Summary Solution practical 3 to 5 - data mining

PRACTICAL 3
SUMMERY SLIDES

Automation, add-on packages and reshaping

AUTOMATION OF REPETITIVE ANALYSES

Dataset

• Genetic analysis of age-related hearing impairment

• Phenotype : Z-score

- Standardized measure for hearing quality

- Lower Z-score = better

• Association between Zscore and genotype

- SNP genotype : aa, ab, bb

• ANOVA : SNP GT = categorical

• Regression : SNP GT = 0,1,2

• Find SNP associated with phenotype



2 ways to analyse this

1) consider genotype categorical variable and do one way anova

2) Consider genotype to be a nr (count nr of rare alleles) (bb has 2 rare alleles)



Repetitive analysis

• Regression :

- one dependent numeric variable (Y)

- several independent (X) variables

• Regress Y on all separate X-variables

- à several times simple linear regression/ANOVA




1

,Y Xvar1 Xvar2 Xvar3 Xvar4
5.71 aa ab aa ab
-0.93 aa ab ab aa
2.58 ab bb bb aa


In columns the genotype

In the regression, the Y, the numeric phenotype = dependent variable (outcome)

We want to know p, which associations are significant

Don’t run all individually (anova) à smarter ways to do it trough R

R is useful for repetitive analysis




For the first Xvariable
myModel<- lm(Y ~ Xvar1)

For the second Xvariable
myModel<- lm(Y ~ Xvar2)

For the third X variable
myModel<- lm(Y ~ Xvar3)




• 1 analysis:

• myModel<- lm(Y ~ allXvars[,1])


For the first X variable
myModel<- lm(Y ~ allXvars[,1])

For the second X variable
myModel<- lm(Y ~ allXvars[,2])

For the third X variable
myModel<- lm(Y ~ allXvars[,3])



Could also run
i<-1
myModel<- lm(Y ~ allXvars[ ,i])

i<-2
myModel<- lm(Y ~ allXvars[ ,i])

i<-3

2

, myModel<- lm(Y ~ allXvars[ ,i])

Than have everytime the same formula, but you change i
Can ask R that i goes trough all the values

For-loop

• Let i run through all values from i to n, for all commands within the {curly braces}

for(i in 1:3) {

myModel<- lm(Y ~ allXvars[ ,i])

}



In curly brackets put value that i needs to run trough

Will be executed for i= 1, i=2, i=3



Parameter estimates in a loop


• Suppose in each step you estimate a parameter or a p-value
• First create empty vector to save the output from each step
p.value<-rep(NA,3)
for(i in 1:3) {
myModel<-lm(Y ~ allXvars[ ,i])
p.value[i]<-anova(myModel)[1,5]


We need p-values

Have to extract p value from that model, but we don’t want to overwrite the p-value

Have to extract and store it somewhere

First create an empty vector

Rep=repeat

Before start loop, create empty vector with 3 spaces

In the first loop you assign the result of the p value to the first place of the empty vector

When R has finished the loop, the vector has been filled and contains all p values of all loops



Automation with new function
• Piece of script, to be carried out multiple times
Data<- read.table(“input_1.txt”)
p.value<-t.test(…)
write.table(p.value,file=“output_1.txt”)

• 3 similar input files

3

, Data<- read.table(“input_1.txt”)
p.value<-t.test(…)
write.table(p.value,file=“output_1.txt”)
Data<- read.table(“input_2.txt”)
p.value<-t.test(…)
write.table(p.value,file=“output_2.txt”)
Data<- read.table(“input_3.txt”)
p.value<-t.test(…)
write.table(p.value,file=“output_3.txt”)




• Wrap the piece of code into a new function
- Give name to function
- List necessary arguments
- Use argument names in the code

doMyAnalysis<- function(inputfile,outputfile) {
Data<- read.table(inputfile)
p.value<-t.test(…)
write.table(p.value,file=outputfile)



To run the new function


• Initialize the new function
- Select code and run
- Each time you restart R
- No output
• Run
doMyAnalysis(“input_1.txt”,”output_1.txt”)
doMyAnalysis(“input_2.txt”,”output_2.txt”)
doMyAnalysis(“input_3.txt”,”output_3.txt”)




Further automation


• Using a list object
- Consists of other objects (elements)
- Individual elements accessed by double square brackets
list.object[[i]]
• Here
- Put input files and/or output files in a list
- 1 list-objects, containing the 3 input-dataframes




Combine list and for-loop


• First create empty list
Mylist<-vector(“list”,n.elements)



• Read in the 3 inputfiles

4