mseapca (version 1.0)

msea_sub: MSEA by Subramanian et al.

Description

This function performs metabolite set enrichment analysis implemented in the same fashion as gene set enrichment analysis (Subramanian et al. 2005). In this function, a permutation procedure is performed for a metabolite set rather than class label. This procedure corresponds to a "gene set" of permutation type in GSEA-P software (Subramanian et al. 2007). A leading-edge subset analysis is also undertaken following the standard GSEA procedure.

Usage

msea_sub(M, D, y, maxiter = 1000)

Arguments

M

list of metbolite set name and metabolite IDs

D

data.frame(metabolite ID, data matix)

y

response variable (e.g. PC score)

maxiter

maximum number of iterations in random permutation (default=1000)

Value

list of normalized enrichment score, p-value and q-value for metabolite set, and the results of leading edge subset

References

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S. & Mesirov, J. P. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550.

Subramanian, A., Kuehn, H., Gould, J., Tamayo, P., Mesirov, J.P. (2007) GSEA-P: A desktop application for Gene Set Enrichment Analysis. Bioinformatics, doi: 10.1093/bioinformatics/btm369.

Examples

	# ------------------------
	#  Set response variable
	# ------------------------
	## T : PC scores
	# y <- T[,1]; 				# 1st PC score

	# -----------------------------------------------------
	#  Preparing metabolome data and metabolite set list
	# -----------------------------------------------------
	## M : metabolite set list, M_ID : metabolite ID
	## Z : data matrix (metabolite IDs * samples)
	# filename <- "C:/pathway.xml"
	# M <- read_pathway(filename) 		# load metabolite set list
	# D <- data.frame(M_ID,Z) 		# preparing dataframe 

	# --------
	#  MSEA 
	# --------
	# P <- msea_sub(M,D,y)			# MSEA by Subramanian et al.