Evaluating scRNA-seq Integration Quality with CIDER

In CIDER: Meta-Clustering for scRNA-Seq Integration and Evaluation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", warning = FALSE, message = FALSE
)

Why Evaluate Integration?

Single-cell RNA-seq integration methods aim to remove technical batch effects while preserving biological variation. CIDER provides a ground-truth-free approach to:

1. Identify well-integrated cell populations
2. Detect potentially incorrect integrations
3. Quantify integration confidence through empirical p-values

This vignette focuses how showing the process using the example data of dendritic cells.

Set up

Apart from CIDER, the following packages also need to be loaded:

library(CIDER)
library(Seurat)
library(cowplot)
library(ggplot2)

Load dendritic data

The example data can be downloaded from https://figshare.com/s/d5474749ca8c711cc205. This dataset contains 26593 genes and 564 cells, and comprises four dendritic cell subtypes (CD141, CD1C, DoubleNeg, and pDC) from two batches. The raw count matrix and sample information were downloaded from a curated set, and cells with fewer than 500 detected genes have been removed.

# Download the data
data_url <- "https://figshare.com/ndownloader/files/52116197"
data_file <- file.path(tempdir(), "dendritic.rds")

if (!file.exists(data_file)) {
  message("Downloading data...")
  download.file(data_url, destfile = data_file, mode = "wb")
}

dendritic_reduced <- load(data_file)

# load("../data/dendritic.rda")
dendritic <- CreateSeuratObject(counts = dendritic@assays$RNA@counts, meta.data = [e-mail])

# Verify batch composition
table(dendritic$Batch)

Perform integration with Seurat

First an integration method$^1$ is applied on the dendritic data. You can apply other integration methods to the your data, as long as the correct PCs are stored in your Seurat object, i.e. Reductions(seu.integrated, "pca") or seu.integrated@reductions$pca.

seu.list <- SplitObject(dendritic, split.by = "Batch")
for (i in 1:length(seu.list)) {
  seu.list[[i]] <- NormalizeData(seu.list[[i]], verbose = FALSE)
  seu.list[[i]] <- FindVariableFeatures(seu.list[[i]], 
                                        selection.method = "vst", 
                                        nfeatures = 1000, verbose = FALSE)
}
seu.anchors <- FindIntegrationAnchors(object.list = seu.list, 
                                      dims = 1:15, verbose = FALSE)
seu.integrated <- IntegrateData(anchorset = seu.anchors, 
                                dims = 1:15, verbose = FALSE)

DefaultAssay(seu.integrated) <- "integrated"
seu.integrated <- ScaleData(seu.integrated, verbose = FALSE)
seu.integrated <- RunPCA(seu.integrated, verbose = FALSE)
seu.integrated <- RunTSNE(seu.integrated, reduction = "pca", dims = 1:5)

Clear the intermediate outcome.

rm(seu.list, seu.anchors)
gc()

CIDER Evaluation Workflow

CIDER evaluates integration results in three steps.

Step 1: Density-Based Clustering

Clustering based on the corrected PCs (hdbscan.seurat). This step uses HDBSCAN, which is a density-based clustering algorithm$^2$. The clustering results are stored in seu.integrated$dbscan_cluster. Clusters are further divided into batch-specific clusters by concatenating dbscan_cluster and batch, stored in seu.integrated$initial_cluster.

seu.integrated <- hdbscan.seurat(seu.integrated)

Step 2: Calculate Cluster Similarities

Compute IDER-based similarity matrix (getIDEr) among the batch-specific initial clusters. If multiple CPUs are availble, you can set use.parallel = TRUE and n.cores to the number of available cores to speed it up.

ider <- getIDEr(seu.integrated, use.parallel = FALSE, verbose = FALSE)

Step 3: Compute Integration Confidence

Assign the similarity and estimate empirical p values (estimateProb) for the correctness of integration. High similarity values and low p values indicate that the cell are similar to the surrounding cells and likely integrated correctly.

seu.integrated <- estimateProb(seu.integrated, ider)

Visual Evaluation

Evaluation scores

The evaluation scores can be viewed by the scatterPlot as below. As shown cells with dbscan_cluster of 2 and 3 have low regional similarity and high empirical p values, suggesting that they can be incorrectly integrated.

p1 <- scatterPlot(seu.integrated, "tsne", "dbscan_cluster")
p2 <- scatterPlot(seu.integrated, "tsne", colour.by = "similarity") + labs(fill = "Similarity")
p3 <- scatterPlot(seu.integrated, "tsne", colour.by = "pvalue") + labs(fill = "Prob of \nrejection")
plot_grid(p1, p2, p3, ncol = 3)

Interpretation Guide:

✅ High similarity + Low p-value: Well-integrated regions

❌ Low similarity + High p-value: Potential integration errors

The IDER-based Similarity Network

To have more insight, we can view the IDER-based similarity matrix by functions plotNetwork or plotHeatmap. Both of them require the input of a Seurat object and the output of getIDEr. In this example, 1_Batch1 and 1_Batch2 as well as 4_Batch1 and 4_Batch2 have high similarity.

plotNetwork generates a graph where vertexes are initial clusters and edge widths are similarity values. The parameter weight.factor controls the scale of edge widths; larger weight.factor will give bolder edges proportionally.

plotNetwork(seu.integrated, ider, weight.factor = 3)

Cluster Similarity Heatmap

plotHeatmap generates a heatmap where each cell is coloured and labeled by the similarity values.

plotHeatmap(seu.integrated, ider)

Validation Against Ground Truth Annotation

So far the evaluation have completed and CIDER has not used the ground truth at all!

Let's peep at the ground truth before the closure of this vignette. As shown in the figure below, the clusters having low IDER-based similarity and high p values actually have at least two populations (CD1C and CD141), verifying that CIDER spots the wrongly integrated cells.

scatterPlot(seu.integrated, "tsne", colour.by = "Group") + labs(fill = "Group\n (ground truth)")

Best Practices

1. Parameter Tuning:

2. Adjust hdbscan.seurat parameters if initial clustering is too granular

3. Modify cutree.h in estimateProb to change confidence thresholds

4. Interpretation Tips:

5. Always validate suspicious joint clusters with marker genes

6. Scalability:

7. For large datasets (>10k cells), enable parallel processing with use.parallel=TRUE

Reproducibility

sessionInfo()

References

1. Stuart and Butler et al. Comprehensive Integration of Single-Cell Data. Cell (2019).
2. Campello, Ricardo JGB, Davoud Moulavi, and Jörg Sander. “Density-based clustering based on hierarchical density estimates.” Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, 2013.
3. The data were downloaded from \url{https://hub.docker.com/r/jinmiaochenlab/batch-effect-removal-benchmarking}.
4. Tran HTN, Ang KS, Chevrier M, Lee NYS, Goh M, Chen J. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. (2020).
5. Villani A-C, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science (2017).

CIDER documentation built on April 4, 2025, 2:28 a.m.