data-raw/pbmc_matrix_small.R

In NCBI-Hackathons/RClusterCT: 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)