data-raw/cbmc_ref.R

In NCBI-Hackathons/RClusterCT: Classifier for Single-cell RNA-seq Using Cell Clusters

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

# following seurat tutorial from https://satijalab.org/seurat/v3.0/multimodal_vignette.html#identify-differentially-expressed-proteins-between-clusters
cbmc.rna <- as.sparse(read.csv(
  file = "GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz",
  sep = ",",
  header = TRUE,
  row.names = 1
))
cbmc.rna <- CollapseSpeciesExpressionMatrix(cbmc.rna)
cbmc.adt <- as.sparse(read.csv(
  file = "GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz",
  sep = ",",
  header = TRUE,
  row.names = 1
))
cbmc.adt <- cbmc.adt[
  setdiff(rownames(x = cbmc.adt), c("CCR5", "CCR7", "CD10")),
]
cbmc <- CreateSeuratObject(counts = cbmc.rna)
cbmc <- NormalizeData(cbmc)
cbmc <- FindVariableFeatures(cbmc)
cbmc <- ScaleData(cbmc)
cbmc <- RunPCA(cbmc, verbose = FALSE)
cbmc <- FindNeighbors(cbmc, dims = 1:25)
cbmc <- FindClusters(cbmc, resolution = 0.8)
cbmc <- RunTSNE(cbmc, dims = 1:25, method = "FIt-SNE")
new.cluster.ids <- c(
  "Memory CD4 T",
  "CD14+ Mono",
  "Naive CD4 T",
  "NK",
  "CD14+ Mono",
  "Mouse",
  "B",
  "CD8 T",
  "CD16+ Mono",
  "T/Mono doublets",
  "NK",
  "CD34+",
  "Multiplets",
  "Mouse",
  "Eryth",
  "Mk",
  "Mouse",
  "DC",
  "pDCs"
)
names(new.cluster.ids) <- levels(cbmc)
cbmc <- RenameIdents(cbmc, new.cluster.ids)
cbmc[["ADT"]] <- CreateAssayObject(counts = cbmc.adt)
cbmc <- NormalizeData(cbmc, assay = "ADT", normalization.method = "CLR")
cbmc <- ScaleData(cbmc, assay = "ADT")
cbmc <- subset(cbmc, idents = c("Multiplets", "Mouse"), invert = TRUE)
DefaultAssay(cbmc) <- "ADT"
cbmc <- RunPCA(
  cbmc,
  features = rownames(cbmc),
  reduction.name = "pca_adt",
  reduction.key = "pca_adt_",
  verbose = FALSE
)
adt.data <- GetAssayData(cbmc, slot = "data")
adt.dist <- dist(t(adt.data))
cbmc[["rnaClusterID"]] <- Idents(cbmc)
cbmc[["tsne_adt"]] <- RunTSNE(
  adt.dist,
  assay = "ADT",
  reduction.key = "adtTSNE_"
)
cbmc[["adt_snn"]] <- FindNeighbors(adt.dist)$snn
cbmc <- FindClusters(cbmc, resolution = 0.2, graph.name = "adt_snn")
new.cluster.ids <- c(
  "CD4 T",
  "CD14+ Mono",
  "NK",
  "B",
  "CD8 T",
  "NK",
  "CD34+",
  "T/Mono doublets",
  "CD16+ Mono",
  "pDCs",
  "B"
)
names(new.cluster.ids) <- levels(cbmc)
cbmc <- RenameIdents(cbmc, new.cluster.ids)
cbmc[["citeID"]] <- Idents(cbmc)

m <- cbmc@meta.data %>%
  rownames_to_column("rn") %>%
  mutate(
    ID = ifelse(
      citeID != "CD8 T" & citeID != "CD4 T",
      as.character(rnaClusterID),
      as.character(citeID)
    )
  ) %>%
  mutate(
    ID = ifelse(
      (rnaClusterID == "CD4 T" & citeID != "CD4 T") |
        (rnaClusterID == "CD8 T" & citeID != "CD8 T"),
      "Unknown",
      as.character(ID)
    )
  ) %>%
  column_to_rownames("rn")
cbmc@meta.data <- m

cbmc_refm <- use_seurat_comp(
  cbmc,
  cluster_col = "ID",
  var_genes_only = FALSE,
  assay_name = NULL
)

cbmc_ref <- as.data.frame(cbmc_refm) %>%
  as_tibble(rownames = "gene") %>%
  select(-Unknown, -`T/Mono doublets`) %>%
  filter(str_sub(gene, 1, 5) != "MOUSE" & str_sub(gene, 1, 5) != "ERCC_") %>%
  column_to_rownames("gene") %>%
  as.matrix()

cbmc_ref <- cbmc_ref[Matrix::rowSums(cbmc_ref) != 0, ]

var_genes <- ref_feature_select(cbmc_ref, n = 2000)
cbmc_ref <- cbmc_ref[var_genes, ]

usethis::use_data(cbmc_ref, compress = "xz", overwrite = TRUE)