KOGMWU (version 1.2)

KOGMWU-package: Functional summary and meta-analysis of gene expression data

Description

Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.

Details

Package:	KOGMWU
Type:	Package
Version:	1.2
Date:	2019-02-19
License:	GPL-3

The most important function is kog.mwu, which performs a series of Mann-Whitney U tests when given two data tables: one, containing measures of interest for each gene (for example, log fold-change), and another, listing the association of each gene with a KOG class. The KOG class annotations for a collection of genes can be obtained using eggNOG-mapper: http://eggnogdb.embl.de/#/app/emapper. To extract KOG annotations understood by this package out of the eggNOG-mapper output, see here: https://github.com/z0on/emapper_to_GOMWU_KOGMWU

References

Dixon, G. B., Davies, S. W., Aglyamova, G. V., Meyer, E., Bay, L. K. and Matz, M. V. Genomic determinants of coral heat tolerance across latitudes. Science 2015, 348:1460-1462. eggNOG-mapper to obtain KOG annotations: http://eggnogdb.embl.de/#/app/emapper To extract KOG annotations from eggNOG-mapper output: https://github.com/z0on/emapper_to_GOMWU_KOGMWU

Examples

# NOT RUN {
data(adults.3dHeat.logFoldChange)
data(larvae.longTerm)
data(larvae.shortTerm)
data(gene2kog)

# Analyzing adult coral response to 3-day heat stress:
alfc.lth=kog.mwu(adults.3dHeat.logFoldChange,gene2kog) 
alfc.lth 

# coral larvae response to 5-day heat stress:
l.lth=kog.mwu(larvae.longTerm,gene2kog)
l.lth

# coral larvae response to 4-hour heat stress 
l.sth=kog.mwu(larvae.shortTerm,gene2kog)
l.sth

# compiling a table of delta-ranks to compare these results:
ktable=makeDeltaRanksTable(list("adults.long"=alfc.lth,"larvae.long"=l.lth,"larvae.short"=l.sth))

# Making a heatmap with hierarchical clustering trees: 
pheatmap(as.matrix(ktable),clustering_distance_cols="correlation") 

# exploring correlations between datasets
pairs(ktable, lower.panel = panel.smooth, upper.panel = panel.cor)
# p-values of these correlations in the upper panel:
pairs(ktable, lower.panel = panel.smooth, upper.panel = panel.cor.pval)

# plotting individual delta-rank correlations:
corrPlot(x="adults.long",y="larvae.long",ktable)
corrPlot(x="larvae.short",y="larvae.long",ktable)
# }