CLIMB

CLIMB (Cellular-level LInear Model of Bulk deconvolution) deconvolutes bulk RNA-seq data using a single-cell RNA-seq reference dataset. See Methods for the underlying model.

This page documents climb(), the package’s single deconvolution function.

Usage

climb(sc, bulk, mode = "abundance", up.lim = Inf, verbose = TRUE, cancer_pattern = "*",
      min_common_genes = 100, n.iter.subsampling = 5, 
      min.n.cells = 50, n.top_mean.genes = 500, confidence_weighting = TRUE)

Parameters

Required

Parameter	Description
sc	ExpressionSet containing the single-cell RNA-seq reference dataset, with cell-type labels in sc$cellType
bulk	ExpressionSet containing the bulk RNA-seq mixtures to be deconvoluted

Optional

Parameter	Description	Default
mode	"abundance" predicts only cell-type proportions; "expression" additionally predicts cell-type-specific expression	"abundance"
up.lim	Upper bound for coefficients (L-infinity norm constraint)	Inf
verbose	Print progress messages	TRUE
cancer_pattern	String pattern matching the cancer cell-types in sc$cellType; CLIMB assumes differentially expressed genes only for these. Only used in expression-predicting modes ("expression", "all", "all+"); ignored in "abundance" mode	"*" (matches every cell-type)
min_common_genes	Minimum number of genes required in common between bulk and sc	100
n.iter.subsampling	Number of reference sub-sampling iterations per Empirical Bayes round (averaged)	5
min.n.cells	Minimum cells per cell type to sub-sample	50
n.top_mean.genes	Number of genes used for bulk-sample-specific gene selection	500
confidence_weighting	If TRUE, blends props.corrected back toward props.init per sample, weighted by the correlation between that sample’s props.init and the reference’s raw per-cell-type cell counts — improves robustness in poorly-identified cases at a small cost to average-case accuracy. If FALSE, returns the raw second-pass estimate	TRUE

Output

climb() returns a list. Fields present in every mode:

Field	Description
props.init	Cell-type proportions from the initial run (samples × cell-types)
props.corrected	Cell-type proportions after Empirical Bayes sub-sampling (samples × cell-types)

Fields present only in mode = "expression":

Field	Description
coefs	Single-cell-level coefficients after Empirical Bayes correction (samples × cells)
expr.highres	High-resolution cell-type expression (samples × genes × cell-types)
expr.overall	Cell-type expression averaged across all samples

A few additional internal/diagnostic fields (coefs.init, sc_norm, bulk_norm, expr.mapping) are also returned; full documentation of these is coming soon.

Note

cliff() reads expr.highres, expr.overall, and props.corrected from its climb_output argument, so CLIMB must be run with mode = "expression" before its output can be passed to CLIFF — mode = "abundance" alone is not sufficient.

Example

Using the bundled in-vitro dataset (4 leukemia cell lines mixed into 6 bulk samples, Fig. 2):

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

# Predict proportions only
result <- climb(sc = sc.es, bulk = bulk.es, mode = "abundance")
head(result$props.corrected)
head(pData(bulk.es))  # compare to the known true mixing proportions

# Predict proportions and cell-type expression
result <- climb(sc = sc.es, bulk = bulk.es, mode = "expression", n.iter.subsampling = 10)
result$expr.highres[1, 1:5, ]  # first sample, first 5 genes