Core Algorithm for CIBERSORT Deconvolution

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

X <- matrix(rnorm(100), nrow = 10)
y <- rnorm(10)
result <- CoreAlg(X, y, absolute = FALSE, abs_method = "sig.score")