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
lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
sim_eset <- as.data.frame(matrix(
rnorm(length(common_genes) * 5, mean = 5, sd = 2),
nrow = length(common_genes), ncol = 5
))
rownames(sim_eset) <- common_genes
colnames(sim_eset) <- paste0("Sample", 1:5)
deconvo_ref(eset = sim_eset, reference = lm22, method = "lsei")
#> ℹ Found 500 common genes
#> ℹ Running lsei deconvolution
#> ID B_cells_naive_CIBERSORT B_cells_memory_CIBERSORT Plasma_cells_CIBERSORT
#> 1 1 0.04545455 0.04545455 0.04545455
#> 2 2 0.04545455 0.04545455 0.04545455
#> 3 3 0.04545455 0.04545455 0.04545455
#> 4 4 0.04545455 0.04545455 0.04545455
#> 5 5 0.04545455 0.04545455 0.04545455
#> T_cells_CD8_CIBERSORT T_cells_CD4_naive_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> T_cells_CD4_memory_resting_CIBERSORT T_cells_CD4_memory_activated_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> T_cells_follicular_helper_CIBERSORT T_cells_regulatory_(Tregs)_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> T_cells_gamma_delta_CIBERSORT NK_cells_resting_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> NK_cells_activated_CIBERSORT Monocytes_CIBERSORT Macrophages_M0_CIBERSORT
#> 1 0.04545455 0.04545455 0.04545455
#> 2 0.04545455 0.04545455 0.04545455
#> 3 0.04545455 0.04545455 0.04545455
#> 4 0.04545455 0.04545455 0.04545455
#> 5 0.04545455 0.04545455 0.04545455
#> Macrophages_M1_CIBERSORT Macrophages_M2_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> Dendritic_cells_resting_CIBERSORT Dendritic_cells_activated_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> Mast_cells_resting_CIBERSORT Mast_cells_activated_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455
#> Eosinophils_CIBERSORT Neutrophils_CIBERSORT
#> 1 0.04545455 0.04545455
#> 2 0.04545455 0.04545455
#> 3 0.04545455 0.04545455
#> 4 0.04545455 0.04545455
#> 5 0.04545455 0.04545455