## ----echo=FALSE, warning=FALSE, message=FALSE--------------------------------- devtools::load_all('.') ## ----warning=FALSE, message=FALSE--------------------------------------------- library('yeastExpData') ## ----echo=TRUE---------------------------------------------------------------- data(ccyclered) head(ccyclered) ## ----------------------------------------------------------------------------- clusters <- ccyclered$Cluster ### convert from Gene names to the new standard of Saccharomyces Genome Database (SGD) gene ids ccyclered$SGDID <- sub('^S','S00',ccyclered$SGDID) names(clusters) <- ccyclered$SGDID str(clusters) ## ----fig.show='hold', echo=TRUE----------------------------------------------- data(Yeast.GO.assocs); str(Yeast.GO.assocs); head(Yeast.GO.assocs); validate_association(Yeast.GO.assocs) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # library(biomaRt) # rn <- useDataset("rnorvegicus_gene_ensembl", mart=useMart("ensembl")) # rgd.symbol=c("As3mt", "Borcs7", "Cyp17a1", "Wbp1l", "Sfxn2", "Arl3") ### exemplify for a limited set of genes # entity.attr <- getBM(attributes=c('rgd_symbol','go_id'), filters='rgd_symbol', values=rgd.symbol, mart=rn) ## ----fig.width=6, fig.height=4------------------------------------------------ entities_attribute_stats(Yeast.GO.assocs) ### shows number of entities per attribute distribution Yeast.GO.assocs.cons1 <- consolidate_entity_attribute( entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 ### keep only attributes associated to 3 or more entities , mut.inf=FALSE ) dim(Yeast.GO.assocs) dim(Yeast.GO.assocs.cons1) ### shows reduction in the number of associations ## ----fig.width=6, fig.height=4------------------------------------------------ data(mi.GO.Yeast) Yeast.GO.assocs.cons <- consolidate_entity_attribute( entity.attribute = Yeast.GO.assocs , min.entities.per.attr =3 , mut.inf=mi.GO.Yeast ### use precalculated mutual information , U.limit = c(0.1, 0.001) ### calculate consolidated association for these uncertainty levels ) ### shows distribution of the number of pairs of attributes by Uncertainty str(Yeast.GO.assocs.cons) ## ----------------------------------------------------------------------------- data(Yeast.GO.assocs) ### because it takes time, we use a small sampled subset of associations entity.attribute.sampled <- Yeast.GO.assocs[sample(1:nrow(Yeast.GO.assocs),100),] mi.GO.Yeast.sampled <- attribute_mut_inf( entity.attribute = entity.attribute.sampled , show.progress = FALSE ## for this small example do not print progress ) str(mi.GO.Yeast.sampled) ## ----fig.width=6, fig.height=4------------------------------------------------ mi.by.swaps<-clusterJudge( clusters = clusters , entity.attribute=Yeast.GO.assocs.cons[["0.001"]] , plot.notes='Yeast clusters judged at uncertainty level 0.001 - Ref: Tavazoie S,& all `Systematic determination of genetic network architecture. Nat Genet. 1999`' , plot.saveRDS.file= 'cj.rds') ### save the plot for later use p <- readRDS('cj.rds') ### retrieve the previous plot pdf('cj.pdf'); plot(p); dev.off() ### plot on another device