View source: R/calculate_sig_score.R
calculate_sig_score | R Documentation |
Calculating signature score on a gene expression dataset
calculate_sig_score(
pdata = NULL,
eset,
signature = signature_collection,
method = "pca",
mini_gene_count = 3,
column_of_sample = "ID",
print_gene_propotion = FALSE,
adjust_eset = FALSE,
parallel.size = 1L,
print_filtered_signatures = FALSE,
...
)
pdata |
phenotype data of input sample |
eset |
normalized transcriptomic data: normalized (CPM, TPM, RPKM, FPKM, etc.) |
signature |
List of gene signatures; such as 'signature_tme', 'signature_metabolism','signature_collection', 'go_bp','kegg','hallmark' |
method |
he methods currently supported are 'pca', 'ssgsea', 'zscore','integration' |
mini_gene_count |
filter out signatures with genes less than minimal gene in expression set; default is 2 for PCA and z score function |
column_of_sample |
Defines in which column of the 'pdata' the identifier of the sample can be found. |
print_gene_propotion |
logical, print the proportion of signature genes in gene matrix |
adjust_eset |
default is FALSE |
parallel.size |
default is 1 |
print_filtered_signatures |
logical, print filtered signatures has gene count less than minimal gene count |
data frame with pdata and signature scores for gene sets; signatures in columns, samples in rows
Dongqiang Zeng
1. Hänzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7. doi: 10.1186/1471-2105-14-7
2. Mariathasan, S., Turley, S., Nickles, D. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
# Loading TCGA-STAD expresion data(raw count matrix)
data("eset_stad", package = "IOBR")
# transform count data to tpm
eset <- count2tpm(eset_stad, idType = "ensembl")
# signature score estimation using PCA, z-score, or ssgsea method
calculate_sig_score(eset = eset, signature = signature_tme, method = "pca")
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