## Friday, July 10, 2015

### 7 10 2015 CARM expression boxplot and statistics

Today I ran the reps of CARM through my comparison script and then averaged the expression values together between the reps. Then using this average expression I generated a boxplot to show the differences between populations and treatment/control. After that, I then ran the data through an ANOVA and Tukey's Honestly Significant Difference test to determine if these differences were statistically significant.  repcomparison.R
#Load in required packages for functions below
require(qpcR)
## Loading required package: qpcR
## 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
#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... ## ## 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 ## .. ## 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(rep1res2sig.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 rep1res2expression<-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$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 ## .. ## 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

As you can see the treatment and control are significantly different from one another but on an a population basis there is no difference between populations or any treatment/population combinations.