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.
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)
#> List of 3
#> $ w : num [1, 1:10] 0.373 0.2 0.301 0 0 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:10] "Cell1" "Cell2" "Cell3" "Cell4" ...
#> $ mix_rmse: num 0.651
#> $ mix_r : num 0.582