Load in the data

This vignette demonstrates new features that allow users to analyze and explore multi-modal data with Seurat. While this represents an initial release, we are excited to release significant new functionality for multi-modal datasets in the future.

Here, we analyze a dataset of 8,617 cord blood mononuclear cells (CBMCs), produced with CITE-seq, where we simultaneously measure the single cell transcriptomes alongside the expression of 11 surface proteins, whose levels are quantified with DNA-barcoded antibodies. First, we load in two count matrices : one for the RNA measurements, and one for the antibody-derived tags (ADT). You can download the ADT file here and the RNA file here

library(Seurat)
library(ggplot2)
library(patchwork)
# Load in the RNA UMI matrix

# Note that this dataset also contains ~5% of mouse cells, which we can use as negative controls
# for the protein measurements. For this reason, the gene expression matrix has HUMAN_ or MOUSE_
# appended to the beginning of each gene.
cbmc.rna <- as.sparse(read.csv(file = "../data/GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz", sep = ",", 
    header = TRUE, row.names = 1))

# To make life a bit easier going forward, we're going to discard all but the top 100 most
# highly expressed mouse genes, and remove the 'HUMAN_' from the CITE-seq prefix
cbmc.rna <- CollapseSpeciesExpressionMatrix(cbmc.rna)

# Load in the ADT UMI matrix
cbmc.adt <- as.sparse(read.csv(file = "../data/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz", sep = ",", 
    header = TRUE, row.names = 1))

# When adding multimodal data to Seurat, it's okay to have duplicate feature names. Each set of
# modal data (eg. RNA, ADT, etc.) is stored in its own Assay object.  One of these Assay objects
# is called the 'default assay', meaning it's used for all analyses and visualization.  To pull
# data from an assay that isn't the default, you can specify a key that's linked to an assay for
# feature pulling.  To see all keys for all objects, use the Key function.  Lastly, we observed
# poor enrichments for CCR5, CCR7, and CD10 - and therefore remove them from the matrix
# (optional)
cbmc.adt <- cbmc.adt[setdiff(rownames(x = cbmc.adt), c("CCR5", "CCR7", "CD10")), ]

Setup a Seurat object, and cluster cells based on RNA expression

The steps below represent a quick clustering of the PBMCs based on the scRNA-seq data. For more detail on individual steps or more advanced options, see our PBMC clustering guided tutorial here

cbmc <- CreateSeuratObject(counts = cbmc.rna)

# standard log-normalization
cbmc <- NormalizeData(cbmc)

# choose ~1k variable features
cbmc <- FindVariableFeatures(cbmc)

# standard scaling (no regression)
cbmc <- ScaleData(cbmc)

# Run PCA, select 13 PCs for tSNE visualization and graph-based clustering
cbmc <- RunPCA(cbmc, verbose = FALSE)
ElbowPlot(cbmc, ndims = 50)

cbmc <- FindNeighbors(cbmc, dims = 1:25)
cbmc <- FindClusters(cbmc, resolution = 0.8)
cbmc <- RunTSNE(cbmc, dims = 1:25, method = "FIt-SNE")

# Find the markers that define each cluster, and use these to annotate the clusters, we use
# max.cells.per.ident to speed up the process
cbmc.rna.markers <- FindAllMarkers(cbmc, max.cells.per.ident = 100, min.diff.pct = 0.3, only.pos = TRUE)
# Note, for simplicity we are merging two CD14+ Monocyte clusters (that differ in expression of
# HLA-DR genes) and NK clusters (that differ in cell cycle stage)
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)
DimPlot(cbmc, label = TRUE) + NoLegend()

Add the protein expression levels to the Seurat object

Seurat v3.0 allows you to store information from multiple assays in the same object, as long as the data is multi-modal (collected on the same set of cells). You can use the SetAssayData and GetAssayData accessor functions to add and fetch data from additional assays.

# We will define an ADT assay, and store raw counts for it

# If you are interested in how these data are internally stored, you can check out the Assay
# class, which is defined in objects.R; note that all single-cell expression data, including RNA
# data, are still stored in Assay objects, and can also be accessed using GetAssayData
cbmc[["ADT"]] <- CreateAssayObject(counts = cbmc.adt)

# Now we can repeat the preprocessing (normalization and scaling) steps that we typically run
# with RNA, but modifying the 'assay' argument.  For CITE-seq data, we do not recommend typical
# LogNormalization. Instead, we use a centered log-ratio (CLR) normalization, computed
# independently for each feature.  This is a slightly improved procedure from the original
# publication, and we will release more advanced versions of CITE-seq normalizations soon.
cbmc <- NormalizeData(cbmc, assay = "ADT", normalization.method = "CLR")
cbmc <- ScaleData(cbmc, assay = "ADT")

Visualize protein levels on RNA clusters

You can use the names of any ADT markers, (i.e. “adt_CD4”), in FetchData, FeaturePlot, RidgePlot, FeatureScatter, DoHeatmap, or any other visualization features

# in this plot, protein (ADT) levels are on top, and RNA levels are on the bottom
FeaturePlot(cbmc, features = c("adt_CD3", "adt_CD11c", "adt_CD8", "adt_CD16", "CD3E", "ITGAX", "CD8A", 
    "FCGR3A"), min.cutoff = "q05", max.cutoff = "q95", ncol = 4)

RidgePlot(cbmc, features = c("adt_CD3", "adt_CD11c", "adt_CD8", "adt_CD16"), ncol = 2)

# Draw ADT scatter plots (like biaxial plots for FACS). Note that you can even 'gate' cells if
# desired by using HoverLocator and FeatureLocator
FeatureScatter(cbmc, feature1 = "adt_CD19", feature2 = "adt_CD3")

# view relationship between protein and RNA
FeatureScatter(cbmc, feature1 = "adt_CD3", feature2 = "CD3E")

# Let's plot CD4 vs CD8 levels in T cells
tcells <- subset(cbmc, idents = c("Naive CD4 T", "Memory CD4 T", "CD8 T"))
FeatureScatter(tcells, feature1 = "adt_CD4", feature2 = "adt_CD8")

# # Let's look at the raw (non-normalized) ADT counts. You can see the values are quite high,
# particularly in comparison to RNA values. This is due to the significantly higher protein copy
# number in cells, which significantly reduces 'drop-out' in ADT data
FeatureScatter(tcells, feature1 = "adt_CD4", feature2 = "adt_CD8", slot = "counts")

If you look a bit more closely, you’ll see that our CD8 T cell cluster is enriched for CD8 T cells, but still contains many CD4+ CD8- T cells. This is because Naive CD4 and CD8 T cells are quite similar transcriptomically, and the RNA dropout levels for CD4 and CD8 are quite high. This demonstrates the challenge of defining subtle immune cell differences from scRNA-seq data alone.

saveRDS(cbmc, file = "../output/cbmc.rds")

Identify differentially expressed proteins between clusters

# Downsample the clusters to a maximum of 300 cells each (makes the heatmap easier to see for
# small clusters)
cbmc.small <- subset(cbmc, downsample = 300)

# Find protein markers for all clusters, and draw a heatmap
adt.markers <- FindAllMarkers(cbmc.small, assay = "ADT", only.pos = TRUE)
DoHeatmap(cbmc.small, features = unique(adt.markers$gene), assay = "ADT", angle = 90) + NoLegend()

# You can see that our unknown cells co-express both myeloid and lymphoid markers (true at the
# RNA level as well). They are likely cell clumps (multiplets) that should be discarded. We'll
# remove the mouse cells now as well
cbmc <- subset(cbmc, idents = c("Multiplets", "Mouse"), invert = TRUE)

Cluster directly on protein levels

You can also run dimensional reduction and graph-based clustering directly on CITE-seq data

# Because we're going to be working with the ADT data extensively, we're going to switch the
# default assay to the 'CITE' assay.  This will cause all functions to use ADT data by default,
# rather than requiring us to specify it each time
DefaultAssay(cbmc) <- "ADT"
cbmc <- RunPCA(cbmc, features = rownames(cbmc), reduction.name = "pca_adt", reduction.key = "pca_adt_", 
    verbose = FALSE)
DimPlot(cbmc, reduction = "pca_adt")

# Since we only have 10 markers, instead of doing PCA, we'll just use a standard euclidean
# distance matrix here.  Also, this provides a good opportunity to demonstrate how to do
# visualization and clustering using a custom distance matrix in Seurat
adt.data <- GetAssayData(cbmc, slot = "data")
adt.dist <- dist(t(adt.data))

# Before we recluster the data on ADT levels, we'll stash the RNA cluster IDs for later
cbmc[["rnaClusterID"]] <- Idents(cbmc)

# Now, we rerun tSNE using our distance matrix defined only on ADT (protein) levels.
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")

# We can compare the RNA and protein clustering, and use this to annotate the protein clustering
# (we could also of course use FindMarkers)
clustering.table <- table(Idents(cbmc), cbmc$rnaClusterID)
clustering.table
Memory CD4 T CD14+ Mono Naive CD4 T NK B CD8 T CD16+ Mono T/Mono doublets CD34+ Eryth Mk DC pDCs
0 1754 0 1217 29 0 27 0 5 2 4 24 1 2
1 0 2189 0 4 0 0 30 1 1 5 25 55 0
2 3 0 2 890 3 1 0 0 1 3 7 2 1
3 0 4 0 2 319 0 2 0 2 2 3 0 0
4 24 0 18 4 1 243 0 0 0 1 2 0 0
5 1 27 4 157 2 2 10 56 0 9 16 6 2
6 4 5 0 1 0 0 0 1 113 81 16 5 0
7 4 59 4 0 0 0 9 117 0 0 2 0 1
8 0 9 0 2 0 0 179 0 0 0 1 0 0
9 0 0 1 0 0 0 0 0 0 0 0 1 43
10 1 0 2 0 25 0 0 2 0 0 0 0 0 
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)

tsne_rnaClusters <- DimPlot(cbmc, reduction = "tsne_adt", group.by = "rnaClusterID") + NoLegend()
tsne_rnaClusters <- tsne_rnaClusters + ggtitle("Clustering based on scRNA-seq") + theme(plot.title = element_text(hjust = 0.5))
tsne_rnaClusters <- LabelClusters(plot = tsne_rnaClusters, id = "rnaClusterID", size = 4)

tsne_adtClusters <- DimPlot(cbmc, reduction = "tsne_adt", pt.size = 0.5) + NoLegend()
tsne_adtClusters <- tsne_adtClusters + ggtitle("Clustering based on ADT signal") + theme(plot.title = element_text(hjust = 0.5))
tsne_adtClusters <- LabelClusters(plot = tsne_adtClusters, id = "ident", size = 4)

# Note: for this comparison, both the RNA and protein clustering are visualized on a tSNE
# generated using the ADT distance matrix.
wrap_plots(list(tsne_rnaClusters, tsne_adtClusters), ncol = 2)

The ADT-based clustering yields similar results, but with a few differences

tcells <- subset(cbmc, idents = c("CD4 T", "CD8 T"))
FeatureScatter(tcells, feature1 = "CD4", feature2 = "CD8")

RidgePlot(cbmc, features = c("adt_CD11c", "adt_CD8", "adt_CD16", "adt_CD4", "adt_CD19", "adt_CD14"), 
    ncol = 2)

saveRDS(cbmc, file = "../output/cbmc_multimodal.rds")

Loading data from 10X multi-modal experiments

Seurat is also able to analyze data from multi-modal 10X experiments processed using CellRanger v3; as an example, we recreate the plots above using a dataset of 7,900 peripheral blood mononuclear cells (PBMC), freely available from 10X Genomics here.

pbmc10k.data <- Read10X(data.dir = "../data/pbmc10k/filtered_feature_bc_matrix/")
rownames(x = pbmc10k.data[["Antibody Capture"]]) <- gsub(pattern = "_[control_]*TotalSeqB", replacement = "", 
    x = rownames(x = pbmc10k.data[["Antibody Capture"]]))

pbmc10k <- CreateSeuratObject(counts = pbmc10k.data[["Gene Expression"]], min.cells = 3, min.features = 200)
pbmc10k <- NormalizeData(pbmc10k)
pbmc10k[["ADT"]] <- CreateAssayObject(pbmc10k.data[["Antibody Capture"]][, colnames(x = pbmc10k)])
pbmc10k <- NormalizeData(pbmc10k, assay = "ADT", normalization.method = "CLR")

plot1 <- FeatureScatter(pbmc10k, feature1 = "adt_CD19", feature2 = "adt_CD3", pt.size = 1)
plot2 <- FeatureScatter(pbmc10k, feature1 = "adt_CD4", feature2 = "adt_CD8a", pt.size = 1)
plot3 <- FeatureScatter(pbmc10k, feature1 = "adt_CD3", feature2 = "CD3E", pt.size = 1)
(plot1 + plot2 + plot3) & NoLegend()