In satijalab/seurat: Tools for Single Cell Genomics

all_times <- list()  # store the time for each chunk
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      now <<- Sys.time()
    } else {
      res <- difftime(Sys.time(), now, units = "secs")
      all_times[[options$label]] <<- res
    }
  }
}))
knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE,
  time_it = TRUE,
  error = TRUE
)

options(SeuratData.repo.use = "http://satijalab04.nygenome.org")

In this vignette, we will combine two 10X PBMC datasets: one containing 4K cells and one containing 8K cells. The datasets can be found here.

To start, we read in the data and create two Seurat objects.

library(Seurat)
pbmc4k.data <- Read10X(data.dir = "../data/pbmc4k/filtered_gene_bc_matrices/GRCh38/")
pbmc4k <- CreateSeuratObject(counts = pbmc4k.data, project = "PBMC4K")
pbmc4k

pbmc8k.data <- Read10X(data.dir = "../data/pbmc8k/filtered_gene_bc_matrices/GRCh38/")
pbmc8k <- CreateSeuratObject(counts = pbmc8k.data, project = "PBMC8K")
pbmc8k

Merging Two Seurat Objects

merge() merges the raw count matrices of two Seurat objects and creates a new Seurat object with the resulting combined raw count matrix. To easily tell which original object any particular cell came from, you can set the add.cell.ids parameter with an c(x, y) vector, which will prepend the given identifier to the beginning of each cell name. The original project ID will remain stored in object meta data under orig.ident

pbmc.combined <- merge(pbmc4k, y = pbmc8k, add.cell.ids = c('4K', '8K'), project = 'PBMC12K')
pbmc.combined

# notice the cell names now have an added identifier
head(colnames(pbmc.combined))
table(pbmc.combined$orig.ident)

Merging More Than Two Seurat Objects

To merge more than two Seurat objects, simply pass a vector of multiple Seurat objects to the y parameter for merge; we'll demonstrate this using the 4K and 8K PBMC datasets as well as our previously computed Seurat object from the 2,700 PBMC tutorial (loaded via the SeuratData package).

library(SeuratData)
InstallData("pbmc3k")
pbmc3k <- LoadData("pbmc3k", type = "pbmc3k.final")
pbmc3k

pbmc.big <- merge(pbmc3k, y = c(pbmc4k, pbmc8k), add.cell.ids = c('3K', '4K', '8K'), project = 'PBMC15K')
pbmc.big

head(colnames(pbmc.big))
tail(colnames(pbmc.big))
unique(sapply(X = strsplit(colnames(pbmc.big), split = '_'), FUN = '[', 1))
table(pbmc.big$orig.ident)

Merge Based on Normalized Data

By default, merge() will combine the Seurat objects based on the raw count matrices, erasing any previously normalized and scaled data matrices. If you want to merge the normalized data matrices as well as the raw count matrices, simply pass merge.data = TRUE. This should be done if the same normalization approach was applied to all objects.

pbmc4k <- NormalizeData(pbmc4k)
pbmc8k <- NormalizeData(pbmc8k)
pbmc.normalized <- merge(pbmc4k, y = pbmc8k, add.cell.ids = c('4K', '8K'), project = 'PBMC12K', merge.data = TRUE)
GetAssayData(pbmc.combined)[1:10, 1:15]
GetAssayData(pbmc.normalized)[1:10, 1:15]

write.csv(x = t(as.data.frame(all_times)), file = "../output/timings/merge_vignette_times.csv")

Session Info

sessionInfo()

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.