Nothing
## ----options, include=FALSE, echo=FALSE---------------------------------------
knitr::opts_chunk$set(warning=FALSE, error=FALSE, message=FALSE)
## ---- eval= FALSE-------------------------------------------------------------
# if (!requireNamespace("BiocManager", quietly=TRUE)){
# install.packages("BiocManager")}
# BiocManager::install("ExperimentSubset")
## ---- eval = FALSE------------------------------------------------------------
# library(devtools)
# install_github("campbio/ExperimentSubset")
## -----------------------------------------------------------------------------
library(ExperimentSubset)
## -----------------------------------------------------------------------------
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(list(counts = counts))
es <- ExperimentSubset(sce)
es
## -----------------------------------------------------------------------------
es <- createSubset(es,
subsetName = "subset1",
rows = c(1:2),
cols = c(1:5),
parentAssay = "counts")
es
## -----------------------------------------------------------------------------
subset1Assay <- assay(es, "subset1")
subset1Assay[,] <- subset1Assay[,] + 1
es <- storeSubset(es,
subsetName = "subset1",
inputMatrix = subset1Assay,
subsetAssayName = "subset1Assay")
es
## -----------------------------------------------------------------------------
subsetSummary(es)
## -----------------------------------------------------------------------------
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(list(counts = counts))
es <- ExperimentSubset(sce)
subsetSummary(es)
## -----------------------------------------------------------------------------
es <- createSubset(es,
subsetName = "subset1",
rows = c(1:5),
cols = c(1:5),
parentAssay = "counts")
subsetSummary(es)
## -----------------------------------------------------------------------------
es <- createSubset(es,
subsetName = "subset2",
rows = c(1:2),
cols = c(1:5),
parentAssay = "subset1")
subsetSummary(es)
## -----------------------------------------------------------------------------
subset2Assay <- assay(es, "subset2")
subset2Assay[,] <- subset2Assay[,] + 1
## -----------------------------------------------------------------------------
#approach 1
es <- storeSubset(es,
subsetName = "subset2",
inputMatrix = subset2Assay,
subsetAssayName = "subset2Assay_a1")
#approach 2
assay(es, "subset2", subsetAssayName = "subset2Assay_a2") <- subset2Assay
subsetSummary(es)
## -----------------------------------------------------------------------------
altExp(x = es,
e = "subset2_alt1",
subsetName = "subset2") <- SingleCellExperiment(assay = list(
counts = assay(es, "subset2")
))
## -----------------------------------------------------------------------------
subsetSummary(es)
## ---- eval = FALSE------------------------------------------------------------
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install(version = "3.11", ask = FALSE)
# BiocManager::install(c("TENxPBMCData", "scater", "scran"))
## ---- eval = FALSE------------------------------------------------------------
# library(ExperimentSubset)
# library(TENxPBMCData)
# library(scater)
# library(scran)
## ---- eval = FALSE------------------------------------------------------------
# tenx_pbmc4k <- TENxPBMCData(dataset = "pbmc4k")
# es <- ExperimentSubset(tenx_pbmc4k)
# subsetSummary(es)
## ---- eval = FALSE------------------------------------------------------------
# perCellQCMetrics <- perCellQCMetrics(assay(es, "counts"))
# colData(es) <- cbind(colData(es), perCellQCMetrics)
## ---- eval = FALSE------------------------------------------------------------
# filteredCellsIndices <- which(colData(es)$sum > 1500)
# es <- createSubset(es, "filteredCells", cols = filteredCellsIndices, parentAssay = "counts")
# subsetSummary(es)
## ---- eval = FALSE------------------------------------------------------------
# assay(es, "filteredCells", subsetAssayName = "filteredCellsNormalized") <- normalizeCounts(assay(es, "filteredCells"))
# subsetSummary(es)
## ---- eval = FALSE------------------------------------------------------------
# topHVG1000 <- getTopHVGs(modelGeneVar(assay(es, "filteredCellsNormalized")), n = 1000)
# es <- createSubset(es, "hvg1000", rows = topHVG1000, parentAssay = "filteredCellsNormalized")
# subsetSummary(es)
## ---- eval = FALSE------------------------------------------------------------
# reducedDim(es, type = "PCA", subsetName = "hvg1000") <- calculatePCA(assay(es, "hvg1000"))
## ---- eval = FALSE------------------------------------------------------------
# subsetSummary(es)
## -----------------------------------------------------------------------------
sessionInfo()
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