## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the fold-change measure
# DEGs <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "foldchange",
# stage.names = c("S1","S2","S3"))
#
#
# head(DEGs)
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the logfold-change measure
# log.DEGs <- DiffGenes(ExpressionSet = tf(PhyloExpressionSetExample[1:5,1:8],log2),
# nrep = 2,
# method = "log-foldchange",
# stage.names = c("S1","S2","S3"))
#
#
# head(log.DEGs)
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by a Welch t-test
# ttest.DEGs <- DiffGenes(ExpressionSet = tf(PhyloExpressionSetExample[1:5,1:8],log2),
# nrep = 2,
# method = "t.test",
# stage.names = c("S1","S2","S3"))
#
# # look at the results
# ttest.DEGs
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by a Welch t-test
# # and furthermore, adjust p-values for multiple comparison
# # using the Benjamini & Hochberg (1995) method: method = "BH"
# ttest.DEGs.p_adjust <- DiffGenes(ExpressionSet = tf(PhyloExpressionSetExample[1:5,1:8],log2),
# nrep = 2,
# method = "t.test",
# p.adjust.method = "BH",
# stage.names = c("S1","S2","S3"))
#
#
# ttest.DEGs.p_adjust
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # Detection of DEGs using the p-value returned by a Welch t-test
# # and furthermore, adjust p-values for multiple comparison
# # using the Benjamini & Hochberg (1995) method: method = "BH"
# # and filter for significantly differentially expressed genes (alpha = 0.05)
# ttest.DEGs.p_adjust.filtered <- DiffGenes(ExpressionSet = tf(PhyloExpressionSetExample[1:10 ,1:8],log2),
# nrep = 2,
# method = "t.test",
# p.adjust.method = "BH",
# stage.names = c("S1","S2","S3"),
# comparison = "above",
# alpha = 0.05,
# filter.method = "n-set",
# n = 1)
#
# # look at the genes fulfilling the filter criteria
# ttest.DEGs.p_adjust.filtered
## ----eval = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#
# ttest.DEGs.p_adjust <- DiffGenes(ExpressionSet = tf(PhyloExpressionSetExample[1:500,1:8],log2),
# nrep = 2,
# method = "t.test",
# p.adjust.method = "BH",
# stage.names = c("S1","S2","S3"))
#
#
# head(ttest.DEGs.p_adjust[order(ttest.DEGs.p_adjust[ , "S1<->S2"], decreasing = FALSE) , 1:3])
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by a Wilcoxon-Mann-Whitney test
# Wilcox.DEGs <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "wilcox.test",
# stage.names = c("S1","S2","S3"))
#
# # look at the results
# Wilcox.DEGs
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by a Wilcoxon-Mann-Whitney test
# # and furthermore, adjust p-values for multiple comparison
# # using the Benjamini & Hochberg (1995) method: method = "BH"
# # and filter for significantly differentially expressed genes (alpha = 0.05)
# Wilcox.DEGs.adj <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "wilcox.test",
# stage.names = c("S1","S2","S3"),
# p.adjust.method = "BH")
#
# # look at the results
# Wilcox.DEGs.adj
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # install edgeR
# source("http://bioconductor.org/biocLite.R")
# biocLite("edgeR")
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by the Double Tail Method
# DoubleTail.DEGs <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "doubletail",
# lib.size = 1000,
# stage.names = c("S1","S2","S3"))
#
# # look at the results
# DoubleTail.DEGs
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by the Double Tail Method
# # and furthermore, adjust p-values for multiple comparison
# # using the Benjamini & Hochberg (1995) method: method = "BH"
# # and filter for significantly differentially expressed genes (alpha = 0.05)
# DoubleTail.DEGs.adj <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "doubletail",
# lib.size = 1000,
# stage.names = c("S1","S2","S3"),
# p.adjust.method = "BH")
#
# # look at the results
# DoubleTail.DEGs.adj
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by the Small-P Method
# SmallP.DEGs <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "smallp",
# lib.size = 1000,
# stage.names = c("S1","S2","S3"))
#
# # look at the results
# SmallP.DEGs
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by the Small-P Method
# # and furthermore, adjust p-values for multiple comparison
# # using the Benjamini & Hochberg (1995) method: method = "BH"
# # and filter for significantly differentially expressed genes (alpha = 0.05)
# SmallP.DEGs.adj <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "smallp",
# lib.size = 1000,
# stage.names = c("S1","S2","S3"),
# p.adjust.method = "BH")
#
# # look at the results
# SmallP.DEGs.adj
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by the Deviance
# Deviance.DEGs <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "deviance",
# lib.size = 1000,
# stage.names = c("S1","S2","S3"))
#
# # look at the results
# Deviance.DEGs
## ---- eval=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data("PhyloExpressionSetExample")
#
# # Detection of DEGs using the p-value returned by the Deviance Method
# # and furthermore, adjust p-values for multiple comparison
# # using the Benjamini & Hochberg (1995) method: method = "BH"
# # and filter for significantly differentially expressed genes (alpha = 0.05)
# Deviance.DEGs.adj <- DiffGenes(ExpressionSet = PhyloExpressionSetExample[1:5,1:8],
# nrep = 2,
# method = "deviance",
# lib.size = 1000,
# stage.names = c("S1","S2","S3"),
# p.adjust.method = "BH")
#
# # look at the results
# Deviance.DEGs.adj
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data(PhyloExpressionSetExample)
#
# # visualize the sd() between replicates
# PlotReplicateQuality(ExpressionSet = PhyloExpressionSetExample[ , 1:8],
# nrep = 2,
# legend.pos = "topright",
# ylim = c(0,0.2),
# lwd = 6)
#
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# data(PhyloExpressionSetExample)
#
# # visualize the mad() between replicates
# PlotReplicateQuality(ExpressionSet = PhyloExpressionSetExample[ , 1:8],
# nrep = 2,
# FUN = mad,
# legend.pos = "topright",
# ylim = c(0,0.015),
# lwd = 6)
#
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# library(myTAI)
#
# # load example data
# data(PhyloExpressionSetExample)
#
# # generate an example PhyloExpressionSet with replicates
# ExampleReplicateExpressionSet <- PhyloExpressionSetExample[ ,1:8]
#
# # rename stages
# names(ExampleReplicateExpressionSet)[3:8] <- c("Stage_1_Repl_1","Stage_1_Repl_2",
# "Stage_2_Repl_1","Stage_2_Repl_2",
# "Stage_3_Repl_1","Stage_3_Repl_2")
# # have a look at the example dataset
# head(ExampleReplicateExpressionSet, 5)
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # visualize the TAI profile over 3 stages of development
# # and 2 replicates per stage
# PlotPattern(ExpressionSet = ExampleReplicateExpressionSet,
# type = "l",
# lwd = 6)
#
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # combine the expression levels of the 2 replicates (const) per stage
# # using geom.mean as window function and rename new stages to: "S1","S2","S3"
# CollapssedPhyloExpressionSet <- CollapseReplicates(
# ExpressionSet = ExampleReplicateExpressionSet,
# nrep = 2,
# FUN = geom.mean,
# stage.names = c("S1","S2","S3"))
#
# # have a look at the collapsed PhyloExpressionSet
# head(CollapssedPhyloExpressionSet)
#
## ----eval = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # check number of genes in PhyloExpressionSetExample
# nrow(PhyloExpressionSetExample)
# #> [1] 25260
#
# # remove genes that have an expression level below 8000
# # in at least one developmental stage
# FilterConst <- Expressed(ExpressionSet = PhyloExpressionSetExample,
# cut.off = 8000,
# comparison = "below",
# method = "const")
#
# nrow(FilterConst) # check number of retained genes
# #> [1] 449
## ----eval = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # again: check number of genes in PhyloExpressionSetExample
# nrow(PhyloExpressionSetExample)
# #> [1] 25260
#
# # remove genes that have an expression level above 12000
# # in at least one developmental stage (outlier removal)
# FilterConst.above <- Expressed(ExpressionSet = PhyloExpressionSetExample,
# cut.off = 12000,
# comparison = "above",
# method = "const")
#
# nrow(FilterConst.above) # check number of retained genes
# #> [1] 23547
## ----eval = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # again: check number of genes in PhyloExpressionSetExample
# nrow(PhyloExpressionSetExample)
# #> [1] 25260
#
# # remove genes that have an expression level below 8000 AND above 12000
# # in at least one developmental stage (non-expressed genes AND outlier removal)
# FilterConst.both <- Expressed(ExpressionSet = PhyloExpressionSetExample,
# cut.off = c(8000,12000),
# comparison = "both",
# method = "const")
#
# nrow(FilterConst.both) # check number of retained genes
# #> [1] 2
## ----eval = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # remove genes that have an expression level below 8000
# # in at least 5 developmental stages (in this case: n = 2 stages fulfilling the criteria)
# FilterNSet <- Expressed(ExpressionSet = PhyloExpressionSetExample,
# cut.off = 8000,
# method = "n-set",
# comparison = "below",
# n = 2)
#
# nrow(FilterMinSet) # check number of retained genes
# #> [1] 20
## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# # load a standard PhyloExpressionSet
# data(PhyloExpressionSetExample)
#
# # we assume that the PhyloExpressionSetExample
# # consists of 3 developmental stages
# # and 2 replicates for stage 1, 3 replicates for stage 2,
# # and 2 replicates for stage 3
# # FOR REAL ANALYSES PLEASE USE: permutations = 1000 or 10000
# # BUT NOTE THAT THIS TAKES MUCH MORE COMPUTATION TIME
# p.vector <- CombinatorialSignificance(ExpressionSet = PhyloExpressionSetExample,
# replicates = c(2,3,2),
# TestStatistic = "FlatLineTest",
# permutations = 100,
# parallel = FALSE)
## ----eval = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# any(p.vector > 0.05)
# #> FALSE
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