NetGSAq function

"Quick" Network-based Gene Set Analysis

Quick version of NetGSA

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.

Returns

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.

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/

Author(s)

Michael Hellstern

See Also

prepareAdjMat, netEst.dir, netEst.undir

Examples

# 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)])