deTS (version 1.0)

tsea.expression.decode: Tissue-specific enrichment analysis for RNA-Seq expression profiles

Description

Tissue-specific enrichment analysis to decode whether a given RNA-seq sample (RPKM) with potential confounding effects based on expression profiles.

Usage

tsea.expression.decode(query_mat_normalized_score, score, 
ratio = 0.05, p.adjust.method = "BH")

Arguments

query_mat_normalized_score

a normalized RNA-seq RPKM object, which produced by "tsea.expression.normalization".

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 for RNA-Seq expression profiles.

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

Details

Tissue-specific enrichment analysis for RNA-Seq expression profiles.

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(query_GTEx)
query_matrix = query_GTEx[,1:2]
data(correction_factor)
data(ENCODE_z_score)
query_mat_zscore_nor = tsea.expression.normalization(query_matrix, 
	correction_factor, normalization = "z-score")
tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor, 
	ENCODE_z_score, 0.05, p.adjust.method = "BH")
# }