ssea_ora: Single sample enrichment analysis by over representation...

In mseapca: Metabolite Set Enrichment Analysis for Loadings

Single sample enrichment analysis by over representation analysis

Description

This function performs single sample enrichment analysis (SSEA) by over representation analysis (ORA). SSEA performs MSEA by ORA between detected and not detected metabolites in each sample."

Usage

ssea_ora(det_list, det_all, M)

Arguments

det_list	metabolite names of detected metabolites
det_all	metabolite names of all metabolites
M	list of metabolite set and metabolite names

Details

The threshold for determining whether a metabolite is detected or not is typically set by the signal-to-noise (S/N) ratio. If the S/N ratio is unavailable, one might consider using the signal intensity or peak area for each metabolite as an alternative. In such cases, all values below the threshold can be set to 0.

Value

A matrix where each row represents a sample and each column represents a set of metabolites.

Author(s)

Hiroyuki Yamamoto

References

Yamamoto H., Single sample enrichment analysisfor mass spectrometry-based omics data, Jxiv.(2023)

Examples

## Not run: 
data(fasting)
data(pathway)

det_list <- pathway$data$fasting
M <- pathway$fasting
det_all <- unique(c(colnames(fasting$X), as.character(unlist(M)))) 

# SSEA
Z <- ssea_ora(det_list, det_all, M)

## PCA for SSEA score
pca <- prcomp(Z, scale=TRUE)
pca <- pca_loading(pca)

## End(Not run)

mseapca documentation built on Nov. 4, 2023, 5:06 p.m.