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1-24-061 limma Data to import: http://eh3.uc.edu/teaching/cfg/2006/data/07-21-03_MO-S-06-23-03N17-72SV2-3-vs-CSV2-5-B.gpr http://eh3.uc.edu/teaching/cfg/2006/data/07-21-03_MO-S-06-23-03N73-CSV3-3-vs-72SV3-5-A.gpr http://eh3.uc.edu/teaching/cfg/2006/data/07-18-03_MO-S-06-23-03N98-CSV1-3-vs-72SV1-5-B.gpr File descriptions: http://eh3.uc.edu/teaching/cfg/2006/data/NTargets.txt http://eh3.uc.edu/teaching/cfg/2006/data/NTargets.txt Spot descriptions: http://eh3.uc.edu/teaching/cfg/2006/data/SpotTypes.txt http://eh3.uc.edu/teaching/cfg/2006/data/SpotTypes.txt Importing data: source("http://eh3.uc.edu/teaching/cfg/2006/R/LimmaDataImportAndNormalization.R")http://eh3.uc.edu/teaching/cfg/2006/R/LimmaDataImportAndNormalization.R
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1-24-062 limma > library(limma) > data.directory<-"http://eh3.uc.edu/teaching/cfg/2006/data/" > targets<-readTargets("http://eh3.uc.edu/teaching/cfg/2006/data/NTargets.txt") > targets array experiment cy3 1 17.0 07-21-03_MO-S-06-23-03N17-72SV2-3-vs-CSV2-5-B.gpr Nic-WT72hr_2 2 73.0 07-21-03_MO-S-06-23-03N73-CSV3-3-vs-72SV3-5-A.gpr Ctl-WT00hr_3 3 98.1 07-18-03_MO-S-06-23-03N98-CSV1-3-vs-72SV1-5-B.gpr Ctl-WT00hr_1 cy5 date 1 Ctl-WT00hr_2 7/21/2003 2 Nic-WT72hr_3 7/21/2003 3 Nic-WT72hr_1 7/18/2003
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1-24-063 limma > spottypes<- readSpotTypes("http://eh3.uc.edu/teaching/cfg/2006/data/SpotTypes.txt") > spottypes SpotType ID Name Color 1 cDNA * * black 2 Blank *Blank* * blue 3 Control * *control* red 4 Empty *Empty* * blue 5 empty *empty* * blue >
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1-24-064 RGList class > LimmaDataNickel<-read.maimages(files=targets$experiment,source="genepix", path = data.directory, names=paste("Cy5_",targets$cy5,"_VS_Cy3_",targets$cy3,sep=""), + columns=list(Gf = "F532 Median",Gb ="B532 Median", Rf = "F635 Median", Rb = "B635 Median"), + annotation=c("Name","ID","Block","Row","Column"),wt.fun=wtflags(0)) Read http://eh3.uc.edu/teaching/cfg/2006/data//07-21-03_MO-S-06-23-03N17-72SV2-3-vs-CSV2-5-B.gpr Read http://eh3.uc.edu/teaching/cfg/2006/data//07-21-03_MO-S-06-23-03N73-CSV3-3-vs-72SV3-5-A.gpr Read http://eh3.uc.edu/teaching/cfg/2006/data//07-18-03_MO-S-06-23-03N98-CSV1-3-vs-72SV1-5-B.gpr > attributes(LimmaDataNickel) $names [1] "R" "G" "Rb" "Gb" "weights" "targets" "genes" $class [1] "RGList" attr(,"package") [1] "limma"
![RGList class > LimmaDataNickel<-read.maimages(files=targets$experiment,source= genepix , path = data.directory, names=paste( Cy5_ ,targets$cy5, _VS_Cy3_ ,targets$cy3,sep= ), + columns=list(Gf = F532 Median ,Gb = B532 Median , Rf = F635 Median , Rb = B635 Median ), + annotation=c( Name , ID , Block , Row , Column ),wt.fun=wtflags(0)) Read Read Read > attributes(LimmaDataNickel) $names [1] R G Rb Gb weights targets genes $class [1] RGList attr(, package ) [1] limma](http://images.slideplayer.com./42/11461848/slides/slide_4.jpg "RGList class > LimmaDataNickel<-read.maimages(files=targets$experiment,source= genepix , path = data.directory, names=paste( Cy5_ ,targets$cy5, _VS_Cy3_ ,targets$cy3,sep= ), + columns=list(Gf = F532 Median ,Gb = B532 Median , Rf = F635 Median , Rb = B635 Median ), + annotation=c( Name , ID , Block , Row , Column ),wt.fun=wtflags(0)) Read Read Read > attributes(LimmaDataNickel) $names [1] R G Rb Gb weights targets genes $class [1] RGList attr(, package ) [1] limma")
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1-24-065 RGList class > LimmaDataNickel$R[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] 2264 3642 [2,] 303 734 [3,] 140 164 Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 726 [2,] 248 [3,] 120 > LimmaDataNickel$Rb[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] 126 154 [2,] 126 150 [3,] 128 155 Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 129 [2,] 127 [3,] 127 >
![RGList class > LimmaDataNickel$R[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 726 [2,] 248 [3,] 120 > LimmaDataNickel$Rb[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 129 [2,] 127 [3,] 127 >](http://images.slideplayer.com./42/11461848/slides/slide_5.jpg "RGList class > LimmaDataNickel$R[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 726 [2,] 248 [3,] 120 > LimmaDataNickel$Rb[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 129 [2,] 127 [3,] 127 >")
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1-24-066 RGList class > LimmaDataNickel$weights[LimmaDataNickel$genes$ID=="Blank"]<-0 > LimmaDataNickel$weights[LimmaDataNickel$genes$ID=="empty"]<-0 > LimmaDataNickel$printer<-getLayout(LimmaDataNickel$genes) > attributes(LimmaDataNickel) $names [1] "R" "G" "Rb" "Gb" "weights" "targets" "genes" "printer" $class [1] "RGList" attr(,"package") [1] "limma"
![RGList class > LimmaDataNickel$weights[LimmaDataNickel$genes$ID== Blank ]<-0 > LimmaDataNickel$weights[LimmaDataNickel$genes$ID== empty ]<-0 > LimmaDataNickel$printer<-getLayout(LimmaDataNickel$genes) > attributes(LimmaDataNickel) $names [1] R G Rb Gb weights targets genes printer $class [1] RGList attr(, package ) [1] limma](http://images.slideplayer.com./42/11461848/slides/slide_6.jpg "RGList class > LimmaDataNickel$weights[LimmaDataNickel$genes$ID== Blank ]<-0 > LimmaDataNickel$weights[LimmaDataNickel$genes$ID== empty ]<-0 > LimmaDataNickel$printer<-getLayout(LimmaDataNickel$genes) > attributes(LimmaDataNickel) $names [1] R G Rb Gb weights targets genes printer $class [1] RGList attr(, package ) [1] limma")
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1-24-067 RGList class > LimmaDataNickel$genes[1,] Name ID Block Row Column 1 D49382 7708 1 1 1 > LimmaDataNickel$genes$Status<-controlStatus(spottypes,LimmaDataNickel) Matching patterns for: ID Name Found 14112 cDNA Found 288 Blank Found 0 Control Found 0 Empty Found 381 empty Setting attributes: values Color > > LimmaDataNickel$genes[1,] Name ID Block Row Column Status 1 D49382 7708 1 1 1 cDNA
![RGList class > LimmaDataNickel$genes[1,] Name ID Block Row Column 1 D > LimmaDataNickel$genes$Status<-controlStatus(spottypes,LimmaDataNickel) Matching patterns for: ID Name Found cDNA Found 288 Blank Found 0 Control Found 0 Empty Found 381 empty Setting attributes: values Color > > LimmaDataNickel$genes[1,] Name ID Block Row Column Status 1 D cDNA](http://images.slideplayer.com./42/11461848/slides/slide_7.jpg "RGList class > LimmaDataNickel$genes[1,] Name ID Block Row Column 1 D > LimmaDataNickel$genes$Status<-controlStatus(spottypes,LimmaDataNickel) Matching patterns for: ID Name Found cDNA Found 288 Blank Found 0 Control Found 0 Empty Found 381 empty Setting attributes: values Color > > LimmaDataNickel$genes[1,] Name ID Block Row Column Status 1 D cDNA")
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1-24-068 Plotting data in a RGList object par(mfrow=c(3,1)) plotMA(LimmaDataNickel,array=1,xlim=c(-1,16),ylim=c(-3,8),zero.weights=T) plotMA(LimmaDataNickel,array=2,xlim=c(-1,16),ylim=c(-3,8),zero.weights=T) plotMA(LimmaDataNickel,array=3,xlim=c(-1,16),ylim=c(-3,8),zero.weights=T)
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1-24-069 limma PlotMA automatically subtracts the background intensities before plotting data It plots M = log 2 (Cy5)-log 2 (Cy3) on y-axis and A = [log 2 (Cy5)+log 2 (Cy3)]/2 on x-axis Does not plot data with weight zero unless you ask it to If you want to plot all data or the data without subtracting background, you need to do a little work
![limma PlotMA automatically subtracts the background intensities before plotting data It plots M = log 2 (Cy5)-log 2 (Cy3) on y-axis and A = [log 2 (Cy5)+log 2 (Cy3)]/2 on x-axis Does not plot data with weight zero unless you ask it to If you want to plot all data or the data without subtracting background, you need to do a little work](http://images.slideplayer.com./42/11461848/slides/slide_9.jpg "limma PlotMA automatically subtracts the background intensities before plotting data It plots M = log 2 (Cy5)-log 2 (Cy3) on y-axis and A = [log 2 (Cy5)+log 2 (Cy3)]/2 on x-axis Does not plot data with weight zero unless you ask it to If you want to plot all data or the data without subtracting background, you need to do a little work")
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1-24-0610 Background Adjustments > NBLimmaDataNickel<-backgroundCorrect(LimmaDataNickel,method="none") > attributes(NBLimmaDataNickel) $names [1] "R" "G" "weights" "targets" "genes" "printer" $class [1] "RGList" attr(,"package") [1] "limma" Note that background measurements are gone Whole bunch of background adjustment procedures
![Background Adjustments > NBLimmaDataNickel<-backgroundCorrect(LimmaDataNickel,method= none ) > attributes(NBLimmaDataNickel) $names [1] R G weights targets genes printer $class [1] RGList attr(, package ) [1] limma Note that background measurements are gone Whole bunch of background adjustment procedures](http://images.slideplayer.com./42/11461848/slides/slide_10.jpg "Background Adjustments > NBLimmaDataNickel<-backgroundCorrect(LimmaDataNickel,method= none ) > attributes(NBLimmaDataNickel) $names [1] R G weights targets genes printer $class [1] RGList attr(, package ) [1] limma Note that background measurements are gone Whole bunch of background adjustment procedures")
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1-24-0611 Plotting data in a RGList object > par(mfrow=c(3,1)) > plotMA(LimmaDataNickel,array=1,xlim=c(-1,16),ylim=c(-3,8),zero.weights=F) > plotMA(LimmaDataNickel,array=2,xlim=c(-1,16),ylim=c(-3,8),zero.weights=F) > plotMA(LimmaDataNickel,array=3,xlim=c(-1,16),ylim=c(-3,8),zero.weights=F)
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1-24-0612 Plotting data in a RGList object > par(mfrow=c(3,1)) > plotMA(NBLimmaDataNickel,array=1,xlim=c(-1,16),ylim=c(-3,8),zero.weights=F) > plotMA(NBLimmaDataNickel,array=2,xlim=c(-1,16),ylim=c(-3,8),zero.weights=F) > plotMA(NBLimmaDataNickel,array=3,xlim=c(-1,16),ylim=c(-3,8),zero.weights=F)
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1-24-0613 Which one to use? Removing points with the weight zero seems reasonable Subtracting background costs us some data points even if one channel is above background since differences of log-transformed measurements are used only Subtracting background seems to increase the variability, but it is unclear how would this affect results For now proceed without background subtraction, but compare results at the end Exploring other proposed background-adjustment methods also seems like a good idea
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1-24-0614 Within Array Normalization > NNBLimmaDataNickel<-normalizeWithinArrays(NBLimmaDataNickel,method="none") > attributes(NNBLimmaDataNickel) $names [1] "weights" "targets" "genes" "printer" "M" "A" $class [1] "MAList" attr(,"package") [1] "limma" Left with log-ratios and averages - the same things as in the scatter plot produced by plotMA
![Within Array Normalization > NNBLimmaDataNickel<-normalizeWithinArrays(NBLimmaDataNickel,method= none ) > attributes(NNBLimmaDataNickel) $names [1] weights targets genes printer M A $class [1] MAList attr(, package ) [1] limma Left with log-ratios and averages - the same things as in the scatter plot produced by plotMA](http://images.slideplayer.com./42/11461848/slides/slide_14.jpg "Within Array Normalization > NNBLimmaDataNickel<-normalizeWithinArrays(NBLimmaDataNickel,method= none ) > attributes(NNBLimmaDataNickel) $names [1] weights targets genes printer M A $class [1] MAList attr(, package ) [1] limma Left with log-ratios and averages - the same things as in the scatter plot produced by plotMA")
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1-24-0615 Checking Out M and A > NNBLimmaDataNickel$M[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] -0.5418423 1.1911745 [2,] -0.1914542 0.9535822 [3,] 0.9593580 0.2700892 Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] -0.4460010 [2,] -0.5654399 [3,] -0.6244909 > NNBLimmaDataNickel$A[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] 11.415579 11.234928 [2,] 8.338901 9.042845 [3,] 6.649604 7.222507 Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] 9.726826 [2,] 8.236916 [3,] 7.219136 > Homework suggestion: Calculate these directly from R and G
![Checking Out M and A > NNBLimmaDataNickel$M[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] [2,] [3,] > NNBLimmaDataNickel$A[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] [2,] [3,] > Homework suggestion: Calculate these directly from R and G](http://images.slideplayer.com./42/11461848/slides/slide_15.jpg "Checking Out M and A > NNBLimmaDataNickel$M[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] [2,] [3,] > NNBLimmaDataNickel$A[1:3,] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [1,] [2,] [3,] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [1,] [2,] [3,] > Homework suggestion: Calculate these directly from R and G")
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1-24-0616 Two-Sample t-test > NBLimmaDataNickelRG<-log2(cbind(NBLimmaDataNickel$R,NBLimmaDataNickel$G)) > dimnames(NBLimmaDataNickelRG)[[2]] [1] "Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2" [2] "Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3" [3] "Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1" [4] "Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2" [5] "Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3" [6] "Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1" > Nic<-c(2,3,4) > Ctl<-c(1,5,6)
![Two-Sample t-test > NBLimmaDataNickelRG<-log2(cbind(NBLimmaDataNickel$R,NBLimmaDataNickel$G)) > dimnames(NBLimmaDataNickelRG)[[2]] [1] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [2] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [3] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [4] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [5] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [6] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 > Nic<-c(2,3,4) > Ctl<-c(1,5,6)](http://images.slideplayer.com./42/11461848/slides/slide_16.jpg "Two-Sample t-test > NBLimmaDataNickelRG<-log2(cbind(NBLimmaDataNickel$R,NBLimmaDataNickel$G)) > dimnames(NBLimmaDataNickelRG)[[2]] [1] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [2] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [3] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 [4] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [5] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [6] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 > Nic<-c(2,3,4) > Ctl<-c(1,5,6)")
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1-24-0617 Two-Sample t-test > MNic<-apply(NBLimmaDataNickelRG[,Nic],1,mean,na.rm=TRUE) > VNic<-apply(NBLimmaDataNickelRG[,Nic],1,var,na.rm=TRUE) > MCtl<-apply(NBLimmaDataNickelRG[,Ctl],1,mean,na.rm=TRUE) > VCtl<-apply(NBLimmaDataNickelRG[,Ctl],1,var,na.rm=TRUE) > NNic<-apply(!is.na(NBLimmaDataNickelRG[,Nic]),1,sum,na.rm=TRUE) > NCtl<-apply(!is.na(NBLimmaDataNickelRG[,Ctl]),1,sum,na.rm=TRUE) > > VNicCtl<-(((NNic-1)*VNic)+((NCtl-1)*VCtl))/(NCtl+NNic-2) > > DF<-NNic+NCtl-2 > > TStat<-abs(MNic-MCtl)/((VNicCtl*((1/NNic)+(1/NCtl)))^0.5) > TPvalue<-2*pt(TStat,DF,lower.tail=FALSE) > TStat[1] [1] 0.517783 > TPvalue[1] [1] 0.6319288
![Two-Sample t-test > MNic<-apply(NBLimmaDataNickelRG[,Nic],1,mean,na.rm=TRUE) > VNic<-apply(NBLimmaDataNickelRG[,Nic],1,var,na.rm=TRUE) > MCtl<-apply(NBLimmaDataNickelRG[,Ctl],1,mean,na.rm=TRUE) > VCtl<-apply(NBLimmaDataNickelRG[,Ctl],1,var,na.rm=TRUE) > NNic<-apply(!is.na(NBLimmaDataNickelRG[,Nic]),1,sum,na.rm=TRUE) > NCtl<-apply(!is.na(NBLimmaDataNickelRG[,Ctl]),1,sum,na.rm=TRUE) > > VNicCtl<-(((NNic-1)*VNic)+((NCtl-1)*VCtl))/(NCtl+NNic-2) > > DF<-NNic+NCtl-2 > > TStat<-abs(MNic-MCtl)/((VNicCtl*((1/NNic)+(1/NCtl)))^0.5) > TPvalue<-2*pt(TStat,DF,lower.tail=FALSE) > TStat[1] [1] > TPvalue[1] [1]](http://images.slideplayer.com./42/11461848/slides/slide_17.jpg "Two-Sample t-test > MNic<-apply(NBLimmaDataNickelRG[,Nic],1,mean,na.rm=TRUE) > VNic<-apply(NBLimmaDataNickelRG[,Nic],1,var,na.rm=TRUE) > MCtl<-apply(NBLimmaDataNickelRG[,Ctl],1,mean,na.rm=TRUE) > VCtl<-apply(NBLimmaDataNickelRG[,Ctl],1,var,na.rm=TRUE) > NNic<-apply(!is.na(NBLimmaDataNickelRG[,Nic]),1,sum,na.rm=TRUE) > NCtl<-apply(!is.na(NBLimmaDataNickelRG[,Ctl]),1,sum,na.rm=TRUE) > > VNicCtl<-(((NNic-1)*VNic)+((NCtl-1)*VCtl))/(NCtl+NNic-2) > > DF<-NNic+NCtl-2 > > TStat<-abs(MNic-MCtl)/((VNicCtl*((1/NNic)+(1/NCtl)))^0.5) > TPvalue<-2*pt(TStat,DF,lower.tail=FALSE) > TStat[1] [1] > TPvalue[1] [1]")
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1-24-0618 Paired t-test > dimnames(NNBLimmaDataNickel)[[2]] [1] "Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2" [2] "Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3" [3] "Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1" > NNBLimmaDataNickelLR<-NNBLimmaDataNickel$M > NNBLimmaDataNickelLR[,1]<-(-NNBLimmaDataNickelLR[,1]) > MLR<-apply(NNBLimmaDataNickelLR,1,mean,na.rm=TRUE) > VLR<-apply(NNBLimmaDataNickelLR,1,var,na.rm=TRUE) > NLR<-apply(!is.na(NNBLimmaDataNickelLR),1,sum,na.rm=TRUE) > > PTDF<-NLR-1 > > PTStat<-abs(MLR)/((VLR*(1/NLR))^0.5) > PTPvalue<-2*pt(PTStat,PTDF,lower.tail=FALSE) > PTStat[1] [1] 0.9013332 > PTPvalue[1] [1] 0.4625393
![Paired t-test > dimnames(NNBLimmaDataNickel)[[2]] [1] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [2] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [3] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 > NNBLimmaDataNickelLR<-NNBLimmaDataNickel$M > NNBLimmaDataNickelLR[,1]<-(-NNBLimmaDataNickelLR[,1]) > MLR<-apply(NNBLimmaDataNickelLR,1,mean,na.rm=TRUE) > VLR<-apply(NNBLimmaDataNickelLR,1,var,na.rm=TRUE) > NLR<-apply(!is.na(NNBLimmaDataNickelLR),1,sum,na.rm=TRUE) > > PTDF<-NLR-1 > > PTStat<-abs(MLR)/((VLR*(1/NLR))^0.5) > PTPvalue<-2*pt(PTStat,PTDF,lower.tail=FALSE) > PTStat[1] [1] > PTPvalue[1] [1]](http://images.slideplayer.com./42/11461848/slides/slide_18.jpg "Paired t-test > dimnames(NNBLimmaDataNickel)[[2]] [1] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [2] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [3] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 > NNBLimmaDataNickelLR<-NNBLimmaDataNickel$M > NNBLimmaDataNickelLR[,1]<-(-NNBLimmaDataNickelLR[,1]) > MLR<-apply(NNBLimmaDataNickelLR,1,mean,na.rm=TRUE) > VLR<-apply(NNBLimmaDataNickelLR,1,var,na.rm=TRUE) > NLR<-apply(!is.na(NNBLimmaDataNickelLR),1,sum,na.rm=TRUE) > > PTDF<-NLR-1 > > PTStat<-abs(MLR)/((VLR*(1/NLR))^0.5) > PTPvalue<-2*pt(PTStat,PTDF,lower.tail=FALSE) > PTStat[1] [1] > PTPvalue[1] [1]")
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1-24-0619 Two-sample vs Paired t-test > par(mfrow=c(2,2)) > plot(MNic-MCtl,MLR) > plot(VNicCtl,VLR) > plot(TStat,PTStat) > plot(-log10(TPvalue),-log10(PTPvalue),xlim=c(0,4),ylim=c(0,4))
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1-24-0620 Limma Analysis > dimnames(NNBLimmaDataNickel)[[2]] [1] "Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2" [2] "Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3" [3] "Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1" > design<-c(-1,1,1) > > LimmaFit<-lmFit(NNBLimmaDataNickel,design) > attributes(LimmaFit) $names [1] "coefficients" "stdev.unscaled" "sigma" "df.residual" [5] "cov.coefficients" "pivot" "method" "design" [9] "genes" "Amean" $class [1] "MArrayLM" attr(,"package") [1] "limma"
![Limma Analysis > dimnames(NNBLimmaDataNickel)[[2]] [1] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [2] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [3] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 > design<-c(-1,1,1) > > LimmaFit<-lmFit(NNBLimmaDataNickel,design) > attributes(LimmaFit) $names [1] coefficients stdev.unscaled sigma df.residual [5] cov.coefficients pivot method design [9] genes Amean $class [1] MArrayLM attr(, package ) [1] limma](http://images.slideplayer.com./42/11461848/slides/slide_20.jpg "Limma Analysis > dimnames(NNBLimmaDataNickel)[[2]] [1] Cy5_Ctl-WT00hr_2_VS_Cy3_Nic-WT72hr_2 [2] Cy5_Nic-WT72hr_3_VS_Cy3_Ctl-WT00hr_3 [3] Cy5_Nic-WT72hr_1_VS_Cy3_Ctl-WT00hr_1 > design<-c(-1,1,1) > > LimmaFit<-lmFit(NNBLimmaDataNickel,design) > attributes(LimmaFit) $names [1] coefficients stdev.unscaled sigma df.residual [5] cov.coefficients pivot method design [9] genes Amean $class [1] MArrayLM attr(, package ) [1] limma")
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1-24-0621 Limma Analysis > par(mfrow=c(1,1)) > plot(LimmaFit$coefficients,MLR)
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1-24-0622 Limma Analysis > complete.data<-apply(NNBLimmaDataNickel$weights,1,sum)==3 > sum(complete.data) [1] 9621 > par(mfrow=c(1,1)) > plot(LimmaFit$coefficients[complete.data],MLR[complete.data])
![Limma Analysis > complete.data<-apply(NNBLimmaDataNickel$weights,1,sum)==3 > sum(complete.data) [1] 9621 > par(mfrow=c(1,1)) > plot(LimmaFit$coefficients[complete.data],MLR[complete.data])](http://images.slideplayer.com./42/11461848/slides/slide_22.jpg "Limma Analysis > complete.data<-apply(NNBLimmaDataNickel$weights,1,sum)==3 > sum(complete.data) [1] 9621 > par(mfrow=c(1,1)) > plot(LimmaFit$coefficients[complete.data],MLR[complete.data])")
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1-24-0623 Limma Analysis > LimmaFitTstat<- abs(LimmaFit$coefficients/(LimmaFit$sigma*LimmaFit$stdev.unscaled)) > LimmaFitPvalue<-2*pt(LimmaFitTstat,LimmaFit$df.residual,lower.tail=FALSE) > > par(mfrow=c(1,2)) > plot(LimmaFitTstat[complete.data],PTStat[complete.data]) > plot(-log10(LimmaFitPvalue[complete.data]),-log10(PTPvalue[complete.data]))
![Limma Analysis > LimmaFitTstat<- abs(LimmaFit$coefficients/(LimmaFit$sigma*LimmaFit$stdev.unscaled)) > LimmaFitPvalue<-2*pt(LimmaFitTstat,LimmaFit$df.residual,lower.tail=FALSE) > > par(mfrow=c(1,2)) > plot(LimmaFitTstat[complete.data],PTStat[complete.data]) > plot(-log10(LimmaFitPvalue[complete.data]),-log10(PTPvalue[complete.data]))](http://images.slideplayer.com./42/11461848/slides/slide_23.jpg "Limma Analysis > LimmaFitTstat<- abs(LimmaFit$coefficients/(LimmaFit$sigma*LimmaFit$stdev.unscaled)) > LimmaFitPvalue<-2*pt(LimmaFitTstat,LimmaFit$df.residual,lower.tail=FALSE) > > par(mfrow=c(1,2)) > plot(LimmaFitTstat[complete.data],PTStat[complete.data]) > plot(-log10(LimmaFitPvalue[complete.data]),-log10(PTPvalue[complete.data]))")
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1-24-0624 Facilitates easy data import and normalization Keeps track of "bad" spots To run the basic t-test, it takes a bit of additional work If we were to use the empirical Bayes statistics as implemented in limma, it would be even easier Empirical Bayes is generally BETTER than simple t-test Will talk about this type of analysis next week Limma also allows fitting models with multiple factors which we will also talk about next week limma so far
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