Cell fraction estimation using SVR or lsei methods with custom reference.
Usage
deconvo_ref(
eset,
project = NULL,
arrays = TRUE,
method = c("svr", "lsei"),
perm = 100,
reference,
scale_reference = TRUE,
absolute.mode = FALSE,
abs.method = "sig.score"
)Arguments
- eset
Gene expression matrix.
- project
Optional project name. Default is `NULL`.
- arrays
Logical: use quantile normalization. Default is `TRUE`.
- method
Method: `"svr"` or `"lsei"`. Default is `"svr"`.
- perm
Permutations for SVR. Default is 100.
- reference
Custom reference matrix (e.g., lm22, lm6).
- scale_reference
Logical: scale reference. Default is `TRUE`.
- absolute.mode
Logical: absolute mode for SVR. Default is `FALSE`.
- abs.method
Method for absolute mode. Default is `"sig.score"`.
Examples
# Simulate data
set.seed(123)
sim_ref <- matrix(rnorm(100 * 5), 100, 5)
rownames(sim_ref) <- paste0("Gene", 1:100)
colnames(sim_ref) <- paste0("CellType", 1:5)
sim_eset <- matrix(rnorm(100 * 3), 100, 3)
rownames(sim_eset) <- paste0("Gene", 1:100)
colnames(sim_eset) <- paste0("Sample", 1:3)
# Run deconvolution
result <- deconvo_ref(eset = sim_eset, reference = sim_ref, method = "lsei")
#> ℹ Found 100 common genes
#> ℹ Running lsei deconvolution
if (!is.null(result)) head(result)
#> ID CellType1_CIBERSORT CellType2_CIBERSORT CellType3_CIBERSORT
#> 1 1 0.2 0.2 0.2
#> 2 2 0.2 0.2 0.2
#> 3 3 0.2 0.2 0.2
#> CellType4_CIBERSORT CellType5_CIBERSORT
#> 1 0.2 0.2
#> 2 0.2 0.2
#> 3 0.2 0.2