CoreAlg: Core Algorithm for CIBERSORT Deconvolution

In IOBR: Immune Oncology Biological Research

Core Algorithm for CIBERSORT Deconvolution

Description

Performs nu-regression using support vector machines (SVM) to estimate cell type proportions. This is the core computational engine of CIBERSORT, using nu-SVR with linear kernel to decompose mixed gene expression signals.

Usage

CoreAlg(X, y, absolute, abs_method)

Arguments

X	Matrix or data frame containing signature matrix (predictor variables). Rows are genes, columns are cell types.
y	Numeric vector containing the mixture sample expression (response variable).
absolute	Logical indicating whether to use absolute space for weights. Default is FALSE (relative proportions).
abs_method	String specifying the method for absolute space weights: '"sig.score"' or '"no.sumto1"'.

Value

List containing:

w

Estimated cell type weights/proportions

mix_rmse

Root mean squared error of the deconvolution

mix_r

Correlation coefficient between observed and predicted mixture

Examples

# Simulate data
set.seed(123)
X <- matrix(rnorm(100), nrow = 10)
rownames(X) <- paste0("Gene", 1:10)
colnames(X) <- paste0("Cell", 1:10)
y <- rnorm(10)
names(y) <- paste0("Gene", 1:10)

# Run core algorithm
result <- CoreAlg(X, y, absolute = FALSE, abs_method = "sig.score")
if (!is.null(result)) str(result)

IOBR documentation built on May 30, 2026, 5:07 p.m.