merge: Merge multiple MCMCout matrices by orthologs

In weiiizong/CAMO: Perform cross-species resemblance analysis

Description

The merge is function to merge multiple MCMCout matrices by matching orthologs.

Usage

merge(mcmc.list, species, ortholog.db, reference = 1, uniqG = T)

Arguments

mcmc.list:	a list of MCMC output matrices.
species:	a vector specie names of same length as mcmc.list indicating the species of each study.
ortholog.db:	a data.frame/matrix match orthologs between species. Column names should be consistent with the species input.
reference:	the index of the reference MCMC matrix. Merged list will be named using the rownames of this matrix.
uniqG:	TRUE: only keep the gene with greatest posterior DE signal when multiple matches provided in the orhthologs file. FALSE: keep duplicated genes when multiple matches exist. default is TRUE.

Value

an merged list of multiple MCMCout datasets (with same number of rows and rownames)

Examples

## Not run: 
data(hb)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
hb_pData = summaryDE[,c(3,1)]
hb_MCMCout = bayes(hb_pData, seed=12345)
data(hs)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
hs_pData = summaryDE[,c(3,1)]
hs_MCMCout = bayes(hs_pData, seed=12345)
data(ht)
summaryDE <- indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
ht_pData <- summaryDE[,c(3,1)]
ht_MCMCout <- bayes(ht_pData, seed=12345)
data(ha)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
ha_pData = summaryDE[,c(3,1)]
ha_MCMCout = bayes(ha_pData, seed=12345)
data(hi)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
hi_pData = summaryDE[,c(3,1)]
hi_MCMCout = bayes(hi_pData, seed=12345)
data(hl)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
hl_pData = summaryDE[,c(3,1)]
hl_MCMCout = bayes(hl_pData, seed=12345)
data(hb)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
mb_pData = summaryDE[,c(3,1)]
mb_MCMCout = bayes(mb_pData, seed=12345)
data(hs)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
ms_pData = summaryDE[,c(3,1)]
ms_MCMCout = bayes(ms_pData, seed=12345)
data(ht)
summaryDE <- indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
mt_pData <- summaryDE[,c(3,1)]
mt_MCMCout <- bayes(mt_pData, seed=12345)
data(ma)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
ma_pData = summaryDE[,c(3,1)]
ma_MCMCout = bayes(ma_pData, seed=12345)
data(hi)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
mi_pData = summaryDE[,c(3,1)]
mi_MCMCout = bayes(mi_pData, seed=12345)
data(ml)
summaryDE = indDE(data=data,group=as.factor(group),data.type="microarray",
                  case.label="2", ctrl.label="1")
ml_pData = summaryDE[,c(3,1)]
ml_MCMCout = bayes(ml_pData, seed=12345)

mcmc.list <- list(hb_MCMCout,hs_MCMCout,ht_MCMCout,
ha_MCMCout,hi_MCMCout,hl_MCMCout,
mb_MCMCout,ms_MCMCout,mt_MCMCout,
ma_MCMCout,mi_MCMCout,ml_MCMCout)
data(hm_orth)
species <- c(rep("human",6), rep("mouse",6))
mcmc.merge.list <- merge(mcmc.list,species = species,
ortholog.db = hm_orth, reference=1,uniqG=T)

## End(Not run)

weiiizong/CAMO documentation built on Dec. 23, 2021, 5:09 p.m.