As described in Stuart*, Butler*, et al. Cell 2019, Seurat v3 introduces new methods for the integration of multiple single-cell datasets. These methods aim to identify shared cell states that are present across different datasets, even if they were collected from different individuals, experimental conditions, technologies, or even species.
Our method aims to first identify 'anchors' between pairs of datasets. These represent pairwise correspondences between individual cells (one in each dataset), that we hypothesize originate from the same biological state. These 'anchors' are then used to harmonize the datasets, or transfer information from one dataset to another. Below, we demonstrate multiple applications of integrative analysis, and also introduce new functionality beyond what was described in the 2019 manuscript. To help guide users, we briefly introduce these vignettes below:
Standard Workflow
SCTransform
Reference-based
Reciprocal PCA
In this example workflow, we demonstrate two new methods we recently introduced in our paper, Comprehensive Integration of Single Cell Data:
For the purposes of this example, we've chosen human pancreatic islet cell datasets produced across four technologies, CelSeq (GSE81076) CelSeq2 (GSE85241), Fluidigm C1 (GSE86469), and SMART-Seq2 (E-MTAB-5061). For convienence, we distribute this dataset through our SeuratData package.
The code for the new methodology is implemented in Seurat v3. You can download and install from CRAN with install.packages
.
install.packages("Seurat")
In addition to new methods, Seurat v3 includes a number of improvements aiming to improve the Seurat object and user interaction. To help users familiarize themselves with these changes, we put together a command cheat sheet for common tasks.
Load in the dataset. The metadata contains the technology (tech
column) and cell type annotations (celltype
column) for each cell in the four datasets.
library(Seurat)
library(SeuratData)
InstallData("panc8")
To construct a reference, we will identify 'anchors' between the individual datasets. First, we split the combined object into a list, with each dataset as an element.
data("panc8")
pancreas.list <- SplitObject(panc8, split.by = "tech")
pancreas.list <- pancreas.list[c("celseq", "celseq2", "fluidigmc1", "smartseq2")]
Prior to finding anchors, we perform standard preprocessing (log-normalization), and identify variable features individually for each. Note that Seurat v3 implements an improved method for variable feature selection based on a variance stabilizing transformation ("vst"
)
for (i in 1:length(pancreas.list)) {
pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE)
pancreas.list[[i]] <- FindVariableFeatures(pancreas.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
}
Next, we identify anchors using the FindIntegrationAnchors
function, which takes a list of Seurat objects as input. Here, we integrate three of the objects into a reference (we will use the fourth later in this vignette)
reference.list <- pancreas.list[c("celseq", "celseq2", "smartseq2")]
pancreas.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:30)
We then pass these anchors to the IntegrateData
function, which returns a Seurat object.
Assay
, which holds an integrated (or 'batch-corrected') expression matrix for all cells, enabling them to be jointly analyzed.pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, dims = 1:30)
After running IntegrateData
, the Seurat
object will contain a new Assay
with the integrated expression matrix. Note that the original (uncorrected values) are still stored in the object in the "RNA" assay, so you can switch back and forth.
We can then use this new integrated matrix for downstream analysis and visualization. Here we scale the integrated data, run PCA, and visualize the results with UMAP. The integrated datasets cluster by cell type, instead of by technology.
library(ggplot2)
library(cowplot)
library(patchwork)
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(pancreas.integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
pancreas.integrated <- ScaleData(pancreas.integrated, verbose = FALSE)
pancreas.integrated <- RunPCA(pancreas.integrated, npcs = 30, verbose = FALSE)
pancreas.integrated <- RunUMAP(pancreas.integrated, reduction = "pca", dims = 1:30)
p1 <- DimPlot(pancreas.integrated, reduction = "umap", group.by = "tech")
p2 <- DimPlot(pancreas.integrated, reduction = "umap", group.by = "celltype", label = TRUE,
repel = TRUE) + NoLegend()
p1 + p2