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robust.clustering.metagenomics_callsExampleDatasets.r
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robust.clustering.metagenomics_callsExampleDatasets.r
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###############################################################
# MAIN PROGRAM (local calls to several datasets at once)
###############################################################
library(ggplot2,quietly=TRUE)
library(phyloseq)
# Warnings with ggplot2 before phyloseq (I hope it is not a problem later)
#Warning messages:
# 1: replacing previous import ‘BiocGenerics::Position’ by ‘ggplot2::Position’ when loading ‘phyloseq’
# 2: replacing previous import ‘S4Vectors::Position’ by ‘ggplot2::Position’ when loading ‘DESeq2’
library(lattice,quietly=TRUE)
library(cluster,quietly=TRUE)
library(fpc,quietly=TRUE)
library(reshape2,quietly=TRUE) # Load the reshape2 package (for the melt() function)
source('robust.clustering.metagenomics.functions.r')
#######################################
# OPTION 1: Only one call per datasets
#######################################
robust.clustering.all.steps("~/RobustClustering/David2014","~/RobustClustering/David2014/data.norm_David2014.RData",'David2014',"COLLECTION_DAY")
robust.clustering.all.steps("~/RobustClustering/Ballou2016","~/RobustClustering/Ballou2016/data.norm_Ballou2016.RData",'Ballou2016',"age")
robust.clustering.all.steps("~/RobustClustering/Gajer2012","~/RobustClustering/Gajer2012/data.norm_Gajer2012.RData",'Gajer2012',"Time.in.study")
robust.clustering.all.steps("~/RobustClustering/LaRosa2014","~/RobustClustering/LaRosa2014/data.norm_LaRosa2014.RData",'LaRosa2014',"age")
# Calls for taxa subset, with David2014 dataset:
# Subset dominant taxa:
robust.clustering.all.steps("~/RobustClustering/David2014","~/RobustClustering/David2014/data.norm_David2014.RData",'David2014',"COLLECTION_DAY",'dominant',percDominant=1)
# Subset nonDominant taxa:
robust.clustering.all.steps("~/RobustClustering/David2014","~/RobustClustering/David2014/data.norm_David2014.RData",'David2014',"COLLECTION_DAY",'nonDominant',percDominant=1)
# Subset all taxa at genes level:
robust.clustering.all.steps("~/RobustClustering/David2014","~/RobustClustering/David2014/data.norm_David2014.RData",'David2014',"COLLECTION_DAY",'all',taxaSubsetGenus='yes')
# Subset dominant taxa at genes level:
robust.clustering.all.steps("~/RobustClustering/David2014","~/RobustClustering/David2014/data.norm_David2014.RData",'David2014',"COLLECTION_DAY",'dominant',percDominant=1,taxaSubsetGenus='yes')
# Subset nonDominant taxa at genes level:
robust.clustering.all.steps("~/RobustClustering/David2014","~/RobustClustering/David2014/data.norm_David2014.RData",'David2014',"COLLECTION_DAY",'nonDominant',percDominant=1,taxaSubsetGenus='yes')
###########################################################
# OPTION 2: Call to the independent functions, step by step
###########################################################
# A) ROBUST CLUSTERING COMPUTATION
setwd("RobustClustering/David2014")
load('data.norm_David2014.RData')
list.results <- robust.clustering(data.norm,"COLLECTION_DAY","David2014")
rm(data.norm)
setwd("RobustClustering/Ballou2016")
load('data.norm_Ballou2016.RData')
list.results <- robust.clustering(data.norm,"age","Ballou20106")
rm(data.norm)
setwd("RobustClustering/Gajer2012")
load('data.norm_Gajer2012.RData')
list.results <- robust.clustering(data.norm,"Time.in.study","Gajer2012")
rm(data.norm)
setwd("RobustClustering/LaRosa2014")
load('data.norm_LaRosa2014.RData')
list.results <- robust.clustering(data.norm,"age","LaRosa2014")
rm(data.norm)
########################################
# B) PLOT GRAPHS
# Using next loop requires you are in a directory where there are a folder named as the dataset (ej. 'RobustClustering/' with subfolders 'David2014', 'Ballou2016', etc). Each subfolder should have at least the RData with the phyloseq object called data.norm (in these examples <data.norm_XXdatasetXX.RData>).
for (dataset in c('David2014','Ballou2016','Gajer2012','LaRosa2014')){
setwd(dataset)
load(paste('eval.arrays_',dataset,'.RData',sep=''))
plot.robust.clustering.all.together.formatted(eval.array4d, eval.SIpoints, eval.Kvalues)
setwd('..')
} # end-for
########################################
# C) AUTOMATIC CLUSTERING DECISION
for (dataset in c('David2014','Ballou2016','Gajer2012','LaRosa2014')){
setwd(dataset)
load(paste('data.norm_',dataset,'.RData',sep=''))
load(paste('eval.arrays_',dataset,'.RData',sep=''))
switch(dataset,
David2014={ var.graph="COLLECTION_DAY" },
Chicks={ var.graph="age" },
Gajer2012={ var.graph="Time.in.study" },
LaRosa2014={ var.graph="age" },
{ var.graph=NULL;
print('ERROR: dataset without var.color defined!!')}
) #end-switch-alg
data.norm <- robust.clustering.decision(data.norm,eval.array4d,var.graph)
rm(data.norm)
setwd('..')
} # end-for