NetGSAq: "Quick" Network-based Gene Set Analysis

In drjingma/netgsa: Network-Based Gene Set Analysis

Description

Quick version of NetGSA

Usage

NetGSAq(x, group, pathways, lambda_c = 1, file_e = NULL, file_ne = NULL,
    lklMethod="REHE", cluster = TRUE, sampling = TRUE, sample_n = NULL,
    sample_p = NULL, minsize=5, eta=0.1, lim4kappa=500)

Arguments

x	See x argument in NetGSA
group	See group argument in NetGSA
pathways	See pathways argument in NetGSA
lambda_c	See lambda_c argument in prepareAdjMat
file_e	See file_e argument in prepareAdjMat
file_ne	See file_ne argument in prepareAdjMat
lklMethod	See lklMethod argument in NetGSA
cluster	See cluster argument in prepareAdjMat
sampling	See sampling argument in NetGSA
sample_n	See sample_n argument in NetGSA
sample_p	See sample_p argument in NetGSA
minsize	See minsize argument in NetGSA
eta	See eta argument in NetGSA
lim4kappa	See lim4kappa argument in NetGSA

Details

This is a wrapper function to perform weighted adjacency matrix estimation and pathway enrichment in one step. For more details see ?prepareAdjMat and ?NetGSA.

Value

A list with components

results	A data frame with pathway names, pathway sizes, p-values and false discovery rate corrected q-values, and test statistic for all pathways.
beta	Vector of fixed effects of length kp, the first k elements corresponds to condition 1, the second k to condition 2, etc.
s2.epsilon	Variance of the random errors ε.
s2.gamma	Variance of the random effects γ.
graph	List of components needed in plot.NetGSA.

Author(s)

Michael Hellstern

References

Ma, J., Shojaie, A. & Michailidis, G. (2016) Network-based pathway enrichment analysis with incomplete network information. Bioinformatics 32(20):165–3174. https://doi.org/10.1093/bioinformatics/btw410

Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical applications in genetics and molecular biology, 9(1), Article 22. http://www.ncbi.nlm.nih.gov/pubmed/20597848.

Shojaie, A., & Michailidis, G. (2009). Analysis of gene sets based on the underlying regulatory network. Journal of Computational Biology, 16(3), 407-426. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3131840/

See Also

prepareAdjMat, netEst.dir, netEst.undir

Examples

## load the data
data("breastcancer2012")

## consider genes from the "ErbB signaling pathway" and "Jak-STAT signaling pathway"
genenames    <- unique(c(pathways[[24]], pathways[[52]]))
sx           <- x[match(rownames(x), genenames, nomatch = 0L) > 0L,]

out_clusterq <- NetGSAq(sx, group, pathways_mat[c(24, 52), rownames(sx)])

drjingma/netgsa documentation built on Feb. 22, 2022, 5:18 p.m.