msea_ora {mseapca}

MSEA by over representation analysis

Description

This function performs metabolite set enrichment analysis by over representation analysis (ORA). Statistical hypothesis test of cross tabulation is performed by one-sided Fisher's exact test.

Usage

msea_ora(SIG, ALL, M)

Arguments

SIG

Metabolite names of significant metabolites

ALL

Metabolite names of all detected metabolites

M

list of metabolite set name and metabolite name

Value

list of p-value and q-value for metabolite set and selected (significant) metabolite IDs for each metabolite set

Author(s)

Hiroyuki Yamamoto

References

Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA. Global functional profiling of gene expression. Genomics. 2003 Feb;81(2):98-104.

Examples

## Example1 : Metabolome data
data(fasting)
data(pathway)

# pca and pca loading
pca <- prcomp(fasting$X, scale=TRUE)
pca <- pca_loading(pca)

# all detected metabolites
metabolites <- colnames(fasting$X)

# statistically significant negatively correlated metabolites in PC1 loading
SIG <- metabolites[pca$loading$R[,1] < 0 & pca$loading$p.value[,1] < 0.05]
ALL <- metabolites #all detected metabolites

# metabolite set list
M <- pathway$fasting

# MSEA by over representation analysis
B <- msea_ora(SIG, ALL, M)
B$`Result of MSEA(ORA)`

## Example2 : Proteome data
data(covid19)
data(pathway)

X <- covid19$X$proteomics
Y <- covid19$Y
D <- covid19$D
tau <- covid19$tau

protein_name <- colnames(X)

# pls-rog and pls-rog loading
plsrog <- pls_rog(X,Y,D)
plsrog <- plsrog_loading(plsrog)

# statistically significant proteins
index_prot <- which(plsrog$loading$R[,1]>0 & plsrog$loading$p.value[,1]<0.05)
sig_prot <- protein_name[index_prot]

# detected proteins
protein_name <- colnames(X)

# protein set list
M <- pathway$covid19$proteomics

# MSEA by over representation analysis
B <- msea_ora(sig_prot, protein_name, M)
B$`Result of MSEA(ORA)`