## ------------------------------------------------------------------------ library('Anaquin') data("RnaQuinIsoformMixture") head(RnaQuinIsoformMixture) ## ------------------------------------------------------------------------ set.seed(1234) sim1 <- 1.0 + 1.2*log2(RnaQuinIsoformMixture$MixA) + rnorm(nrow(RnaQuinIsoformMixture),0,1) sim2 <- c(1.0 + rnorm(100,1,3), 1.0 + 1.2*log2(tail(RnaQuinIsoformMixture,64)$MixA) + rnorm(64,0,1)) ## ---- message=FALSE, results='hide', fig.align='center'------------------ names <- row.names(RnaQuinIsoformMixture) input <- log2(RnaQuinIsoformMixture$MixA) title <- 'Isoform expression (Good)' xlab <- 'Input concentration (log2)' ylab <- 'Measured FPKM (log2)' plotLinear(names, input, sim1, title=title, xlab=xlab, ylab=ylab) ## ---- message=FALSE, results='hide', fig.align='center'------------------ names <- row.names(RnaQuinIsoformMixture) input <- log2(RnaQuinIsoformMixture$MixA) title <- 'Isoform expression (Bad)' xlab <- 'Input concentration (log2)' ylab <- 'Measured FPKM (log2)' plotLinear(names, input, sim2, 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 x <- log2(UserGuideData_5.4.5.1$Input) # Measured sensitivity y <- UserGuideData_5.4.5.1$Sn plotLogistic(names, x, y, title=title, xlab=xlab, ylab=ylab, showLOA=TRUE) ## ------------------------------------------------------------------------ data(UserGuideData_5.4.6.3) head(UserGuideData_5.4.6.3) ## ---- message=FALSE, results='hide', 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 x <- log2(UserGuideData_5.4.6.3$Input) # Measured FPKM y <- log2(UserGuideData_5.4.6.3$Observed1) plotLinear(names, x, y, title=title, xlab=xlab, ylab=ylab, showLOQ=TRUE) ## ---- message=FALSE, results='hide', 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 x <- log2(UserGuideData_5.4.6.3$Input) # Measured FPKM y <- log2(UserGuideData_5.4.6.3[,2:4]) plotLinear(names, x, y, title=title, xlab=xlab, ylab=ylab, showLOQ=TRUE) ## ------------------------------------------------------------------------ data(UserGuideData_5.6.3) head(UserGuideData_5.6.3) ## ---- results='hide', results='hide', fig.align='center'----------------- title <- 'Gene Fold Change' xlab <- 'Expected fold change (log2)' ylab <- 'Measured fold change (log2)' # Sequin names names <- row.names(UserGuideData_5.6.3) # Expected log-fold x <- UserGuideData_5.6.3$ExpLFC # Measured log-fold y <- UserGuideData_5.6.3$ObsLFC plotLinear(names, x, y, title=title, xlab=xlab, ylab=ylab, showAxis=TRUE, showLOQ=FALSE) ## ---- results='hide', fig.align='center'--------------------------------- title <- 'ROC Plot' # Sequin names seqs <- row.names(UserGuideData_5.6.3) # Expected ratio ratio <- UserGuideData_5.6.3$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 plotROC(seqs, score, ratio, label, title=title, refGroup=0) ## ---- fig.align='center', results='hide', warning=FALSE------------------ xlab <- 'Average Counts' ylab <- 'P-value' title <- 'LOD Curves' # Measured mean mean <- UserGuideData_5.6.3$Mean # Expected log-fold ratio <- UserGuideData_5.6.3$ExpLFC # P-value pval <- UserGuideData_5.6.3$Pval qval <- UserGuideData_5.6.3$Qval plotLOD(mean, pval, abs(ratio), qval=qval, xlab=xlab, ylab=ylab, title=title, FDR=0.05)