Coralysis: sensitive integration of single-cell data
Introduction
Coralysis is an R package for sensitive integration,
reference-mapping and cell-state identification of single-cell data
(Sousa et al., 2025). It relies on an adapted version of our previously
introduced Iterative Clustering Projection (ICP) algorithm (Smolander et
al., 2021) to identify shared cell clusters across heterogeneous
datasets by leveraging multiple rounds of divisive clustering.
Inspired by the process of assembling a puzzle - where one begins by grouping pieces based on low-to high-level features, such as color and shading, before looking into shape and patterns - this multi-level integration algorithm progressively blends the batch effects while separating cell types across multiple runs of divisive clustering. The trained ICP models can then be used for various purposes, including prediction of cluster identities of related, unannotated single-cell datasets through reference-mapping, and inference of cell states and their differential expression programs using the cell cluster probabilities that represent the likelihood of each cell belonging to each cluster.
While state-of-the-art single-cell integration methods often struggle
with imbalanced cell types across heterogeneous datasets,
Coralysis effectively differentiates similar yet unshared
cell types across batches (Sousa et al., 2025).
Installation
Coralysis can be installed from
Bioconductor by running the following commands:
# Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Coralysis")Dataset
To quickly demonstrate the ability of Coralysis’s
multi-level integration algorithm to integrate data that is unevenly
distributed across batches or completely absent from some datasets, we
selected two brain datasets — zeisel (Zeisel et al.,
2018) and romanov (Romanov et al., 2017). These
datasets share only a few cell types, with some similar yet distinct
cell types across datasets, such as interneurons in
zeisel and neurons in romanov. This
makes them an ideal example to highlight the strengths of
Coralysis.
The zeisel (Zeisel et al., 2018) and
romanov datasets can be imported as
SingleCellExperiment objects via the scRNAseq
R package using the functions
ZeiselBrainData() and RomanovBrainData(). Run
the R code below to load the packages Coralysis,
scRNAseq and SingleCellExperiment as well as
the datasets zeisel and romanov.
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## value will be disallowed in the next release cycle.Preprocess
The datasets were normalized with the scater function
logNormCounts() below. Additionaly they were merged based
on shared genes and later subset to 2,000 highly variable genes with the
scran function getTopHVGs(). Cell type labels
correspond to level 1 class annotations provided in both
SingleCellExperiment objects. They were stored under
cell_type in colData(sce), while batch labels
(i.e., Zeisel et al. or Romanov et al.) were
saved under batch in colData(sce).
# Normalize
zeisel <- scater::logNormCounts(zeisel)
counts(zeisel) <- NULL
altExps(zeisel) <- list()
romanov <- scater::logNormCounts(romanov)
counts(romanov) <- NULL
# Intersected genes
genes <- intersect(row.names(zeisel), row.names(romanov))
zeisel <- zeisel[genes, ]
romanov <- romanov[genes, ]
# Remove colData
colData(zeisel) <- colData(zeisel)[, "level1class", drop = FALSE]
colnames(colData(zeisel)) <- "cell_type"
colData(romanov) <- colData(romanov)[, "level1 class", drop = FALSE]
colnames(colData(romanov)) <- "cell_type"
# Batch
zeisel[["batch"]] <- "Zeisel et al."
romanov[["batch"]] <- "Romanov et al."
# Merge cells
sce <- cbind(zeisel, romanov)
rm(list = c("zeisel", "romanov"))
# HVG
hvg <- scran::getTopHVGs(sce, n = 2000)
sce <- sce[hvg, ]The PrepareData() function checks whether a sparse
matrix is available in the logcounts assay (which
corresponds to log-normalized data) and removes non-expressed features
preparing the object to run RunParallelDivisiveICP()
next.
# Prepare data:
# checks 'logcounts' format & removes non-expressed genes
sce <- PrepareData(object = sce)## Data in `logcounts` slot already of `dgCMatrix` class...## 2000/2000 features remain after filtering features with only zero values.Multi-level integration
The multi-level integration algorithm is implemented in the
RunParallelDivisiveICP() function, the main function in
Coralysis. It only requires a
SingleCellExperiment object, which in this case is
sce.
To perform integration across a batch, a batch.label
available in colData(sce) must be provided. In this case,
it is "batch", created above to hold the information about
the brain dataset origin, i.e., Zeisel et al. or Romanov et
al.. The ensemble algorithm runs 50 times by default, but for
illustrative purposes, this has been reduced to 10
(L=10).
Two threads are allocated to speed up the process
(threads=2), though by default, the function uses all
available system threads. Specify one thread if you prefer not to use
any additional threads. In addition, the cells are downsampled to 1,000
cells per batch to speed up the process
(icp.batch.size = 1000). The seed provided in
set.seed is required to ensure the reproducibility of the
sampling taken before parallelization. To ensure reproducibility during
parallelization, the RNGseed parameter is set to
123 by default.
The result consists of a set of models and their respective
probability matrices (n = 40; log2(k) * L),
stored in metadata(sce)$coralysis$models and
metadata(sce)$coralysis$joint.probability,
respectively.
# Multi-level integration
set.seed(4591) # reproducibility of downsampling - 'icp.batch.size'
sce <- RunParallelDivisiveICP(
object = sce,
batch.label = "batch",
icp.batch.size = 1000,
L = 10, threads = 2,
RNGseed = 123
)##
## Downsampling dataset by batch using random sampling.##
## Building training set...## Training set successfully built.##
## Computing cluster seed.##
## Initializing divisive ICP clustering...## | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100%##
## Divisive ICP clustering completed successfully.##
## Predicting cell cluster probabilities using ICP models...## Prediction of cell cluster probabilities completed successfully.##
## Multi-level integration completed successfully.Integrated embedding
The integrated result of Coralysis consist of an
integrated embedding which can be obtained by running the function
RunPCA. This integrated PCA can, in turn, be used
downstream for clustering or non-linear dimensional reduction
techniques, such as RunTSNE or RunUMAP. The
function RunPCA runs by default the PCA method implemented
the R package irlba
(pca.method="irlba"), which requires a seed to ensure the
same PCA result.
## Divisive ICP: selecting ICP tables multiple of 4Nonlinear dimensional reduction
Compute UMAP by running the function RunUMAP().
Visualize batch & cell types
Finally, the integration can be visually inspected by highlighting
the batch and cell type labels into the UMAP projection.
Coralysis is sensitive enough to integrate cell types
shared across datasets such as oligodendrocytes, microglia and
endothelial cells, but not ependymal or pyramidal cells. This difference
arises because Romanov et al., 2017 focused specifically on the
hypothalamus, whereas Zeisel et al., 2018 provided a broader
characterization of the entire mouse nervous system, including multiple
brain regions.
# Visualize categorical variables integrated emb.
vars <- c("batch", "cell_type")
plots <- lapply(X = vars, FUN = function(x) {
PlotDimRed(
object = sce, color.by = x,
point.size = 0.25, point.stroke = 0.5,
legend.nrow = 4
)
})## Warning in guide_legend(title = legend.title, nrow = legend.nrow, bycol = TRUE, : Arguments in `...` must be used.
## ✖ Problematic argument:
## • bycol = TRUE
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • bycol = TRUE
## ℹ Did you misspell an argument name?
To allow a better distinction between batches, plot cell types by batch below.
Interestingly, Coralysis integrated VSM (Vascular Smooth
Muscle) cells from Romanov et al., 2017 with a subset of endothelial
mural cells from Zeisel et al., 2018. Gene expression visualization of
cell type markers for VSM — Acta2, Myh11,
Tagln — and endothelial — Cdh5, Pecam1 —
cells support this integration.
# Visualization of gene expression markers
markers <- c("Acta2", "Myh11", "Tagln", "Cdh5", "Pecam1")
plot.markers <- lapply(markers, function(x) {
PlotExpression(sce, color.by = x, point.size = 0.5, point.stroke = 0.5) +
ggplot2::facet_grid(~batch)
})
cowplot::plot_grid(plotlist = plot.markers, ncol = 2)
Graph-based clustering
Graph-based clustering can be obtained by running the
scran function clusterCells() using the
Coralysis integrated embedding. The clustering result can
be saved directly into the SingleCellExperiment object
(sce$cluster).
# Graph-based clustering
set.seed(123)
sce$cluster <- scran::clusterCells(sce,
use.dimred = "PCA",
BLUSPARAM = bluster::SNNGraphParam(k = 30, cluster.fun = "louvain")
)
# Plot the clustering result
plots[[3]] <- PlotDimRed(
object = sce, color.by = "cluster",
point.size = 0.25, point.stroke = 0.5,
legend.nrow = 4
)## Warning in guide_legend(title = legend.title, nrow = legend.nrow, bycol = TRUE, : Arguments in `...` must be used.
## ✖ Problematic argument:
## • bycol = TRUE
## ℹ Did you misspell an argument name?
The comparison below highlights the original cell type annotations
and Louvain clustering obtained with the Coralysis
integrated embedding, allowing us to assess the extent of cell
population overlap between datasets. For instance,
Coralysis integrated shared populations (e.g., clusters 11,
12 and 14), identified similar but unshared cell populations (e.g.,
clusters 10 versus 12), and preserved unique populations specific to
each dataset (e.g., clusters 7 and 13).
cowplot::plot_grid(
plots[[2]] + ggplot2::facet_grid(~batch),
plots[[3]] + ggplot2::facet_grid(~batch),
ncol = 1
)
Gene coefficients
Next, we can examine the gene coefficients from the ICP models that best explain the clusters identified above, allowing us to explore the expression of genes that most strongly support these clusters. Below we will look into the identified similar but unshared cell clusters 10 and 12.
Gene coefficients can be obtained for the cluster label
through majority voting with the function
MajorityVotingFeatures(). The majority voting is performed
by searching for the most representative cluster for a given cell label
across all possible clusters (i.e., across all icp runs and rounds). The
most representative cluster corresponds to the cluster with the highest
majority voting score. This corresponds to the geometric mean between
the proportion of cells from the given label intersected with a cluster
and the proportion of cells from that same cluster that intersected with
the cells from the given label. The higher the score the better. A
cluster that scores 1 indicates that all its cells correspond to the
assigned label, and vice versa; i.e., all the cells with the assigned
label belong to this cluster. For example,
MajorityVotingFeatures() by providing the
colData variable "cluster" (i.e.,
label="cluster").
# Get label gene coefficients by majority voting
clts.gene.coeff <- MajorityVotingFeatures(object = sce, label = "cluster")
# Print summary
clts.gene.coeff$summary## label icp_run icp_round cluster score
## 1 1 7 4 15 0.7321750
## 2 2 5 4 11 0.9278696
## 3 3 1 3 6 0.7120689
## 4 4 5 4 16 0.6855573
## 5 5 8 3 4 0.9170213
## 6 6 5 4 4 0.9449112
## 7 7 4 4 14 0.7893239
## 8 8 8 3 8 0.8476514
## 9 9 4 4 13 0.8389856
## 10 10 10 4 10 0.9493106
## 11 11 9 4 4 0.8929196
## 12 12 4 4 11 0.8897075
## 13 13 9 4 1 0.8881332
## 14 14 1 3 1 0.9253346Visualize below the expression for the top 6 positive coefficients for the identified similar but unshared cell populations: clusters 10 and 12.
## Visualize top coefficients for cluster 10 and 12
clts <- c("10", "12")
plot.coeff.markers <- list()
for (i in clts) {
top.markers <- clts.gene.coeff$feature_coeff[[i]]
top.markers <- top.markers[order(top.markers[,2], decreasing = TRUE), ]
top.markers <- top.markers[1:6, "feature"]
plot.coeff.markers[[i]] <- lapply(top.markers, function(x) {
PlotExpression(sce, color.by = x, point.size = 0.3, point.stroke = 0.5) +
ggplot2::facet_grid(~batch)
})
}Visualize the expression of the top 6 positive coefficients for cluster 10. The top 6 positive coefficients identified are highly specific to cluster 10.
Visualize the expression of the top six positive coefficients for cluster 12. Among the highly specific positive coefficients, myelination-related genes Ptgds and Mal are enriched in cluster 12 compared to cluster 10, where they are practically absent. This suggests that cluster 12 represents a mature, myelinating oligodendrocyte population, in contrast to the more immature cluster 10.
Overall, ICP gene coefficients supported, to some extent, the
identification of similar but unshared cell populations (clusters 10 and
12) with Coralysis.
R session
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ensembldb_2.35.0 AnnotationFilter_1.34.0
## [3] GenomicFeatures_1.63.1 AnnotationDbi_1.72.0
## [5] ComplexHeatmap_2.27.0 Coralysis_1.0.0
## [7] scRNAseq_2.25.0 scater_1.39.0
## [9] ggplot2_4.0.0 scuttle_1.21.0
## [11] SingleCellExperiment_1.33.0 SummarizedExperiment_1.41.0
## [13] Biobase_2.70.0 GenomicRanges_1.63.0
## [15] Seqinfo_1.1.0 IRanges_2.45.0
## [17] S4Vectors_0.49.0 BiocGenerics_0.57.0
## [19] generics_0.1.4 MatrixGenerics_1.23.0
## [21] matrixStats_1.5.0 dplyr_1.1.4
## [23] BiocStyle_2.38.0
##
## loaded via a namespace (and not attached):
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## [4] filelock_1.0.3 tibble_3.3.0 XML_3.99-0.20
## [7] lifecycle_1.0.4 httr2_1.2.1 aricode_1.0.3
## [10] edgeR_4.9.0 doParallel_1.0.17 lattice_0.22-7
## [13] alabaster.base_1.10.0 magrittr_2.0.4 limma_3.67.0
## [16] sass_0.4.10 rmarkdown_2.30 jquerylib_0.1.4
## [19] yaml_2.3.10 metapod_1.19.0 askpass_1.2.1
## [22] reticulate_1.44.0 cowplot_1.2.0 LiblineaR_2.10-24
## [25] DBI_1.2.3 buildtools_1.0.0 RColorBrewer_1.1-3
## [28] abind_1.4-8 purrr_1.2.0 RCurl_1.98-1.17
## [31] rappdirs_0.3.3 circlize_0.4.16 ggrepel_0.9.6
## [34] irlba_2.3.5.1 alabaster.sce_1.10.0 maketools_1.3.2
## [37] pheatmap_1.0.13 umap_0.2.10.0 RSpectra_0.16-2
## [40] dqrng_0.4.1 codetools_0.2-20 DelayedArray_0.37.0
## [43] shape_1.4.6.1 tidyselect_1.2.1 UCSC.utils_1.7.0
## [46] farver_2.1.2 ScaledMatrix_1.19.0 viridis_0.6.5
## [49] BiocFileCache_3.0.0 GenomicAlignments_1.47.0 jsonlite_2.0.0
## [52] GetoptLong_1.0.5 BiocNeighbors_2.4.0 iterators_1.0.14
## [55] foreach_1.5.2 tools_4.5.2 Rcpp_1.1.0
## [58] glue_1.8.0 gridExtra_2.3 SparseArray_1.11.1
## [61] xfun_0.54 flexclust_1.5.0 GenomeInfoDb_1.47.0
## [64] HDF5Array_1.39.0 gypsum_1.7.0 withr_3.0.2
## [67] BiocManager_1.30.26 fastmap_1.2.0 rhdf5filters_1.23.0
## [70] bluster_1.21.0 openssl_2.3.4 SparseM_1.84-2
## [73] digest_0.6.37 rsvd_1.0.5 R6_2.6.1
## [76] colorspace_2.1-2 Cairo_1.7-0 RSQLite_2.4.4
## [79] cigarillo_1.1.0 h5mread_1.3.0 rtracklayer_1.69.1
## [82] class_7.3-23 httr_1.4.7 S4Arrays_1.11.0
## [85] uwot_0.2.4 pkgconfig_2.0.3 gtable_0.3.6
## [88] modeltools_0.2-24 blob_1.2.4 S7_0.2.0
## [91] XVector_0.51.0 sys_3.4.3 htmltools_0.5.8.1
## [94] clue_0.3-66 ProtGenerics_1.43.0 scales_1.4.0
## [97] alabaster.matrix_1.10.0 png_0.1-8 scran_1.39.0
## [100] knitr_1.50 reshape2_1.4.4 rjson_0.2.23
## [103] curl_7.0.0 GlobalOptions_0.1.2 cachem_1.1.0
## [106] rhdf5_2.55.7 stringr_1.6.0 BiocVersion_3.23.1
## [109] parallel_4.5.2 vipor_0.4.7 ggrastr_1.0.2
## [112] restfulr_0.0.16 pillar_1.11.1 alabaster.schemas_1.10.0
## [115] vctrs_0.6.5 RANN_2.6.2 BiocSingular_1.27.0
## [118] dbplyr_2.5.1 beachmat_2.26.0 cluster_2.1.8.1
## [121] beeswarm_0.4.0 evaluate_1.0.5 magick_2.9.0
## [124] cli_3.6.5 locfit_1.5-9.12 compiler_4.5.2
## [127] Rsamtools_2.27.0 rlang_1.1.6 crayon_1.5.3
## [130] labeling_0.4.3 plyr_1.8.9 ggbeeswarm_0.7.2
## [133] stringi_1.8.7 viridisLite_0.4.2 alabaster.se_1.10.0
## [136] BiocParallel_1.45.0 Biostrings_2.79.2 lazyeval_0.2.2
## [139] Matrix_1.7-4 ExperimentHub_3.1.0 sparseMatrixStats_1.23.0
## [142] bit64_4.6.0-1 Rhdf5lib_1.33.0 KEGGREST_1.51.0
## [145] statmod_1.5.1 alabaster.ranges_1.10.0 AnnotationHub_4.0.0
## [148] igraph_2.2.1 memoise_2.0.1 bslib_0.9.0
## [151] bit_4.6.0References
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