View source: R/calculate_sig_score.R
| calculate_sig_score | R Documentation |
Main interface for calculating signature scores from gene expression data. Supports PCA, z-score, ssGSEA, and integration methods.
calculate_sig_score(
pdata = NULL,
eset,
signature = NULL,
method = c("pca", "ssgsea", "zscore", "integration"),
mini_gene_count = 3,
column_of_sample = "ID",
print_gene_proportion = FALSE,
print_filtered_signatures = FALSE,
adjust_eset = FALSE,
parallel.size = 1L,
...
)
pdata |
Phenotype data. If 'NULL', created from 'eset' column names. |
eset |
Expression matrix (CPM, TPM, RPKM, FPKM, etc.). |
signature |
List of gene signatures. Can also be a character string naming a built-in signature collection (e.g., '"signature_collection"', '"signature_tme"', '"go_bp"', '"kegg"', '"hallmark"'). |
method |
Scoring method: '"pca"', '"ssgsea"', '"zscore"', or '"integration"'. Default is '"pca"'. |
mini_gene_count |
Minimum genes required per signature. Default is 3 (or 5 for ssGSEA). |
column_of_sample |
Column with sample IDs in 'pdata'. Default is '"ID"'. |
print_gene_proportion |
Logical: print gene coverage. Default is 'FALSE'. |
print_filtered_signatures |
Logical: print filtered signatures. Default is 'FALSE'. |
adjust_eset |
Logical: clean problematic features. Default is 'FALSE'. |
parallel.size |
Parallel workers for ssGSEA. Default is 1. |
... |
Additional arguments passed to specific methods. |
Tibble with phenotype data and signature scores.
Dongqiang Zeng
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis. BMC Bioinformatics. 2013;14:7.
Mariathasan S, et al. TGF\beta attenuates tumour response to PD-L1 blockade.
Nature. 2018;554:544-548.
set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
Signature1 = paste0("Gene", 1:10),
Signature2 = paste0("Gene", 11:20)
)
result <- calculate_sig_score(eset = eset, signature = signature, method = "pca")
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