Nothing
## ----load_library_fake, eval=FALSE---------------------------------------
# ## Install
# source("http://bioconductor.org/biocLite.R")
# biocLite("gaucho")
# ## Load
# library(gaucho)
## ----demontrate_proportions, eval=TRUE-----------------------------------
## First sample is reprented by first block of three digits
## First sample is 100% clone 1, 0% clone 2, 0% clone 3
c(5,0,0)/sum(c(5,0,0))
## Second sample is reprented by second block of three digits
## Second sample is 30% clone 1, 70% clone 2, 0% clone 3
c(3,7,0)/sum(c(3,7,0))
## Third sample is reprented by third block of three digits
## Third sample is 0% clone 1, 20% clone 2, 80% clone 3
c(0,1,4)/sum(c(0,1,4))
## ----emit_results, eval=FALSE--------------------------------------------
# ## Write results to files
# gauchoReport(gaucho_input_dataframe,gaucho_output_object)
## ----gaucho_simple_data_show, eval=TRUE----------------------------------
## Load library
library(gaucho)
## Load simple data set
gaucho_simple_data = read.table(file.path(system.file("extdata",package="gaucho"),"gaucho_simple_data.txt"),header=TRUE,row.names=1)
## There are three columns (time points T0, T1 and T2)
## and three rows (mutations M1, M2, M3)
gaucho_simple_data
## ----gaucho_simple_data_execute, eval=FALSE------------------------------
# ## Execute gaucho() function on the simple data set
# simpleDataSolution=gaucho(gaucho_simple_data, number_of_clones=3, nroot=1,iterations=1000)
## ----gaucho_simple_data_report, eval=FALSE-------------------------------
# ## Produce plots for the phylogeny, heatmap and proportions in turn
# gauchoReport(gaucho_simple_data,simpleDataSolution,outType="phylogeny")
# gauchoReport(gaucho_simple_data,simpleDataSolution,outType="heatmap")
# gauchoReport(gaucho_simple_data,simpleDataSolution,outType="proportion")
#
# ## Create output files representing the solution(s) in the current working directory
# gauchoReport(gaucho_simple_data,simpleDataSolution,outType="complete")
#
# ## In case you want to know the current working directory, it can be reported using this function:
# getwd()
## ----gaucho_hidden_data_load, eval=TRUE----------------------------------
## Load library
library(gaucho)
## Load hidden data set
= read.table(file.path(system.file("extdata",package="gaucho"),"gaucho_hidden_data.txt"),header=TRUE,row.names=1)
## There are three columns (time points T0, T1 and T2)
## and five rows (mutations M1, M2, M3, M4 and M5)
## ----gaucho_hidden_data_results, eval=FALSE------------------------------
# ## Produce plots for the phylogeny, heatmap and proportions in turn
# gauchoReport(gaucho_hidden_data,hiddenDataSolution,outType="phylogeny")
# gauchoReport(gaucho_hidden_data,hiddenDataSolution,outType="heatmap")
# gauchoReport(gaucho_hidden_data,hiddenDataSolution,outType="proportion")
#
# ## Create output files representing the solution(s) in the current working directory
# gauchoReport(gaucho_simple_data,simpleDataSolution,outType="complete")
## ----gaucho_synthetic_data, eval=FALSE-----------------------------------
# ## Load library
# library(gaucho)
#
# ## Load synthetic data set and the same data with added jitter
# gaucho_synth_data = read.table(file.path(system.file("extdata",package="gaucho"),"gaucho_synth_data.txt"),header=TRUE,row.names=1)
# gaucho_synth_data_jittered = read.table(file.path(system.file("extdata",package="gaucho"),"gaucho_synth_data_jittered.txt"),header=TRUE,row.names=1)
#
# ## Execute gaucho() function on the synthetic data set - we know that
# ## there are 6 clones
# s=gaucho(gaucho_synth_data,number_of_clones=6,iterations=3000)
#
# ## Access solution slot in returned object to show highest scoring solution(s)
# ## The optimum solution's score is -2.22E-15, which is a rounding error from zero
# s@solution
#
# ## Create output files representing the solution(s)
# ## in the current working directory
# gauchoReport(gaucho_synth_data,s)
## ----gaucho_yeast_data, eval=FALSE---------------------------------------
# ## Load library
# library(gaucho)
#
# ## Load yeast data set
# BYB1_G07_pruned = read.table(file.path(system.file("extdata",package="gaucho"),"BYB1_G07_pruned.txt"),header=TRUE,row.names=1)
#
# ## Execute gaucho() function on the yeast data set
# ## The paper claims that there are 6 clones with multiple roots
# yDataSolution=gaucho(BYB1_G07_pruned, number_of_clones=6, contamination=1, iterations=3000)
#
# ## Access solution slot in returned object to show highest scoring solution(s)
# yDataSolution@solution
#
# ## Create output files representing the solution(s) in the current working directory
# gauchoReport(BYB1_G07_pruned,yDataSolution)
## ----gaucho_find_clones, eval=FALSE--------------------------------------
# ## Load library
# library(gaucho)
#
# ## Load simple data set
# gaucho_synth_data = read.table(file.path(system.file("extdata",package="gaucho"),"gaucho_synth_data.txt"),header=TRUE,row.names=1)
#
# ## We know that there are 6 clones in this data set.
# ## Let's loop from number_of_clones=4 to number_of_clones=8 to illustrate this
# ## Also, assume we know nothing about phylogeny, so leave nroot as the default value
#
# ## Assign parameters and create an empty data frame to hold the results
# clonerange=4:8
# n=5
# results=matrix(NA,nrow=iterations,ncol=length(clonerange))
# colnames(results)=paste0(rep("clone",length(clonerange)),clonerange)
#
# ## Execute gaucho n times for each number of clones
# for(c in clonerange){
# for(thisn in 1:n){
# message(paste("Iteration",thisn,"using",c,"clones"))
# s=gaucho(gaucho_synth_data, number_of_clones=c,iterations=1000)
# ## Assign best fitness value to an element in the matrix
# results[thisn,which(c==clonerange)]=s@fitnessValue
# }
# }
#
# ## Plot the results
# boxplot(results)
## ----gaucho_multi_runs, eval=FALSE---------------------------------------
# ## Load library
# library(gaucho)
#
# ## Load simple data set
# gaucho_synth_data = read.table(file.path(system.file("extdata",package="gaucho"),"gaucho_synth_data.txt"),header=TRUE,row.names=1)
#
# ## Execute 20 runs
# n=20
#
# ## Create an empty list to hold all results
# gauchoResults = list()
#
# ## Execute gaucho n times, pushing each resulting object into a list
# for(thisn in 1:n){
# message(paste("Iteration",thisn))
# syntheticDataSolution=gaucho(gaucho_synthetic_data, number_of_clones=6, iterations=10000)
# gauchoResults[thisn]=syntheticDataSolution
# }
#
# ## Find lowest fitness values of all runs
# bestScore = max(sapply(gauchoResults, function(x) x@fitnessValue))
#
# ## Which run was it?
# which(sapply(gauchoResults, function(x) x@fitnessValue)==bestScore)
## ----sessionInfo, eval=TRUE----------------------------------------------
sessionInfo()
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