CLIFF

CLIFF (CeLl-type-specific drug sensItivity From deconvoluted expression proFiles) takes CLIMB’s output and integrates it with drug sensitivity data (AUC values) to identify cell-type-specific drug responses, via an Expectation-Maximization algorithm. See Methods for the underlying model.

This page covers both cliff(), the main deconvolution function, and cliff_gene_selection(), a helper used to pre-select drug-predictive genes before running it.

cliff()

Usage

cliff(climb_output, drug_data, mutation_data = NULL, 
      min.mutation = 0, max.em.steps = 100, 
      mode = "highres", regularization = "none", 
      cancer_pattern = "like")

Parameters

Required

Parameter	Description
climb_output	Output of climb(), run with mode = "expression" — CLIFF requires expr.highres, expr.overall, and props.corrected to be present
drug_data	Data frame with an auc column (drug response values, expected in [0, 1]) and a sample column matching CLIMB’s sample names

Note

drug_data$auc is internally recentered so its mean is exactly 0.5 before fitting. Predicted sensitivities (pi_hat / PI_hat_nk) should therefore be interpreted relative to that recentering, not on the raw input AUC scale.

Optional

Parameter	Description	Default
mutation_data	Matrix (samples × mutations) of binary mutation status (1/0)	NULL
min.mutation	Minimum number of samples with a mutation for it to be included in the model	0
max.em.steps	Maximum number of EM iterations	100
mode	"overall" uses average cell-type expression across all samples (recommended); "highres" uses CLIMB’s per-sample high-resolution expression	"highres"
regularization	"none" (unregularized fit) or "L2" (ridge). Any other value falls back to "none" — lasso ("L1") is not currently implemented	"none"
cancer_pattern	String pattern identifying cancer cell types, for mutation attribution	"like"

Output

cliff() returns an unnamed list of three elements, accessed by position:

Index	Description
[[1]] (PI_hat_nk)	Cell-type-specific drug sensitivity predictions (samples × cell-types)
[[2]] (mutation_data)	The mutation data matrix used in the analysis
[[3]] (climb_prop)	Cell-type proportions from CLIMB

Note

With thousands of genes and only a handful of bulk samples, cliff() is heavily over-parameterized if run on CLIMB’s full gene set — cell-type estimates collapse to nearly identical values. Always pre-select a small set of drug-predictive genes with cliff_gene_selection() first, as shown below.

Example

Using the bundled in-vitro dataset (drug sensitivity of the same 6 mixtures, Fig. 4A):

data(sc.es); data(bulk.es); data(bulk_auc_data)

climb_result <- climb(sc = sc.es, bulk = bulk.es, mode = "expression")

# Subset to one drug/timepoint combination — cliff() takes one 'auc' column at a time
drug_data <- bulk_auc_data[bulk_auc_data$drug == "Cytarabine_48h", ]

# Pre-select genes predictive of this drug, then subset CLIMB's expression output to them
bulk_mat <- t(exprs(bulk.es))[drug_data$sample, ]
sel.genes <- cliff_gene_selection(bulk_mat, drug_data$auc, min.genes = 10)
climb_result$expr.highres <- climb_result$expr.highres[, sel.genes, ]
climb_result$expr.overall <- climb_result$expr.overall[sel.genes, ]

cliff_result <- cliff(climb_output = climb_result,
                       drug_data = drug_data,
                       mode = "overall")

# Averaged across samples, this correlates well (Pearson R > 0.9 for this
# drug/timepoint) with the measured pure cell-line AUC
head(cliff_result[[1]])  # PI_hat_nk matrix
colMeans(cliff_result[[1]])

cliff_gene_selection()

A standalone helper that selects a small set of genes predictive of a drug-response phenotype, by fitting a lasso path across a decreasing grid of penalties and returning the first gene set that clears a minimum size. It is independent of climb()/cliff() and can be used to pre-filter genes before building a bulk expression matrix for deconvolution.

Usage

cliff_gene_selection(bulk_mat, drug_d, min.genes = 10)

Parameters

Required

Parameter	Description
bulk_mat	Matrix of bulk gene expression (samples × genes)
drug_d	Numeric vector of drug sensitivity values (e.g. AUC), one per sample, in the same sample order as bulk_mat

Optional

Parameter	Description	Default
min.genes	Minimum number of genes to select before stopping the lasso path search	10

Output

A character vector of selected gene names (column names of bulk_mat with a non-zero lasso coefficient), or NULL if no penalty in the search grid selects at least min.genes genes.

Example

library(Biobase)
data(bulk.es); data(bulk_auc_data)

drug_data <- bulk_auc_data[bulk_auc_data$drug == "Cytarabine_48h", ]
bulk_mat <- t(exprs(bulk.es))[drug_data$sample, ]

sel.genes <- cliff_gene_selection(bulk_mat, drug_data$auc, min.genes = 10)
length(sel.genes)