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

## ----eval = FALSE-------------------------------------------------------------
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# 
# BiocManager::install("SwarnSeq")

## ----Load_Package, include = FALSE--------------------------------------------
# Load the package
library(SwarnSeq)

## -----------------------------------------------------------------------------
library(SingleCellExperiment)
# Load the test data.
data(SwarnSeqToyData); data(SpikeInData)
data <- assays(SwarnSeqToyData)[[1]][1:20, c(1:50, 350:399)]
groups <- SwarnSeqToyData$groups[c(1:50, 350:399)]
clusters <- SwarnSeqToyData$clusters[c(1:50, 350:399)]
# Make the spike-in single cell experiment object like the following examples.
testData <- SingleCellExperiment(assays = list(counts = data), colData = data.frame(clusters = clusters, groups = groups))
SwarnAdjLRT_Results <- swarnAdjLrt(sce = testData,norm.method = "DEseq.norm", RNAspike.use = TRUE, spike_in_sce = SpikeInData)

## -----------------------------------------------------------------------------
SwarnClass <- swarnClassDe(results = SwarnAdjLRT_Results, alpha = 0.01)
swarnClass_result <- SwarnClass$SwarnClassDE
# Consider Top.DEG item from the following output list.
top_genes <- swarnTopTags(results = SwarnAdjLRT_Results, m = 10)
top_genes_result <- top_genes$Top.DEG

## -----------------------------------------------------------------------------
capeff <- capEff(sce = testData,CE.range = c(0.01, 0.05),RNAspike.use = TRUE, spike_in_sce = SpikeInData,method = "")
extractedAdjNormData <- extAdjNormData(sce=testData,norm.method = "log1p",CE.range = c(0.01,0.5))

## ----sessionInfo--------------------------------------------------------------
utils::sessionInfo()