neat (version 1.2.4)

yeast: List collecting various yeast data (see 'description')

Description

yeast is a list that contains:

yeastnet: network matrix representing Yeastnet-v3 (Kim et al., 2013)

ynetgenes: vector with the names of the genes appearing in yeastnet

esr1: vector containing the first of the two gene sets that constitute the "Environmental Stress Response" (ESR) reported by Gasch et al. (2012)

esr2: vector containing the second gene set of the ESR

goslimproc: list containing the gene sets of the GOslim process ontology (Ashburner et al., 2000) for the buddying yeast Saccaromyces Cerevisiae (groups 'biological process' and 'other' are not included)

kegg: list containing the KEGG pathways (Kanehisa and Goto, 2002) for the buddying yeast Saccaromyces Cerevisiae

Format

yeast: list

References

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000). Gene ontology: tool for the unification of biology. Nat. Genet., 25(1), 25-29.

Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., and Brown, P. O. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11(12), 4241-4257.

Kanehisa, M., and Goto, S. (2002). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res., 28(1), 27-30.

Kim, H., Shin, J., Kim, E., Kim, H., Hwang, S., Shim, J. E., and Lee, I. (2013). Yeastnet v3: a public database of data-specific and integrated functional gene networks for saccharomyces cerevisiae. Nucleic Acids Res., 42 (D1), D731-6.

Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.

See Also

neat

Examples

if (FALSE) {
data(yeast)
esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2)
test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet,
            nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01)
# Replace with "blist = yeast$kegg" to use kegg pathways

m = dim(test)[1]
test1 = test[1:(m/2),]
o1=test1[test1$conclusion=='Overenrichment',]
# list of overenrichments for the first ESR set:
print(o1, nrows='ALL') 

test2 = test[(m/2+1):m,]
o2=test2[test2$conclusion=='Overenrichment',]
# list of overenrichments for the second ESR set:
print(o2, nrows='ALL')

# the same can be done using KEGG pathways:
keggtest = neat(alist = esr, blist = yeast$kegg, network = yeast$yeastnet,
            nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01)
}