data-raw/pbmc_matrix_small.R

In rnabioco/clustifyR: Classifier for Single-cell RNA-seq Using Cell Clusters

library(Seurat)
library(tidyverse)
library(clustifyr)
library(usethis)

# follow seurat tutorial from https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
pbmc <- RunUMAP(pbmc, dims = 1:10)
new.cluster.ids <- c(
    "Naive CD4 T", "Memory CD4 T", "CD14+ Mono", "B", "CD8 T", "FCGR3A+ Mono",
    "NK", "DC", "Platelet"
)
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
pbmc <- StashIdent(pbmc, "classified")

pbmc_matrix <- pbmc@assays$RNA@data
pbmc_matrix_small <- pbmc_matrix[pbmc@assays$RNA@var.features, ]
usethis::use_data(pbmc_matrix_small, compress = "xz", overwrite = TRUE)