deTS (version 1.0)

tsea.analysis.multiple: Tissue-specific enrichment analysis for multi query gene lists

Description

Tissue-specific enrichment analysis by Fisher's Exact Test for multiple gene list.

Usage

tsea.analysis.multiple(query_gene_list, score, ratio = 0.05, 
p.adjust.method = "BH")

Arguments

query_gene_list

a 0~1 gene~sample table object, row should be gene symbol, column should be sample name. In the table, gene labeled with 1 indicated it is target gene for a given sample, while 0 indicated it is not target in a given sample.

score

a gene tissue-specific score matrix, c("GTEx_t_score" or "ENCODE_z_score"), can be loaded by data(GTEx) or data(ENCODE), the default value is recommended "GTEx_t_score".

ratio

the threshold to define tissue-specific genes (with top t-score or z-score), the default value is 0.05.

p.adjust.method

p.adjust.method, c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")

Value

A data frame with p-value of tissue-specific enrichment result.

Rows stand for tissue names and columns stand for sample names.

Details

Tissue-specific enrichment analysis by Fisher's Exact Test for multiple gene list.

References

Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.

See Also

https://github.com/bsml320/deTS

Examples

# NOT RUN {
data(GWAS_gene_multiple)
data(GTEx_t_score)
query_gene_list = GWAS_gene_multiple
tsea_t_multi = tsea.analysis.multiple(query_gene_list, 
	GTEx_t_score, 0.05, p.adjust.method = "BH")
# }