#Load in required packages for functions below
require(qpcR)
## Loading required package: qpcR
## Loading required package: MASS
## Loading required package: minpack.lm
## Loading required package: rgl
## Loading required package: robustbase
## Loading required package: Matrix
require(plyr)
## Loading required package: plyr
require(ggplot2)
## Loading required package: ggplot2
require(splitstackshape)
## Loading required package: splitstackshape
## Loading required package: data.table
#Read in raw fluorescence data from 1st Actin replicate
rep1<-read.csv("CARM3rawfluoro.csv", header = T)
#Remove blank first column entitled "X"
rep1$X<-NULL
#Rename columns so that qpcR package and appropriately handle the data
rep1<-rename(rep1, c("Cycle" = "Cycles", "A1" = "H_C_1", "A2" = "N_C_1",
"A3"= "S_C_1", "A4"="H_T_1", "A5"="N_T_1","A6"="S_T_1",
"A7"="NT_C_1","B1" = "H_C_2", "B2" = "N_C_2","B3"= "S_C_2",
"B4"="H_T_2", "B5"="N_T_2", "B6"="S_T_2","B7"="NT_C_2",
"C1" = "H_C_3", "C2" = "N_C_3","C3"= "S_C_3","C4"="H_T_3",
"C5"="N_T_3", "C6"="S_T_3", "C7"="NT_C_3","D1" = "H_C_4",
"D2" = "N_C_4","D3"= "S_C_4", "D4"="H_T_4", "D5"="N_T_4",
"D6"="S_T_4", "D7"="NT_C_4","E1" = "H_C_5", "E2" = "N_C_5",
"E3"= "S_C_5", "E4"="H_T_5", "E5"="N_T_5", "E6"="S_T_5",
"F1" = "H_C_6", "F2" = "N_C_6","F3"= "S_C_6", "F4"="H_T_6",
"F5"="N_T_6", "F6"="S_T_6","G1" = "H_C_7", "G2" = "N_C_7",
"G3"= "S_C_7", "G4"="H_T_7", "G5"="N_T_7", "G6"="S_T_7",
"H1" = "H_C_8", "H2" = "N_C_8","H3"= "S_C_8", "H4"="H_T_8",
"H5"="N_T_8", "H6"="S_T_8"))
#Run data through pcrbatch in qpcR package which analyzes fluorescence and produces efficiency and cycle threshold values
rep1ct<-pcrbatch(rep1, fluo=NULL)
## Making model for H_C_1 (l4)
## => Fitting passed...
##
## Making model for N_C_1 (l4)
## => Fitting passed...
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## Making model for S_C_1 (l4)
## => Fitting passed...
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## Making model for H_T_1 (l4)
## => Fitting passed...
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## Making model for N_T_1 (l4)
## => Fitting passed...
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## Making model for S_T_1 (l4)
## => Fitting passed...
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## Making model for NT_C_1 (l4)
## => Fitting passed...
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## Making model for H_C_2 (l4)
## => Fitting passed...
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## Making model for N_C_2 (l4)
## => Fitting passed...
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## Making model for S_C_2 (l4)
## => Fitting passed...
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## Making model for H_T_2 (l4)
## => Fitting passed...
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## Making model for N_T_2 (l4)
## => Fitting passed...
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## Making model for S_T_2 (l4)
## => Fitting passed...
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## Making model for NT_C_2 (l4)
## => Fitting passed...
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## Making model for H_C_3 (l4)
## => Fitting passed...
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## Making model for N_C_3 (l4)
## => Fitting passed...
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## Making model for S_C_3 (l4)
## => Fitting passed...
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## Making model for H_T_3 (l4)
## => Fitting passed...
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## Making model for N_T_3 (l4)
## => Fitting passed...
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## Making model for S_T_3 (l4)
## => Fitting passed...
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## Making model for NT_C_3 (l4)
## => Fitting passed...
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## Making model for H_C_4 (l4)
## => Fitting passed...
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## Making model for N_C_4 (l4)
## => Fitting passed...
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## Making model for S_C_4 (l4)
## => Fitting passed...
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## Making model for H_T_4 (l4)
## => Fitting passed...
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## Making model for N_T_4 (l4)
## => Fitting passed...
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## Making model for S_T_4 (l4)
## => Fitting passed...
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## Making model for NT_C_4 (l4)
## => Fitting passed...
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## Making model for H_C_5 (l4)
## => Fitting passed...
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## Making model for N_C_5 (l4)
## => Fitting passed...
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## Making model for S_C_5 (l4)
## => Fitting passed...
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## Making model for H_T_5 (l4)
## => Fitting passed...
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## Making model for N_T_5 (l4)
## => Fitting passed...
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## Making model for S_T_5 (l4)
## => Fitting passed...
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## Making model for H_C_6 (l4)
## => Fitting passed...
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## Making model for N_C_6 (l4)
## => Fitting passed...
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## Making model for S_C_6 (l4)
## => Fitting passed...
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## Making model for H_T_6 (l4)
## => Fitting passed...
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## Making model for N_T_6 (l4)
## => Fitting passed...
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## Making model for S_T_6 (l4)
## => Fitting passed...
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## Making model for H_C_7 (l4)
## => Fitting passed...
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## Making model for N_C_7 (l4)
## => Fitting passed...
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## Making model for S_C_7 (l4)
## => Fitting passed...
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## Making model for H_T_7 (l4)
## => Fitting passed...
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## Making model for N_T_7 (l4)
## => Fitting passed...
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## Making model for S_T_7 (l4)
## => Fitting passed...
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## Making model for H_C_8 (l4)
## => Fitting passed...
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## Making model for N_C_8 (l4)
## => Fitting passed...
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## Making model for S_C_8 (l4)
## => Fitting passed...
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## Making model for H_T_8 (l4)
## => Fitting passed...
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## Making model for N_T_8 (l4)
## => Fitting passed...
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## Making model for S_T_8 (l4)
## => Fitting passed...
##
## Calculating delta of first/second derivative maxima...
## .........10.........20.........30.........40.........50
## ..
## Analyzing H_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
#pcrbatch creates a file with each sample as an individual column in the dataframe. The problem with this is
#that I want to compare all the Ct (labelled sig.cpD2) and generate expression data for them but these values have to be
#in individual columns. To do this I must transpose the data and set the first row as the column names.
rep1res<-setNames(data.frame(t(rep1ct)),rep1ct[,1])
#Now I must remove the first row as it is a duplicate and will cause errors with future analysis
rep1res<-rep1res[-1,]
#since the sample names are now in the first column the column title is row.names. This makes analys hard based on the ability to call the first column.
#to eliminate this issue, I copied the first column into a new column called "Names"
rep1res$Names<-rownames(rep1res)
#Since each sample name contains information such as Population, Treatment, and Sample Number I want to separate out these factors
#into new columns so that I can run future analysis based on population, treatment, or both. Also note the "drop = F" this is so the original names column remains.
rep1res2<-cSplit_f(rep1res, splitCols=c("Names"), sep="_", drop = F)
#After splitting the names column into three new columns I need to rename them appropriately.
rep1res2<-rename(rep1res2, c("Names_1"="Pop", "Names_2"="Treat", "Names_3"="Sample"))
#I also create a column with the target gene name. This isn't used in this analysis but will be helpful for future work.
rep1res2$Gene<-rep("CARM", length(rep1res2))
#In transposing the data frame, the column entries became factors which cannot be used for equations.
#to fix this, I set the entries for sig.eff (efficiency) and sig.cpD2 (Ct value) to numeric. Be aware, without the as.character function the factors will be transformed inappropriately.
rep1res2$sig.eff<-as.numeric(as.character(rep1res2$sig.eff))
rep1res2$sig.cpD2<-as.numeric(as.character(rep1res2$sig.cpD2))
#Now I plot the Ct values to see how they align without converting them to expression.
ggplot(rep1res2, aes(x=Names,y=sig.cpD2, fill=Pop))+geom_bar(stat="identity")
#Now I want to get expression information from my data set. qpcR has a way of doing this but its complicated and I'm not comfortable using it.
#Luckily there is an equation I can use to do it. The equation is expression = 1/(1+efficiency)^Ctvalue. I tried multiple ways to get this to work in R
#but it doesn't handle the complicated equation easily.
#To work around this, I created a function in R to run the equation and produce an outcome. x = efficiency argument, y=Ctvalue argument
expr<-function(x,y){
newVar<-(1+x)^y
1/newVar
}
#Now I run the data through the function and produce a useful expression value
rep1res2$expression<-expr(rep1res2$sig.eff, rep1res2$sig.cpD2)
#Graphing the expression values is a good way to examine the data quickly for errors that might have occurred.
ggplot(rep1res2, aes(x=Names,y=expression, fill=Pop))+geom_bar(stat="identity")
#Before I'm able to compare the replicates I need to process the raw fluorescence from the second Actin run.
#To do this I perform all the same steps as the previous replicate.
rep2<-read.csv("CARM4rawfluoro.csv", header = T)
rep2$X<-NULL
rep2<-rename(rep2, c("Cycle" = "Cycles", "A1" = "H_C_1", "A2" = "N_C_1",
"A3"= "S_C_1", "A4"="H_T_1", "A5"="N_T_1","A6"="S_T_1",
"A7"="NT_C_1","B1" = "H_C_2", "B2" = "N_C_2","B3"= "S_C_2",
"B4"="H_T_2", "B5"="N_T_2", "B6"="S_T_2","B7"="NT_C_2",
"C1" = "H_C_3", "C2" = "N_C_3","C3"= "S_C_3","C4"="H_T_3",
"C5"="N_T_3", "C6"="S_T_3", "C7"="NT_C_3","D1" = "H_C_4",
"D2" = "N_C_4","D3"= "S_C_4", "D4"="H_T_4", "D5"="N_T_4",
"D6"="S_T_4", "D7"="NT_C_4","E1" = "H_C_5", "E2" = "N_C_5",
"E3"= "S_C_5", "E4"="H_T_5", "E5"="N_T_5", "E6"="S_T_5",
"F1" = "H_C_6", "F2" = "N_C_6","F3"= "S_C_6", "F4"="H_T_6",
"F5"="N_T_6", "F6"="S_T_6","G1" = "H_C_7", "G2" = "N_C_7",
"G3"= "S_C_7", "G4"="H_T_7", "G5"="N_T_7", "G6"="S_T_7",
"H1" = "H_C_8", "H2" = "N_C_8","H3"= "S_C_8", "H4"="H_T_8",
"H5"="N_T_8", "H6"="S_T_8"))
rep2ct<-pcrbatch(rep2, fluo=NULL)
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)
## Making model for H_C_1 (l4)
## => Fitting passed...
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## Making model for N_C_1 (l4)
## => Fitting passed...
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## Making model for S_C_1 (l4)
## => Fitting passed...
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## Making model for H_T_1 (l4)
## => Fitting passed...
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## Making model for N_T_1 (l4)
## => Fitting passed...
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## Making model for S_T_1 (l4)
## => Fitting passed...
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## Making model for NT_C_1 (l4)
## => Fitting passed...
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## Making model for H_C_2 (l4)
## => Fitting passed...
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## Making model for N_C_2 (l4)
## => Fitting passed...
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## Making model for S_C_2 (l4)
## => Fitting passed...
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## Making model for H_T_2 (l4)
## => Fitting passed...
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## Making model for N_T_2 (l4)
## => Fitting passed...
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## Making model for S_T_2 (l4)
## => Fitting passed...
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## Making model for NT_C_2 (l4)
## => Fitting passed...
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## Making model for H_C_3 (l4)
## => Fitting passed...
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## Making model for N_C_3 (l4)
## => Fitting passed...
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## Making model for S_C_3 (l4)
## => Fitting passed...
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## Making model for H_T_3 (l4)
## => Fitting passed...
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## Making model for N_T_3 (l4)
## => Fitting passed...
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## Making model for S_T_3 (l4)
## => Fitting passed...
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## Making model for NT_C_3 (l4)
## => Fitting passed...
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## Making model for H_C_4 (l4)
## => Fitting passed...
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## Making model for N_C_4 (l4)
## => Fitting passed...
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## Making model for S_C_4 (l4)
## => Fitting passed...
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## Making model for H_T_4 (l4)
## => Fitting passed...
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## Making model for N_T_4 (l4)
## => Fitting passed...
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## Making model for S_T_4 (l4)
## => Fitting passed...
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## Making model for NT_C_4 (l4)
## => Fitting passed...
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## Making model for H_C_5 (l4)
## => Fitting passed...
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## Making model for N_C_5 (l4)
## => Fitting passed...
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## Making model for S_C_5 (l4)
## => Fitting passed...
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## Making model for H_T_5 (l4)
## => Fitting passed...
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## Making model for N_T_5 (l4)
## => Fitting passed...
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## Making model for S_T_5 (l4)
## => Fitting passed...
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## Making model for H_C_6 (l4)
## => Fitting passed...
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## Making model for N_C_6 (l4)
## => Fitting passed...
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## Making model for S_C_6 (l4)
## => Fitting passed...
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## Making model for H_T_6 (l4)
## => Fitting passed...
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## Making model for N_T_6 (l4)
## => Fitting passed...
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## Making model for S_T_6 (l4)
## => Fitting passed...
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## Making model for H_C_7 (l4)
## => Fitting passed...
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## Making model for N_C_7 (l4)
## => Fitting passed...
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## Making model for S_C_7 (l4)
## => Fitting passed...
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## Making model for H_T_7 (l4)
## => Fitting passed...
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## Making model for N_T_7 (l4)
## => Fitting passed...
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## Making model for S_T_7 (l4)
## => Fitting passed...
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## Making model for H_C_8 (l4)
## => Fitting passed...
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## Making model for N_C_8 (l4)
## => Fitting passed...
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## Making model for S_C_8 (l4)
## => Fitting passed...
##
## Making model for H_T_8 (l4)
## => Fitting passed...
##
## Making model for N_T_8 (l4)
## => Fitting passed...
##
## Making model for S_T_8 (l4)
## => Fitting passed...
##
## Calculating delta of first/second derivative maxima...
## .........10.........20.........30.........40.........50
## ..
## Analyzing H_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_1 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_2 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_3 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing NT_C_4 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_5 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_6 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_7 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_C_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_C_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_C_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing H_T_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing N_T_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
##
## Analyzing S_T_8 ...
## Calculating 'eff' and 'ct' from sigmoidal model...
## Using window-of-linearity...
## Fitting exponential model...
## Using linear regression of efficiency (LRE)...
rep2res<-setNames(data.frame(t(rep2ct)),rep2ct[,1])
rep2res<-rep2res[-1,]
rep2res$Names<-rownames(rep2res)
rep2res2<-cSplit_f(rep2res, splitCols=c("Names"), sep="_", drop = F)
rep2res2<-rename(rep2res2, c("Names_1"="Pop", "Names_2"="Treat", "Names_3"="Sample"))
rep2res2$Gene<-rep("CARM", length(rep2res2))
rep2res2$sig.eff<-as.numeric(as.character(rep2res2$sig.eff))
rep2res2$sig.cpD2<-as.numeric(as.character(rep2res2$sig.cpD2))
ggplot(rep2res2, aes(x=Names,y=sig.cpD2, fill=Pop))+geom_bar(stat="identity")
expr<-function(x,y){
newVar<-(1+x)^y
1/newVar
}
rep2res2$expression<-expr(rep2res2$sig.eff, rep2res2$sig.cpD2)
ggplot(rep2res2, aes(x=Names,y=expression, fill=Pop))+geom_bar(stat="identity")
#Now that I have Ct values, efficiencies and expression values for both replicates I can create a table of the differences between reps.
#To do this I create a data frame with a single formula that creates a column of values generated by subtracting the first run from the second.
repcomp<-as.data.frame(rep1res2$sig.cpD2-rep2res2$sig.cpD2)
#Now I need to add some Names for the samples to use with ggplot.Since the names column contains all the relevant information
#I copy only that column and run the split function on it again as well as the rename function.
repcomp$Names<-rep1res2$Names
repcomp<-cSplit_f(repcomp, splitCols=c("Names"), sep="_", drop = F)
#To better address the difference column in ggplot I need to rename it something simple and short.
repcomp<-rename(repcomp, c("rep1res2$sig.cpD2 - rep2res2$sig.cpD2"="rep.diff", "Names_1"="Pop", "Names_2"="Treat", "Names_3"="Sample"))
#Now I just run the data through ggplot to generate a bar graph exploring the differences between the two replicate in terms of Ct values.
ggplot(repcomp, aes(x=Names, y=rep.diff, fill=Pop))+geom_bar(stat="identity")
carm<-as.data.frame(cbind(rep1res2$expression,rep1res2$Names,rep1res2$Pop,rep1res2$Treat,rep2res2$expression))
carm<-rename(carm, c(V1="rep1.expr","V2"="name","V3"="pop","V4"="treat"
,"V5"="rep2.expr"))
carm$rep1.expr<-as.numeric(as.character(carm$rep1.expr))
carm$rep2.expr<-as.numeric(as.character(carm$rep2.expr))
carm$avgexpr<-rowMeans(carm[,c("rep1.expr","rep2.expr")],na.rm=F)
carm<-carm[which(carm$pop!=c("NT")),]
ggplot(carm, aes(x=treat,y=avgexpr, fill=pop))+geom_boxplot()
fit<-aov(avgexpr~pop+treat+pop:treat,data=carm)
fit
## Call:
## aov(formula = avgexpr ~ pop + treat + pop:treat, data = carm)
##
## Terms:
## pop treat pop:treat Residuals
## Sum of Squares 6.704900e-21 1.803367e-20 5.129000e-21 1.215387e-19
## Deg. of Freedom 2 1 2 42
##
## Residual standard error: 5.379385e-11
## Estimated effects may be unbalanced
TukeyHSD(fit)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = avgexpr ~ pop + treat + pop:treat, data = carm)
##
## $pop
## diff lwr upr p adj
## N-H -3.094162e-12 -4.930070e-11 4.311237e-11 0.9855196
## S-H 2.338089e-11 -2.282564e-11 6.958743e-11 0.4428240
## S-N 2.647506e-11 -1.973148e-11 7.268159e-11 0.3541602
##
## $treat
## diff lwr upr p adj
## T-C 3.876604e-11 7.427354e-12 7.010472e-11 0.0165568
##
## $`pop:treat`
## diff lwr upr p adj
## N:C-H:C 2.145712e-11 -5.883689e-11 1.017511e-10 0.9663468
## S:C-H:C 4.102022e-11 -3.927379e-11 1.213142e-10 0.6504725
## H:T-H:C 6.689311e-11 -1.340090e-11 1.471871e-10 0.1512237
## N:T-H:C 3.924767e-11 -4.104635e-11 1.195417e-10 0.6912581
## S:T-H:C 7.263468e-11 -7.659334e-12 1.529287e-10 0.0964350
## S:C-N:C 1.956310e-11 -6.073091e-11 9.985711e-11 0.9774261
## H:T-N:C 4.543599e-11 -3.485802e-11 1.257300e-10 0.5462641
## N:T-N:C 1.779054e-11 -6.250347e-11 9.808455e-11 0.9851806
## S:T-N:C 5.117755e-11 -2.911646e-11 1.314716e-10 0.4147652
## H:T-S:C 2.587289e-11 -5.442112e-11 1.061669e-10 0.9272874
## N:T-S:C -1.772557e-12 -8.206657e-11 7.852145e-11 0.9999998
## S:T-S:C 3.161446e-11 -4.867956e-11 1.119085e-10 0.8459043
## N:T-H:T -2.764545e-11 -1.079395e-10 5.264856e-11 0.9058906
## S:T-H:T 5.741566e-12 -7.455245e-11 8.603558e-11 0.9999342
## S:T-N:T 3.338701e-11 -4.690700e-11 1.136810e-10 0.8140486
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)