----group--------------------------------------------------------------------
length(group)
summary(group)

## ----demo1, eval = FALSE------------------------------------------------------
#  # Load library and the test data for DEsingle
#  library(DEsingle)
#  data(TestData)
#  
#  # Specifying the two groups to be compared
#  # The sample number in group 1 and group 2 is 50 and 100 respectively
#  group <- factor(c(rep(1,50), rep(2,100)))
#  
#  # Detecting the DE genes
#  results <- DEsingle(counts = counts, group = group)
#  
#  # Dividing the DE genes into 3 categories at threshold of FDR < 0.05
#  results.classified <- DEtype(results = results, threshold = 0.05)

## ----demo2, eval = FALSE------------------------------------------------------
#  # Load library and the test data for DEsingle
#  library(DEsingle)
#  library(SingleCellExperiment)
#  data(TestData)
#  
#  # Convert the test data in DEsingle to SingleCellExperiment data representation
#  sce <- SingleCellExperiment(assays = list(counts = as.matrix(counts)))
#  
#  # Specifying the two groups to be compared
#  # The sample number in group 1 and group 2 is 50 and 100 respectively
#  group <- factor(c(rep(1,50), rep(2,100)))
#  
#  # Detecting the DE genes with SingleCellExperiment input sce
#  results <- DEsingle(counts = sce, group = group)
#  
#  # Dividing the DE genes into 3 categories at threshold of FDR < 0.05
#  results.classified <- DEtype(results = results, threshold = 0.05)

## ----extract DE, eval = FALSE-------------------------------------------------
#  # Extract DE genes at threshold of FDR < 0.05
#  results.sig <- results.classified[results.classified$pvalue.adj.FDR < 0.05, ]

## ----extract subtypes, eval = FALSE-------------------------------------------
#  # Extract three types of DE genes separately
#  results.DEs <- results.sig[results.sig$Type == "DEs", ]
#  results.DEa <- results.sig[results.sig$Type == "DEa", ]
#  results.DEg <- results.sig[results.sig$Type == "DEg", ]

## ----demo3, eval = FALSE------------------------------------------------------
#  # Load library
#  library(DEsingle)
#  
#  # Detecting the DE genes in parallelization
#  results <- DEsingle(counts = counts, group = group, parallel = TRUE)

## ----demo4, eval = FALSE------------------------------------------------------
#  # Load library
#  library(DEsingle)
#  library(BiocParallel)
#  
#  # Set the parameters and register the back-end to be used
#  param <- MulticoreParam(workers = 18, progressbar = TRUE)
#  register(param)
#  
#  # Detecting the DE genes in parallelization with 18 cores
#  results <- DEsingle(counts = counts, group = group, parallel = TRUE, BPPARAM = param)

## ----demo5, eval = FALSE------------------------------------------------------
#  # Load library
#  library(DEsingle)
#  library(BiocParallel)
#  
#  # Set the parameters and register the back-end to be used
#  param <- SnowParam(workers = 8, type = "SOCK", progressbar = TRUE)
#  register(param)
#  
#  # Detecting the DE genes in parallelization with 8 cores
#  results <- DEsingle(counts = counts, group = group, parallel = TRUE, BPPARAM = param)

## ----help1, eval = FALSE------------------------------------------------------
#  # Documentation for DEsingle
#  ?DEsingle

## ----help2, eval = FALSE------------------------------------------------------
#  # Documentation for DEtype
#  ?DEtype

## ----help3, eval = FALSE------------------------------------------------------
#  # Documentation for TestData
#  ?TestData
#  ?counts
#  ?group

## ----sessionInfo--------------------------------------------------------------
sessionInfo()