### R code from vignette source 'vignette_EasyqpcR.Rnw' ################################################### ### code chunk number 1: first ################################################### library(EasyqpcR) data(Efficiency_calculation) slope(data=Efficiency_calculation, q=c(1000, 100 ,10, 1, 0.1), r=3, na.rm=TRUE) ################################################### ### code chunk number 2: step1 ################################################### efficiency <- slope(data=Efficiency_calculation, q=c(1000, 100 ,10, 1, 0.1), r=3, na.rm=TRUE) ################################################### ### code chunk number 3: step2 ################################################### data(qPCR_run1,qPCR_run2,qPCR_run3) str(c(qPCR_run1,qPCR_run2,qPCR_run3)) ################################################### ### code chunk number 4: step3 ################################################### ## Isolate the calibrator NRQ values of the first biological replicate aa <- nrmData(data=qPCR_run1 , r=3, E=c(2, 2, 2, 2), Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, nbRef=2, Refposcol=1:2, nCTL=2, CF=c(1, 1, 1, 1), CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE)[[3]] ## Isolate the calibrator NRQ values of the first biological replicate bb <- nrmData(data=qPCR_run2 , r=3, E=c(2, 2, 2, 2), Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, nbRef=2, Refposcol=1:2, nCTL=2, CF=c(1, 1, 1, 1), CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE)[[3]] ## Isolate the calibrator NRQ values of the first biological replicate cc <- nrmData(data=qPCR_run3 , r=3, E=c(2, 2, 2, 2), Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, nbRef=2, Refposcol=1:2, nCTL=2, CF=c(1, 1, 1, 1), CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE)[[3]] ################################################### ### code chunk number 5: step4 ################################################### ## Calibration factor calculation e <- calData(aa) f <- calData(bb) g <- calData(cc) ################################################### ### code chunk number 6: step5 (eval = FALSE) ################################################### ## ## nrmData(data=qPCR_run1 , r=3, E=c(2, 2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, ## nbRef=2, Refposcol=1:2, nCTL=2, ## CF=e, CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE) ## ## nrmData(data=qPCR_run2 , r=3, E=c(2, 2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, ## nbRef=2, Refposcol=1:2, nCTL=2, ## CF=f, CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE) ## ## nrmData(data=qPCR_run3 , r=3, E=c(2, 2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, ## nbRef=2, Refposcol=1:2, nCTL=2, ## CF=g, CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE) ## ################################################### ### code chunk number 7: step6 ################################################### ## Isolate the NRQs scaled to control of the first biological replicate a1 <- nrmData(data=qPCR_run1 , r=3, E=c(2, 2, 2, 2), Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, nbRef=2, Refposcol=1:2, nCTL=2, CF=e, CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE)[1] ## Isolate the NRQs scaled to control of the second biological replicate b1 <- nrmData(data=qPCR_run2 , r=3, E=c(2, 2, 2, 2), Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, nbRef=2, Refposcol=1:2, nCTL=2, CF=f, CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE)[1] ## Isolate the NRQs scaled to control of the third biological replicate c1 <- nrmData(data=qPCR_run3 , r=3, E=c(2, 2, 2, 2), Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, nbRef=2, Refposcol=1:2, nCTL=2, CF=g, CalPos=5, trace=FALSE, geo=TRUE, na.rm=TRUE)[1] ## Data frame transformation a2 <- as.data.frame(a1) b2 <- as.data.frame(b1) c2 <- as.data.frame(c1) ## Aggregation of the three biological replicates d2 <- rbind(a2, b2, c2) ################################################### ### code chunk number 8: step7 ################################################### totData(data=d2, r=3, geo=TRUE, logarithm=TRUE, base=2, transformation=TRUE, nSpl=5, linear=TRUE, na.rm=TRUE) ################################################### ### code chunk number 9: step8 ################################################### file <- system.file("extdata", "qPCR_run1.csv", package="EasyqpcR") qPCR_run1 <- read.table(file, header=TRUE, sep="", dec=".") qPCR_run1 ################################################### ### code chunk number 10: step9 ################################################### badCt(data=qPCR_run1, r=3, threshold=0.5, na.rm=TRUE) ################################################### ### code chunk number 11: step10 ################################################### badCt(data=qPCR_run1, r=3, threshold=0.2, na.rm=TRUE) ################################################### ### code chunk number 12: step11 ################################################### filebis <- system.file("extdata", "Gene_maximisation.csv", package="EasyqpcR") Gene_maximisation <- read.table(filebis, header=TRUE, sep=";", dec=",") ################################################### ### code chunk number 13: step12 ################################################### badCt(data=Gene_maximisation, r=3, threshold=0.5, na.rm=FALSE)[1] ################################################### ### code chunk number 14: step13 (eval = FALSE) ################################################### ## ## fileter <- system.file("extdata", "Gene_maximisation_cor.csv", ## package="EasyqpcR") ## ## Gene_maximisation_cor <- read.table(fileter, header=TRUE, sep=";", dec=",") ## ## Gene_maximisation_cor1 <- Gene_maximisation_cor[-c(106:108, 118:120, 130:132, ## 142:144, 154:156, 166:168, 178:180, 190:192),] ## ## rownames(Gene_maximisation_cor1) <- c(1:168) ## ################################################### ### code chunk number 15: step14 (eval = FALSE) ################################################### ## ## calr1 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, ## nCTL=16, CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][1:3,] ## ## calr2 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][4:6,] ## ## calr3 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][7:9,] ## ## calr4 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, ## nCTL=16, CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][10:12,] ## ## calr5 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][13:15,] ## ## calr6 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][16:18,] ## ## calr7 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, ## nCTL=16, CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][19:21,] ## ## calr8 <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=c(1, 1, 1), CalPos=c(33:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[3]][22:24,] ## ## ## e <- calData(calr1) ## ## f <- calData(calr2) ## ## g <- calData(calr3) ## ## h <- calData(calr4) ## ## i <- calData(calr5) ## ## j <- calData(calr6) ## ## k <- calData(calr7) ## ## l <- calData(calr8) ## ## ################################################### ### code chunk number 16: step15 (eval = FALSE) ################################################### ## ## m <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=e, CalPos=c(33:35), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(1:4,33:35),] ## ## ## n <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=f, CalPos=c(36:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(5:8,36:38),] ## ## o <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=g, CalPos=c(36:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(9:12,39:41),] ## ## p <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=h, CalPos=c(33:35), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(13:16,42:44),] ## ## ## q <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=i, CalPos=c(36:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(17:20,45:47),] ## ## r <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=j, CalPos=c(36:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(21:24,48:50),] ## ## s <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=k, CalPos=c(33:35), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(25:28,51:53),] ## ## ## t <- nrmData(data = Gene_maximisation_cor1, r=3, E=c(2, 2, 2), ## Eerror=c(0.02, 0.02, 0.02), nSpl=56, nbRef=2, Refposcol=1:2, nCTL=16, ## CF=l, CalPos=c(36:56), trace = FALSE, geo = TRUE, ## na.rm = TRUE)[[2]][c(29:32,54:56),] ## ## ## Aggregation of all the CNRQs ## ## u <- rbind(m, n, o, p, q, r, s, t) ## ################################################### ### code chunk number 17: step16 (eval = FALSE) ################################################### ## ## ctlgroup <- u[c(1:4,8:11,15:18,22:25),] ## ## ctlgeom <- colProds(ctlgroup)^(1/dim(ctlgroup)[1]) ## ctlgeom1 <- (as.data.frame(ctlgeom)[rep(1:(ncol(u)), each = nrow(u)), ]) ## ctlgeom2 <- as.data.frame(matrix(ctlgeom1, ncol = ncol(u), byrow = FALSE)) ## ## CNRQs_scaled_to_group <- u/ctlgeom2 ## ##