CIDER

CIDER is a meta-clustering workflow designed to handle scRNA-seq data that span multiple samples or conditions. Often, these datasets are confounded by batch effects or other variables. Many existing batch-removal methods assume near-identical cell population compositions across samples. CIDER, in contrast, leverages inter-group similarity measures to guide clustering without requiring such strict assumptions.

• Genome Biology (2021) publication: CIDER article
• Original prototype: Hu et al., Cancer Cell 2020

Highlights

• Clustering: Overcome confounders in scRNA-seq data (e.g., batch effects) without requiring identical cell-type composition.
• Evaluation metric: Assess whether integrated data from methods like Seurat-CCA, Harmony, or Scanorama preserve meaningful biological structure—no prior cell-type labels required.

Installation

You can install CIDER from github with:

# install.packages("devtools")
devtools::install_github('zhiyuan-hu-lab/CIDER')

Quick Start: Using CIDER as an Evaluation Metric

If you have already integrated your scRNA-seq data (e.g., using Seurat-CCA, Harmony, or Scanorama) and want to evaluate how well the biological populations align post-integration, you can use CIDER as follows.

1. Before running CIDER evaluation functions, make sure that you have a Seurat object (e.g. seu.integrated) with corrected PCs in

seu.integrated@reductions$pca@cell.embeddings`

• Seurat-CCA automatically put the corrected PCs there.
• If other methods are used, the corrected PCs can be added using

seu.integrated@reductions$pca@cell.embeddings <- corrected.PCs

2. Run hdbscan clustering (optional) and compute the IDER score:

library(CIDER)
seu.integrated <- hdbscan.seurat(seu.integrated)
ider <- getIDEr(seu.integrated, verbose = FALSE)
seu.integrated <- estimateProb(seu.integrated, ider)

3. Visualize evaluation scores on t-SNE or UMAP:

The evaluation scores (IDER-based similarity and empirical p values) can be visualised by the scatterPlot function.

p1 <- scatterPlot(seu.integrated, "tsne", colour.by = "similarity")
p2 <- scatterPlot(seu.integrated, "tsne", colour.by = "pvalue") 
plot_grid(p1,p2, ncol = 2)

For a more detailed walkthrough, see the detailed tutorial of evaluation

Using CIDER for Clustering Tasks