## ------------------------------------------------------------------------
library('Anaquin')
data("RnaQuinMixtureA")
head(RnaQuinMixtureA)

## ------------------------------------------------------------------------
set.seed(1234)
sim1 <- 1.0 + 1.2*log2(RnaQuinMixtureA$MixA) + rnorm(nrow(RnaQuinMixtureA),0,1)
sim2 <- c(1.0 + rnorm(100,1,3), 1.0 +
          1.2*log2(tail(RnaQuinMixtureA,64)$MixA) +
          rnorm(64,0,1))

## ---- message=FALSE, results='hide', fig.align='center'------------------
names <- row.names(RnaQuinMixtureA)
input <- log2(RnaQuinMixtureA$MixA)

anaquin <- AnaquinData(analysis='PlotLinear',
                           seqs=names,
                          input=input,
                       measured=sim1)

title <- 'Isoform expression (Good)'
xlab  <- 'Input concentration (log2)'
ylab  <- 'Measured FPKM (log2)'

plotLinear(anaquin, title=title, xlab=xlab, ylab=ylab)

## ---- message=FALSE, results='hide', fig.align='center'------------------
names <- row.names(RnaQuinMixtureA)
input <- log2(RnaQuinMixtureA$MixA)

anaquin <- AnaquinData(analysis='PlotLinear',
                           seqs=names,
                          input=input,
                       measured=sim2)

title <- 'Isoform expression (Bad)'
xlab  <- 'Input concentration (log2)'
ylab  <- 'Measured FPKM (log2)'

plotLinear(anaquin, title=title, xlab=xlab, ylab=ylab)

## ------------------------------------------------------------------------
data(UserGuideData_5.4.5.1)
head(UserGuideData_5.4.5.1)

## ---- message=FALSE, results='hide', fig.align='center'------------------
title <- 'Assembly Plot'
xlab  <- 'Input Concentration (log2)'
ylab  <- 'Sensitivity'

# Sequin names
names <- row.names(UserGuideData_5.4.5.1)

# Input concentration
input <- log2(UserGuideData_5.4.5.1$InputConcent)

# Measured sensitivity
measured <- UserGuideData_5.4.5.1$Sn

anaquin <- AnaquinData(analysis='PlotLogistic',
                           seqs=names,
                          input=input,
                       measured=measured)

plotLogistic(anaquin, title=title, xlab=xlab, ylab=ylab, showLOA=TRUE)

## ------------------------------------------------------------------------
data(UserGuideData_5.4.6.3)
head(UserGuideData_5.4.6.3)

## ---- message=FALSE, fig.align='center'----------------------------------
title <- 'Gene Expression'
xlab  <- 'Input Concentration (log2)'
ylab  <- 'FPKM (log2)'

# Sequin names
names <- row.names(UserGuideData_5.4.6.3)

# Input concentration
input <- log2(UserGuideData_5.4.6.3$InputConcent)

# Measured FPKM
measured <- log2(UserGuideData_5.4.6.3$Observed1)

anaquin <- AnaquinData(analysis='PlotLinear',
                           seqs=names,
                          input=input,
                       measured=measured)

plotLinear(anaquin, title=title, xlab=xlab, ylab=ylab, showLOQ=TRUE)

## ---- message=FALSE, fig.align='center'----------------------------------
title <- 'Gene Expression'
xlab  <- 'Input Concentration (log2)'
ylab  <- 'FPKM (log2)'

# Sequin names
names <- row.names(UserGuideData_5.4.6.3)

# Input concentration
input <- log2(UserGuideData_5.4.6.3$InputConcent)

# Measured FPKM
measured <- log2(UserGuideData_5.4.6.3[,2:4])

anaquin <- AnaquinData(analysis='PlotLinear',
                           seqs=names,
                          input=input,
                       measured=measured)

plotLinear(anaquin, title=title, xlab=xlab, ylab=ylab, showLOQ=TRUE)

## ------------------------------------------------------------------------
data(UserGuideData_5.6.3)
head(UserGuideData_5.6.3)

## ---- fig.align='center'-------------------------------------------------
title <- 'Gene Fold Change'
xlab  <- 'Expected fold change (log2)'
ylab  <- 'Measured fold change (log2)'

# Sequin names
names <- row.names(UserGuideData_5.6.3)

# Expected log-fold
input <- UserGuideData_5.6.3$ExpLFC

# Measured log-fold
measured <- UserGuideData_5.6.3$ObsLFC

anaquin <- AnaquinData(analysis='PlotLinear',
                           seqs=names,
                          input=input,
                       measured=measured)

plotLinear(anaquin, title=title, xlab=xlab, ylab=ylab, showAxis=TRUE,
           showLOQ=FALSE)

## ---- fig.align='center'-------------------------------------------------
title <- 'ROC Plot'

# Sequin names
names <- row.names(UserGuideData_5.6.3)

# Expected ratio
ratio <- UserGuideData_5.6.3$ExpLFC

# How the ROC points are ranked (scoring function)
score <- 1-UserGuideData_5.6.3$Pval

# Classified labels (TP/FP)
label <- UserGuideData_5.6.3$Label

anaquin <- AnaquinData(analysis='PlotROC',
                           seqs=names,
                          ratio=ratio,
                          score=score,
                          label=label)

plotROC(anaquin, title=title, refRats=0)

## ---- fig.align='center', warning=FALSE----------------------------------
xlab  <- 'Average Counts'
ylab  <- 'P-value'
title <- 'LODR Curves'

# Sequin names
names <- row.names(UserGuideData_5.6.3)

# Measured mean
measured <- UserGuideData_5.6.3$Mean

# Expected log-fold
ratio <- UserGuideData_5.6.3$ExpLFC

# P-value
pval <- UserGuideData_5.6.3$Pval

# Q-value
qval <- UserGuideData_5.6.3$Qval

anaquin <- AnaquinData(analysis='PlotLODR',
                           seqs=names,
                       measured=measured,
                          ratio=ratio,
                           pval=pval,
                           qval=qval)

plotLODR(anaquin, xlab=xlab, ylab=ylab, title=title, FDR=0.1)