Kang_subset: Subset from the 'Kang18_8vs8()' object of the 'muscData'...

In distinct: distinct: a method for differential analyses via hierarchical permutation tests

Description

Subset from the 'Kang18_8vs8()' object of the muscData package.

Arguments

Kang_subset

contains a SingleCellExperiment object, representing a subset of 6 samples (3 individuals observed across 2 conditions) and 100 genes selected from the 'Kang18_8vs8()' object of the muscData package. Below the code used to subset the original dataset.

Author(s)

Simone Tiberi [e-mail]

See Also

distinct_test

Examples

#################### 
# Object 'Kang_subset' is generated as follows:
####################
# library(muscData)
# sce = Kang18_8vs8()
# 
# library(scater)
# sce = computeLibraryFactors(sce)
# sce = logNormCounts(sce)
# cpm(sce) <- calculateCPM(sce)
# 
# Select genes with at least 1000 non-zero cells:
# sce = sce[ rowSums(assays(sce)$counts > 0) >= 1000, ]
# 
# randomly select 100 of these genes:
# set.seed(61217)
# sel = sample( rownames(sce), size = 100)
# sce = sce[ rownames(sce) %in% sel, ]
# 
# select 3 individuals only:
# ind_selected = levels(factor(colData(sce)$ind))[1:3]
# sce = sce[, sce$ind %in% ind_selected]
# 
# make a sample_id column:
# colData(sce)$sample_id = factor(paste(colData(sce)$stim, colData(sce)$ind, sep = "_"))
# 
# create an experiment_info object containing sample-group information:
# experiment_info = unique(data.frame(sample_id = colData(sce)$sample_id, 
#                                     stim = colData(sce)$stim) )
# metadata(sce)$experiment_info = data.frame(experiment_info, row.names = NULL)
# 
# remove unnecessary information to reduce storage space:
# sce$cluster = NULL;
# sce$multiplets = NULL;
# rowData(sce) = NULL;
# colnames(sce) = NULL;
# reducedDim(sce) = NULL
# sce$ind = NULL
# sce$sizeFactor = NULL
# rm assays counts
# assays(sce) = assays(sce)[2:3]
#
# Kang_subset = sce
# save(Kang_subset, file = "Kang_subset.RData")

distinct documentation built on Nov. 8, 2020, 8:20 p.m.