sc_example {clustree}

Simulated scRNA-seq dataset

Description

A simulated scRNA-seq dataset generated using the splatter package and clustered using the SC3 and Seurat packages.

Usage

sc_example

Format

sc_example is a list holding a simulated scRNA-seq dataset. Items in the list included the simulated counts, normalised log counts, tSNE dimensionality reduction and cell assignments from SC3 and Seurat clustering.

Source

# Simulation
library("splatter") # Version 1.2.1

sim <- splatSimulate(batchCells = 200, nGenes = 10000,
                     group.prob = c(0.4, 0.2, 0.2, 0.15, 0.05),
                     de.prob = c(0.1, 0.2, 0.05, 0.1, 0.05),
                     method = "groups", seed = 1)
sim_counts <- counts(sim)[1:1000, ]

# SC3 Clustering
library("SC3") # Version 1.7.6
library("scater") # Version 1.6.2

sim_sc3 <- SingleCellExperiment(assays = list(counts = sim_counts))
rowData(sim_sc3)$feature_symbol <- rownames(sim_counts)
sim_sc3 <- normalise(sim_sc3)
sim_sc3 <- sc3(sim_sc3, ks = 1:8, biology = FALSE, n_cores = 1)
sim_sc3 <- runTSNE(sim_sc3)

# Seurat Clustering
library("Seurat") # Version 2.2.0

sim_seurat <- CreateSeuratObject(sim_counts)
sim_seurat <- NormalizeData(sim_seurat, display.progress = FALSE)
sim_seurat <- FindVariableGenes(sim_seurat, do.plot = FALSE,
                                display.progress = FALSE)
sim_seurat <- ScaleData(sim_seurat, display.progress = FALSE)
sim_seurat <- RunPCA(sim_seurat, do.print = FALSE)
sim_seurat <- FindClusters(sim_seurat, dims.use = 1:6,
                           resolution = seq(0, 1, 0.1),
                           print.output = FALSE)

sc_example <- list(counts = counts(sim_sc3),
                   logcounts = logcounts(sim_sc3),
                   tsne = reducedDim(sim_sc3),
                   sc3_clusters = as.data.frame(colData(sim_sc3)),
                   seurat_clusters = [e-mail])