#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("TLR1rawfluoro.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...
##
## Making model for S_C_1 (l4)
## => Fitting passed...
##
## Making model for H_T_1 (l4)
## => Fitting passed...
##
## Making model for N_T_1 (l4)
## => Fitting passed...
##
## Making model for S_T_1 (l4)
## => Fitting passed...
##
## Making model for NT_C_1 (l4)
## => Fitting passed...
##
## Making model for H_C_2 (l4)
## => Fitting passed...
##
## Making model for N_C_2 (l4)
## => Fitting passed...
##
## Making model for S_C_2 (l4)
## => Fitting passed...
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## Making model for H_T_2 (l4)
## => Fitting passed...
##
## Making model for N_T_2 (l4)
## => Fitting passed...
##
## Making model for S_T_2 (l4)
## => Fitting passed...
##
## 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...
##
## Making model for NT_C_3 (l4)
## => Fitting passed...
##
## Making model for H_C_4 (l4)
## => Fitting passed...
##
## Making model for N_C_4 (l4)
## => Fitting passed...
##
## Making model for S_C_4 (l4)
## => Fitting passed...
##
## 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...
##
## Making model for N_T_5 (l4)
## => Fitting failed. Tagging name of N_T_5...
##
## Making model for S_T_5 (l4)
## => Fitting passed...
##
## Making model for H_C_6 (l4)
## => Fitting passed...
##
## Making model for N_C_6 (l4)
## => Fitting passed...
##
## Making model for S_C_6 (l4)
## => Fitting passed...
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## Making model for H_T_6 (l4)
## => Fitting passed...
##
## 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
## ..
## Found univariate outlier for NT_C_3 NT_C_4 S_T_5 H_T_6 H_T_7
## Tagging name of NT_C_3 NT_C_4 S_T_5 H_T_6 H_T_7 ...
## 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$Gene<-rep("TLR", length(rep1res2))
#In transposing the data frame, the column entries became factors which cannot be used for equations.
#to fix this, I set the entries for sig.eff (efficiency) and sig.cpD2 (Ct value) to numeric. Be aware, without the as.character function the factors will be transformed inappropriately.
rep1res2$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("TLR2rawfluoro.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)
## Making model for H_C_1 (l4)
## => Fitting passed...
##
## Making model for N_C_1 (l4)
## => Fitting passed...
##
## Making model for S_C_1 (l4)
## => Fitting passed...
##
## Making model for H_T_1 (l4)
## => Fitting passed...
##
## Making model for N_T_1 (l4)
## => Fitting passed...
##
## Making model for S_T_1 (l4)
## => Fitting passed...
##
## Making model for NT_C_1 (l4)
## => Fitting passed...
##
## Making model for H_C_2 (l4)
## => Fitting passed...
##
## Making model for N_C_2 (l4)
## => Fitting passed...
##
## Making model for S_C_2 (l4)
## => Fitting passed...
##
## Making model for H_T_2 (l4)
## => Fitting passed...
##
## Making model for N_T_2 (l4)
## => Fitting passed...
##
## Making model for S_T_2 (l4)
## => Fitting passed...
##
## Making model for NT_C_2 (l4)
## => Fitting passed...
##
## Making model for H_C_3 (l4)
## => Fitting passed...
##
## Making model for N_C_3 (l4)
## => Fitting passed...
##
## Making model for S_C_3 (l4)
## => Fitting passed...
##
## Making model for H_T_3 (l4)
## => Fitting passed...
##
## Making model for N_T_3 (l4)
## => Fitting passed...
##
## Making model for S_T_3 (l4)
## => Fitting passed...
##
## Making model for NT_C_3 (l4)
## => Fitting passed...
##
## Making model for H_C_4 (l4)
## => Fitting passed...
##
## Making model for N_C_4 (l4)
## => Fitting passed...
##
## Making model for S_C_4 (l4)
## => Fitting passed...
##
## Making model for H_T_4 (l4)
## => Fitting passed...
##
## Making model for N_T_4 (l4)
## => Fitting passed...
##
## Making model for S_T_4 (l4)
## => Fitting passed...
##
## Making model for NT_C_4 (l4)
## => Fitting passed...
##
## Making model for H_C_5 (l4)
## => Fitting passed...
##
## Making model for N_C_5 (l4)
## => Fitting passed...
##
## Making model for S_C_5 (l4)
## => Fitting passed...
##
## Making model for H_T_5 (l4)
## => Fitting passed...
##
## Making model for N_T_5 (l4)
## => Fitting passed...
##
## Making model for S_T_5 (l4)
## => Fitting passed...
##
## Making model for H_C_6 (l4)
## => Fitting passed...
##
## Making model for N_C_6 (l4)
## => Fitting passed...
##
## Making model for S_C_6 (l4)
## => Fitting passed...
##
## Making model for H_T_6 (l4)
## => Fitting passed...
##
## Making model for N_T_6 (l4)
## => Fitting passed...
##
## Making model for S_T_6 (l4)
## => Fitting passed...
##
## Making model for H_C_7 (l4)
## => Fitting passed...
##
## Making model for N_C_7 (l4)
## => Fitting passed...
##
## Making model for S_C_7 (l4)
## => Fitting passed...
##
## Making model for H_T_7 (l4)
## => Fitting passed...
##
## Making model for N_T_7 (l4)
## => Fitting passed...
##
## Making model for S_T_7 (l4)
## => Fitting passed...
##
## Making model for H_C_8 (l4)
## => Fitting passed...
##
## Making model for N_C_8 (l4)
## => Fitting passed...
##
## 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
## ..
## Found univariate outlier for H_T_2
## Tagging name of H_T_2 ...
## 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$Gene<-rep("TLR", length(rep2res2))
rep2res2$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")
tlr<-as.data.frame(cbind(rep1res2$expression,rep1res2$Names,rep1res2$Pop,rep1res2$Treat,rep2res2$expression))
tlr<-rename(tlr, c(V1="rep1.expr","V2"="name","V3"="pop","V4"="treat"
,"V5"="rep2.expr"))
tlr$rep1.expr<-as.numeric(as.character(tlr$rep1.expr))
tlr$rep2.expr<-as.numeric(as.character(tlr$rep2.expr))
tlr$avgexpr<-rowMeans(tlr[,c("rep1.expr","rep2.expr")],na.rm=F)
tlr<-tlr[which(tlr$name!=c("H_C_3")),]
tlr<-tlr[which(tlr$name!=c("H_T_2")),]
tlr<-tlr[which(tlr$pop!=c("**S")),]
tlr<-tlr[which(tlr$pop!=c("**H")),]
tlr<-tlr[which(tlr$pop!=c("**NT")),]
tlr<-tlr[which(tlr$pop!=c("NT")),]
tlr<-tlr[which(tlr$pop!=c("*N")),]
ggplot(tlr, aes(x=treat,y=avgexpr, fill=pop))+geom_boxplot()
fit<-aov(avgexpr~pop+treat+pop:treat,data=tlr)
fit
## Call:
## aov(formula = avgexpr ~ pop + treat + pop:treat, data = tlr)
##
## Terms:
## pop treat pop:treat Residuals
## Sum of Squares 1.387959e-24 3.623459e-24 1.087264e-24 1.521966e-23
## Deg. of Freedom 2 1 2 36
##
## Residual standard error: 6.502063e-13
## 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 = tlr)
##
## $pop
## diff lwr upr p adj
## N-H 1.871523e-13 -4.283799e-13 8.026845e-13 0.7396288
## S-H 4.499808e-13 -1.655514e-13 1.065513e-12 0.1883345
## S-N 2.628285e-13 -3.175009e-13 8.431578e-13 0.5160357
##
## $treat
## diff lwr upr p adj
## T-C -5.895191e-13 -9.983307e-13 -1.807076e-13 0.0059349
##
## $`pop:treat`
## diff lwr upr p adj
## N:C-H:C 2.035564e-13 -8.088703e-13 1.215983e-12 0.9900053
## S:C-H:C 7.815453e-13 -2.308813e-13 1.793972e-12 0.2116836
## H:T-H:C -3.495711e-13 -1.495001e-12 7.958589e-13 0.9392588
## N:T-H:C -1.437124e-13 -1.189342e-12 9.019174e-13 0.9983185
## S:T-H:C -2.410672e-13 -1.286697e-12 8.045626e-13 0.9815165
## S:C-N:C 5.779889e-13 -4.001082e-13 1.556086e-12 0.4920724
## H:T-N:C -5.531275e-13 -1.668330e-12 5.620748e-13 0.6712284
## N:T-N:C -3.472688e-13 -1.359695e-12 6.651579e-13 0.9039812
## S:T-N:C -4.446237e-13 -1.457050e-12 5.678030e-13 0.7714929
## H:T-S:C -1.131116e-12 -2.246319e-12 -1.591417e-14 0.0451756
## N:T-S:C -9.252577e-13 -1.937684e-12 8.716899e-14 0.0898974
## S:T-S:C -1.022613e-12 -2.035039e-12 -1.018588e-14 0.0465536
## N:T-H:T 2.058587e-13 -9.395713e-13 1.351289e-12 0.9940347
## S:T-H:T 1.085039e-13 -1.036926e-12 1.253934e-12 0.9997234
## S:T-N:T -9.735487e-14 -1.142985e-12 9.482749e-13 0.9997459
