Cardelino

cardelino

Workflows

Standard Workflow

Assign cells to clones using scRNA-seq variant counts and a pre-defined clonal tree configuration.

library(cardelino)
library(ggplot2)

# 1. Load scRNA-seq variant data
vcf_file <- system.file("extdata", "cellSNP.cells.vcf.gz", package = "cardelino")
input_data <- load_cellSNP_vcf(vcf_file)

# 2. Load clonal tree configuration and align variant IDs
canopy_res <- readRDS(system.file("extdata", "canopy_results.coveraged.rds", package = "cardelino"))
Config <- canopy_res$tree$Z
rownames(Config) <- gsub(":", "_", rownames(Config))

# 3. Run Bayesian clustering using clone_id with the configuration matrix
set.seed(7)
assignments <- clone_id(input_data$A, input_data$D, Config = Config, min_iter = 800, max_iter = 1200)

# 4. Assign cells to clones
df <- assign_cells_to_clones(assignments$prob)

# 5. Visualize results
prob_heatmap(assignments$prob)
heat_matrix(t(assignments$Config_prob - Config))

AF <- as.matrix(input_data$A / input_data$D)
cardelino::vc_heatmap(AF, assignments$prob, Config, show_legend=TRUE)

Note: Inputs are variant-by-cell matrices of alternative allele counts (A) and total coverage (D), plus a prior configuration matrix (Config); output is a list containing posterior assignment probabilities and updated clonal configurations.

Clone Id Denovo

Infer clonal structure and assign cells to clones de-novo without a pre-defined clonal tree, typically using mitochondrial or high-coverage variants.

library(cardelino)

# 1. Read AD and DP matrices and assign variant and cell names
AD_file <- system.file("extdata", "passed_ad.mtx", package = "cardelino")
DP_file <- system.file("extdata", "passed_dp.mtx", package = "cardelino")
id_file <- system.file("extdata", "passed_variant_names.txt", package = "cardelino")

AD <- Matrix::readMM(AD_file)
DP <- Matrix::readMM(DP_file)
var_ids <- read.table(id_file)
rownames(AD) <- rownames(DP) <- var_ids[, 1]
colnames(AD) <- colnames(DP) <- paste0('Cell', seq(ncol(DP)))

# 2. Run de-novo Bayesian clustering using clone_id with Config=NULL
set.seed(7)
assign_mtClones <- clone_id(AD, DP, Config=NULL, n_clone = 3, keep_base_clone=FALSE)

# 3. Threshold the posterior configuration probability matrix
Config_mt <- assign_mtClones$Config_prob
Config_mt[Config_mt >= 0.5] = 1
Config_mt[Config_mt < 0.5] = 0

# 4. Visualize cell assignments and clonal configurations
AF_mt <- as.matrix(AD / DP)
cardelino::vc_heatmap(AF_mt, assign_mtClones$prob, Config_mt, show_legend=TRUE)

Note: Inputs are sparse matrices of alternative (AD) and total (DP) counts without a prior tree; output is a de-novo clonal assignment and inferred configuration matrix.

When to Use

• To assign single-cell transcriptomes to individual clones using sparse variant information from scRNA-seq reads.
• To integrate prior clonal tree configurations (e.g., from bulk DNA-seq using Canopy) with single-cell RNA-seq variant counts.
• To perform de-novo clonal clustering on mitochondrial variations (e.g., from MQuad) without a prior clonal tree.

When NOT to Use

• Do not use if you do not have variant-level coverage and alternative allele count matrices (A and D) extracted from single-cell sequencing reads.
• Do not use for bulk-only clonal tree reconstruction; use tools like Canopy instead.

Data Requirements

• Matrix A (or AD): variant x cell matrix of integer counts supporting the alternative allele.
• Matrix D (or DP): variant x cell matrix of integer counts providing the total read coverage at each site.
• Config: variant x clone binary configuration matrix (optional for de-novo mode).

Key Parameters

• Config (NULL): Prior variant-by-clone configuration matrix. Set to NULL for de-novo mode.
• n_clone (3): Number of clones to infer when Config is NULL.
• min_iter (800): Minimum number of Gibbs sampling iterations.
• max_iter (1200): Maximum number of Gibbs sampling iterations.
• keep_base_clone (FALSE): Logical indicating whether to keep the first clone as a base clone with no mutations.

Best Practices

• Ensure variant IDs are perfectly matched between the scRNA-seq matrices (A and D) and the prior configuration matrix (Config).
• Use the Geweke z-statistic (checked automatically in clone_id) to ensure Gibbs sampling convergence.
• For de-novo mode, run clone_id multiple times with different random seeds and select the run with the highest DIC to avoid local optima.

Common Pitfalls

• Gibbs sampler not converging: Increase min_iter and max_iter parameters.
• Local optima in de-novo mode: Run multiple initializations with different seeds.

Alternatives

• Canopy for bulk DNA-seq clonal tree inference.
• MQuad for mitochondrial variant calling.

Citations

• McCarthy, D., & Huang, Y. (2026). cardelino: Clonal tree configuration and cell assignment from single-cell data.

References

• Homepage: bioconductor.org/packages/cardelino
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cardelino/inst/doc/vignette-cloneid.html