netgsa (version 4.0.5)

NetGSAq: "Quick" 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)

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 \(\epsilon\).

s2.gamma

Variance of the random effects \(\gamma\).

graph

List of components needed in plot.NetGSA.

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

Author

Michael Hellstern

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.

References

Ma, J., Shojaie, A. & Michailidis, G. (2016) Network-based pathway enrichment analysis with incomplete network information. Bioinformatics 32(20):165--3174. tools:::Rd_expr_doi("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. https://pubmed.ncbi.nlm.nih.gov/20597848/.

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

See Also

prepareAdjMat, netEst.dir, netEst.undir

Examples

# \donttest{
# Example takes ~3 minutes to run depending on computer
## load the data
data("breastcancer2012_subset")

## consider genes from just 2 pathways
genenames    <- unique(c(pathways[["Adipocytokine signaling pathway"]], 
                         pathways[["Adrenergic signaling in cardiomyocytes"]]))
sx           <- x[match(rownames(x), genenames, nomatch = 0L) > 0L,]

out_clusterq <- NetGSAq(sx, group, pathways_mat[c(1, 2), rownames(sx)])
# }