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). We provide a combined raw data matrix and associated metadata file here to get started.
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 expression matrix and metadata. The metadata file contains the technology (tech
column) and cell type annotations (cell type
column) for each cell in the four datasets.
library(Seurat)
pancreas.data <- readRDS(file = "../data/pancreas_expression_matrix.rds")
metadata <- readRDS(file = "../data/pancreas_metadata.rds")
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.
pancreas <- CreateSeuratObject(pancreas.data, meta.data = metadata)
pancreas.list <- SplitObject(pancreas, split.by = "tech")
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)
# 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()
plot_grid(p1, p2)
Seurat v3 also supports the projection of reference data (or meta data) onto a query object. While many of the methods are conserved (both procedures begin by identifying anchors), there are two important distinctions between data transfer and integration:
After finding anchors, we use the TransferData
function to classify the query cells based on reference data (a vector of reference cell type labels). TransferData
returns a matrix with predicted IDs and prediction scores, which we can add to the query metadata.
pancreas.query <- pancreas.list[["fluidigmc1"]]
pancreas.anchors <- FindTransferAnchors(reference = pancreas.integrated, query = pancreas.query,
dims = 1:30)
predictions <- TransferData(anchorset = pancreas.anchors, refdata = pancreas.integrated$celltype,
dims = 1:30)
pancreas.query <- AddMetaData(pancreas.query, metadata = predictions)
Because we have the original label annotations from our full integrated analysis, we can evaluate how well our predicted cell type annotations match the full reference. In this example, we find that there is a high agreement in cell type classification, with over 97% of cells being labeled correctly.
pancreas.query$prediction.match <- pancreas.query$predicted.id == pancreas.query$celltype
table(pancreas.query$prediction.match)
##
## FALSE TRUE
## 16 622
To verify this further, we can examine some canonical cell type markers for specific pancreatic islet cell populations. Note that even though some of these cell types are only represented by one or two cells (e.g. epsilon cells), we are still able to classify them correctly.
table(pancreas.query$predicted.id)
##
## acinar activated_stellate alpha
## 21 17 248
## beta delta ductal
## 258 22 33
## endothelial epsilon gamma
## 13 1 17
## macrophage mast schwann
## 1 2 5
VlnPlot(pancreas.query, c("REG1A", "PPY", "SST", "GHRL", "VWF", "SOX10"), group.by = "predicted.id")
On the previous tab, we demonstrate how to integrate datasets after each has been pre-processed using standard log-normalization. Here, we modify the workflow to take advantage of our improved pre-processing and normalization workflow: SCTransform. You can read more about SCTransform in our recent preprint, and see how to apply it to a single dataset in a separate vignette. We suggest exploring these resources before proceeding.
Conceptually, this workflow is very similar to what we have previously introduced, where we ‘correct’ (or harmonize) log-normalized expression values across datasets. Here, instead, we will harmonize the Pearson residuals that are output from SCTransform. As demonstrated below, the workflow consists of the following steps:
First, you will need to have the development version of Seurat installed. Then, setup the Seurat object list, and run SCTransform on each object separately:
devtools::install_github(repo = "satijalab/seurat", ref = "develop")
library(Seurat)
library(ggplot2)
options(future.globals.maxSize = 4000 * 1024^2)
pancreas.data <- readRDS(file = "../data/pancreas_expression_matrix.rds")
metadata <- readRDS(file = "../data/pancreas_metadata.rds")
pancreas <- CreateSeuratObject(pancreas.data, meta.data = metadata)
pancreas.list <- SplitObject(pancreas, split.by = "tech")
for (i in 1:length(pancreas.list)) {
pancreas.list[[i]] <- SCTransform(pancreas.list[[i]], verbose = FALSE)
}
Next, select features for downstream integration, and run PrepSCTIntegration
, which ensures that all necessary Pearson residuals have been calculated.
pancreas.features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)
pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = pancreas.features,
verbose = FALSE)
Next, identify anchors and integrate the datasets. Commands are identical to the standard workflow, but make sure to set normalization.method = 'SCT'
:
pancreas.anchors <- FindIntegrationAnchors(object.list = pancreas.list, normalization.method = "SCT",
anchor.features = pancreas.features, verbose = FALSE)
pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, normalization.method = "SCT",
verbose = FALSE)
Now proceed with downstream analysis (i.e. visualization, clustering) on the integrated dataset. Commands are identical to the standard workflow, but do not run the ScaleData
function after integration. You can see that after integration, cells group by their biological cell type (which has been pre-annotated), instead of by their underlying technology.
pancreas.integrated <- RunPCA(pancreas.integrated, verbose = FALSE)
pancreas.integrated <- RunUMAP(pancreas.integrated, dims = 1:30)
plots <- DimPlot(pancreas.integrated, group.by = c("tech", "celltype"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
To further demonstrate this workflow, we next apply it to a series of human PBMC datasets from eight different technologies, produced as a systematic technology benchmark by the Human Cell Atlas. Data was downloaded from the Broad Single Cell Portal. We provide the count matrix and metadata file for convenience in getting started.
pbmc.data <- readRDS("../data/pbmc_ssc_mat.rds")
pbmc.metadata <- readRDS("../data/pbmc_ssc_metadata.rds")
pbmc <- CreateSeuratObject(counts = pbmc.data, meta.data = pbmc.metadata)
pbmc <- subset(pbmc, subset = nFeature_RNA > 200)
pbmc.list <- SplitObject(pbmc, split.by = "Method")
for (i in names(pbmc.list)) {
pbmc.list[[i]] <- SCTransform(pbmc.list[[i]], verbose = FALSE)
}
pbmc.features <- SelectIntegrationFeatures(object.list = pbmc.list, nfeatures = 3000)
pbmc.list <- PrepSCTIntegration(object.list = pbmc.list, anchor.features = pbmc.features)
pbmc.anchors <- FindIntegrationAnchors(object.list = pbmc.list, normalization.method = "SCT",
anchor.features = pbmc.features)
pbmc.integrated <- IntegrateData(anchorset = pbmc.anchors, normalization.method = "SCT")
pbmc.integrated <- RunPCA(object = pbmc.integrated, verbose = FALSE)
pbmc.integrated <- RunUMAP(object = pbmc.integrated, dims = 1:30)
plots <- DimPlot(pbmc.integrated, group.by = c("Method", "CellType"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
Note that again, cells are grouped together by shared biological subtype, instead of by technology. In this case, the authors labeled “CellType” in each dataset independently, and you can see that this is consistent with our integrated analysis:
DimPlot(pbmc.integrated, group.by = "CellType", split.by = "Method", ncol = 3)
However, our integrated analysis reveals finer subdivisions amongst these broad cell types, since the combined dataset has substantially greater statistical power. We don’t do a full clustering here on the integrated dataset, but note that we can observe subpopulations of CD4+ (Memory/Naive), CD8 (multiple cytotoxic populations), and B cells (multiple developmental stages). Below, we visualize the expression of the original measurements on our integrated visualization (note that you can add split.by = 'Method
to make these plots for each technology independently)"
DefaultAssay(pbmc.integrated) <- "RNA"
# Normalize RNA data for visualization purposes
pbmc.integrated <- NormalizeData(pbmc.integrated, verbose = FALSE)
FeaturePlot(pbmc.integrated, c("CCR7", "S100A4", "GZMB", "GZMK", "GZMH", "TCL1A"))
Here, we present an additional modification to the Seurat integration workflow, which we refer to as ‘Reference-based’ integration. In the previous workflows, we identify anchors between all pairs of datasets. While this gives datasets equal weight in downstream integration, it can also become computationally intensive. For example, when integrating 10 different datasets, we perform 45 different pairwise comparisons.
As an alternative, we introduce here the possibility of specifying one or more of the datasets as the ‘reference’ for integrated analysis, with the remainder designated as ‘query’ datasets. In this workflow, we do not identify anchors between pairs of query datasets, reducing the number of comparisons. For example, when integrating 10 datasets with one specified as a reference, we perform only 9 comparisons. Reference-based integration can be applied to either log-normalized or SCTransform-normalized datasets.
In practice, we often observe very similar results between the approaches, but with substantially reduced computational time. We demonstrate this using the same PBMC analysis as in the previous tab with highly concordant outputs. We therefore recommend this workflow for users who are integrating a large number of datasets, and desire increased computational efficiency.
This approach does require users to pre-select datasets to serve as a reference. In this example, we use the 10X v3 dataset, as the dataset contains both high cell number and high sensitivity. Users can apply similar criteria when selecting a reference. Furthermore, you can change this setting in the workflow below (for example, try selecting the 10X v2, or Drop-seq datasets as a reference), with only very minor differences in the final result.
The workflow is demonstrated below. Commands are identical to the previous tab, except that we specify a reference
argument in the FindIntegrationAnchors
function. You will also need to have the development version of Seurat installed. Data was originally downloaded from the Broad Single Cell Portal but we provide the count matrix and metadata file for convenience in getting started.
devtools::install_github(repo = "satijalab/seurat", ref = "develop")
library(Seurat)
library(ggplot2)
pbmc.data <- readRDS("../data/pbmc_ssc_mat.rds")
pbmc.metadata <- readRDS("../data/pbmc_ssc_metadata.rds")
pbmc <- CreateSeuratObject(counts = pbmc.data, meta.data = pbmc.metadata)
pbmc <- subset(pbmc, subset = nFeature_RNA > 200)
pbmc.list <- SplitObject(pbmc, split.by = "Method")
for (i in names(pbmc.list)) {
pbmc.list[[i]] <- SCTransform(pbmc.list[[i]], verbose = FALSE)
}
pbmc.features <- SelectIntegrationFeatures(object.list = pbmc.list, nfeatures = 3000)
pbmc.list <- PrepSCTIntegration(object.list = pbmc.list, anchor.features = pbmc.features)
# This command returns dataset 5. We can also specify multiple refs. (i.e. c(5,6))
reference_dataset <- which(names(pbmc.list) == "10x Chromium (v3)")
pbmc.anchors <- FindIntegrationAnchors(object.list = pbmc.list, normalization.method = "SCT",
anchor.features = pbmc.features, reference = reference_dataset)
pbmc.integrated <- IntegrateData(anchorset = pbmc.anchors, normalization.method = "SCT")
pbmc.integrated <- RunPCA(object = pbmc.integrated, verbose = FALSE)
pbmc.integrated <- RunUMAP(object = pbmc.integrated, dims = 1:30)
plots <- DimPlot(pbmc.integrated, group.by = c("Method", "CellType"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
DefaultAssay(pbmc.integrated) <- "RNA"
# Normalize RNA data for visualization purposes
pbmc.integrated <- NormalizeData(pbmc.integrated, verbose = FALSE)
FeaturePlot(pbmc.integrated, c("CCR7", "S100A4", "GZMB", "GZMK", "GZMH", "TCL1A"))
For very large datasets, Canonical Correlation Analysis can sometimes become a prohibitively computationally expensive step. In this workflow, we introduce an alternate form of dimension reduction, reciprocal PCA, to identify an effective space in which to find anchors. When determining anchors between any two datasets using reciprocal PCA, we project each dataset into the other’s PCA space and constrain the anchors by the same mutual neighborhood requirement. All downstream integration steps remain the same and we are able to ‘correct’ (or harmonize) the datasets.
As demonstrated below, the workflow consists of the following steps:
In general, we observe strikingly similar results between the two workflows, but with substantial reduction in compute time and memory when using reciprocal PCA. However, if the datasets are highly divergent (for example, cross-modality mapping or cross-species mapping), where only a small subset of features can be used to facilitate integration, and you may observe superior results using CCA.
For large studies with many datasets, we recommend also combining reciprocal PCA with reference-based integration, or SCTransform normalization (see details on previous tab).
First, you will need to have the development version of Seurat installed.
devtools::install_github(repo = "satijalab/seurat", ref = "develop")
library(SeuratData)
library(Seurat)
For these examples, we will be using the “Immune Cell Atlas” data from the Human Cell Atlas which can be found here. First, we will integrate a randomly downsampled version of this dataset and then the full dataset. The randomly downsampled version contains 40K cells (5K from each donor) and is available through our new SeuratData package.
# Get data from seurat-data
InstallData(pkgs = "bm40k.SeuratData")
After acquiring the data, we first perform standard normalization and variable feature selection.
library(ggplot2)
# set up future for parallelization
library(future)
library(future.apply)
plan("multiprocess", workers = 4)
options(future.globals.maxSize = 20000 * 1024^2)
# load in the data
data(hcabm40k)
bm40k.list <- SplitObject(hcabm40k, split.by = "orig.ident")
bm40k.list <- lapply(X = bm40k.list, FUN = function(x) {
x <- NormalizeData(x, verbose = FALSE)
x <- FindVariableFeatures(x, verbose = FALSE)
})
Next, select features for downstream integration, and run PCA on each object in the list, which is required for running the reciprocal PCA workflow.
features <- SelectIntegrationFeatures(object.list = bm40k.list)
bm40k.list <- lapply(X = bm40k.list, FUN = function(x) {
x <- ScaleData(x, features = features, verbose = FALSE)
x <- RunPCA(x, features = features, verbose = FALSE)
})
Since this dataset contains both men and women, we will chose one male and one female (BM1 and BM2) to use in the reference-based workflow. We determined donor sex by examining the expression of the XIST gene. Next, identify anchors and integrate the datasets. Commands are identical to the standard workflow, but make sure to set reduction = 'rpca'
:
anchors <- FindIntegrationAnchors(object.list = bm40k.list, reference = c(1, 2), reduction = "rpca",
dims = 1:30)
bm40k.integrated <- IntegrateData(anchorset = anchors, dims = 1:30)
Now proceed with downstream analysis (i.e. visualization, clustering) on the integrated dataset. Commands are identical to the standard workflow.
bm40k.integrated <- ScaleData(bm40k.integrated, verbose = FALSE)
bm40k.integrated <- RunPCA(bm40k.integrated, verbose = FALSE)
bm40k.integrated <- RunUMAP(bm40k.integrated, dims = 1:30)
DimPlot(bm40k.integrated, group.by = "orig.ident")
To further demonstrate this workflow, we next apply it to the full dataset. While we do not provide this through SeuratData, you can find the counts matrix on the HCA Data Portal site. The commands below are identical to the 40k integration except we’ve increased the dimensionality to 50 to reflect the increased cell number and diversity.
bm280k.data <- Read10X_h5("../data/ica_bone_marrow_h5.h5")
bm280k <- CreateSeuratObject(counts = bm280k.data, min.cells = 100, min.features = 500)
bm280k.list <- SplitObject(bm280k, split.by = "orig.ident")
bm280k.list <- future_lapply(X = bm280k.list, FUN = function(x) {
x <- NormalizeData(x, verbose = FALSE)
x <- FindVariableFeatures(x, verbose = FALSE)
})
features <- SelectIntegrationFeatures(object.list = bm280k.list)
bm280k.list <- future_lapply(X = bm280k.list, FUN = function(x) {
x <- ScaleData(x, features = features, verbose = FALSE)
x <- RunPCA(x, features = features, verbose = FALSE)
})
anchors <- FindIntegrationAnchors(object.list = bm280k.list, reference = c(1, 2), reduction = "rpca",
dims = 1:50)
bm280k.integrated <- IntegrateData(anchorset = anchors, dims = 1:50)
bm280k.integrated <- ScaleData(bm280k.integrated, verbose = FALSE)
bm280k.integrated <- RunPCA(bm280k.integrated, verbose = FALSE)
bm280k.integrated <- RunUMAP(bm280k.integrated, dims = 1:50)
DimPlot(bm280k.integrated, group.by = "orig.ident")