## ----------------------------------------------------------------------------- 库(Anaquin)数据(“RnaQuinIsoformMixture”)负责人(RnaQuinIsoformMixture ) ## ----------------------------------------------------------------------------- set.seed (1234) sim1 < - 1.0 + 1.2 * log2 (RnaQuinIsoformMixture MixA美元)+ rnorm (nrow (RnaQuinIsoformMixture), 0, 1) sim2 < - c (1.0 + rnorm(100、1、3),1.0 + 1.2 * log2(尾(RnaQuinIsoformMixture, 64) MixA美元)+ rnorm (64 0 1 )) ## ---- 消息= FALSE,结果= '隐藏',图align='center'----------------------- names <- row.names(rnaquinisoformmix) input <- log2(rnaquinisoformmix $MixA) title <- 'Isoform expression (Good)' xlab <- ' input concentration (log2)' ylab <- 'Measured FPKM (log2)' plotLinear(names, input, sim1, title=title, xlab=xlab, ylab=ylab) ## ---- message=FALSE, results='hide',fig.align = '中心 '----------------------- 名称< - row.names (RnaQuinIsoformMixture)输入< - log2 (RnaQuinIsoformMixture MixA美元)标题< -“同种型表达式(坏的管理者)”xlab < -“输入浓度(log2)”ylab < -“测量FPKM (log2) plotLinear(名称、输入、sim2 title =标题,xlab = xlab ylab = ylab ) ## ----------------------------------------------------------------------------- 数据(UserGuideData_5.4.5.1)头(UserGuideData_5.4.5.1 ) ## ---- 消息= FALSE,结果= '隐藏',图align='center'----------------------- title <- 'Assembly Plot' xlab <- 'Input Concentration (log2)' ylab <- 'Sensitivity' # Sequin names names <- row.names(UserGuideData_5.4.5.1) #输入浓度x <- log2(UserGuideData_5.4.5.1$Input) #测量灵敏度y <- UserGuideData_5.4.5.1$Sn plotlogic (names, x, y, title=title, xlab=xlab, ylab=ylab,showLOA = TRUE ) ## ----------------------------------------------------------------------------- 数据(UserGuideData_5.4.6.3)头(UserGuideData_5.4.6.3 ) ## ---- 消息= FALSE,结果= '隐藏',图align='center'----------------------- title <- '基因表达' xlab <- '输入浓度(log2)' ylab <- 'FPKM (log2)' # Sequin names names <- row.names(UserGuideData_5.4.6.3 $Input) #输入浓度x <- log2(UserGuideData_5.4.6.3$Input) #测量的FPKM y <- log2(UserGuideData_5.4.6.3$ observved1) plotLinear(names, x, y, title=title, xlab=xlab, ylab=ylab, showLOQ=TRUE) # ---- message=FALSE, results='hide',图align='center'----------------------- title <- '基因表达' xlab <- '输入浓度(log2)' ylab <- 'FPKM (log2)' # Sequin names names <- row.names(UserGuideData_5.4.6.3) #输入浓度x <- log2(UserGuideData_5.4.6.3$Input) #测量的FPKM y <- log2(UserGuideData_5.4.6.3[,2:4]) plotLinear(names, x, y, title=title, xlab=xlab, ylab=ylab,showLOQ = TRUE ) ## ----------------------------------------------------------------------------- 数据(UserGuideData_5.6.3)头(UserGuideData_5.6.3 ) ## ---- 结果=‘隐藏’,结果=“隐藏”,图align='center'---------------------- title <- 'Gene Fold Change' xlab <- 'Expected Fold Change (log2)' ylab <- 'Measured Fold Change (log2)' # Sequin names names <- row.names(UserGuideData_5.6.3) # Expected log-fold x <- UserGuideData_5.6.3$ExpLFC # Measured log-fold y <- UserGuideData_5.6.3$ObsLFC plotLinear(names, x, y, title=title, xlab=xlab, ylab=ylab, showAxis=TRUE, showLOQ=FALSE) # ---- results='hide',图align='center'-------------------------------------- title <- 'ROC Plot' # Sequin names seqs <- row.names(UserGuideData_5.6.3) #期望比率比率<- UserGuideData_5.6.3$ExpLFC # ROC点如何排名(评分函数)评分<- 1-UserGuideData_5.6.3$Pval #分类标签(TP/FP)标签<- UserGuideData_5.6.3$ label plotROC(seqs, score, ratio, label, title=title, refGroup=0) ## ----图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)