Presentation R basic teaching module

Presentation R basic teaching module

• 1.
  Introduction to R Basic Teaching module EMBL International PhD Program 13-10-2010 Sander Timmer & Myrto Kostadima
• 2.
  Overview What is R Quickoverview datatypes, input/output and plots Some biological examples I’m not a particular good teacher, so please ask when you’re lost!
• 3.
  What is thisR thing? R is a powerful, general purpose language and software environment for statistical computing and graphics Runs on Linux, OS X and for the unlucky few also on Windows R is open source and free!
• 4.
  Start your Rinterface
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  Variables x <- 2 x<- x^2 x [1] 4
• 6.
  Vectors Many ways ofgenerating a vector with a range of numbers: x <- 1:10 assign(“x”, 1:10) x <- c(1,2,3,4,5,6,7,8,9,10) x <- seq(1,10, by=1) x <- seq(length = 10, from=1,by=1) x [1] 1 2 3 4 5 6 7 8 9 10
• 7.
  Vectors Common way tostore multiple values x <- c(1,2,4,5,10,12,15) length(x) mean(x) summary(x)
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  Vectors Vectors are indexed x[5]+ x[10] [1] 15 x[-c(5,10)] [1] 1 2 3 4 6 7 8 9
• 9.
  Matrices Common form ofstoring 2 dimensional data Think about having an Excel sheet m = matrix(1:10,2,5) [,1] [,2] [,3] [,4] [,5] [1,] 1 3 5 7 9 [2,] 2 4 6 8 10 summary(m)
• 10.
  Factors Factors are vectorswith a discrete number of levels: x <- factor(c(“Cancer”, “Cancer”, “Normal”, “Normal”)) levels(x) [1] “Cancer” “Normal” table(x) Cancer Normal 2 2
• 11.
  Lists A list cancontain “anything” Useful for storing several vectors list(gene=”gene 1”, expression=c(5,2,3)) $gene [1] “gene 1” $expression [1] 5, 2, 4
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  If-else statements Essential forany programming language if state then do x else do y if(p < 0.01){ print(“Significant gene”) }else{ print(“Insignificant gene”) }
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  Repetition You want toapply 1 function to every element of a list for(element in list){ ....do something.... } For loops are easy though tend to be slow Apply is the fast way of getting things done in R: apply(List,1,mean)
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  Data input R hascountless ways of importing data: CSV Excel Flat text file
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  Data input Most simple,the CSV file: read.csv(“mydata.csv”, row.names=T,col.names=T) Load a tab separated file read.table(“mytable.txt”, sep=”t”) Load Rdata file load(“mydata.Rdata”)
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  Data input Also formore specific data sources: Excel Database connections Mysql -> Ensembl e.g. Affy Affymetrix chips data HapMap .........
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  Data output Most simple,the CSV file: write.csv(x, file=”myx.csv”) Save Rdata file: save(x, file=”myx.Rdata”) Save whole R session: save(file=”mysession.Rdata”)
• 18.
  Graphics Quick way tostudy your data is plotting it The function “plot” in R can plot almost anything out of the box (even if this doesn’t make sense!)
• 19.
  plot(1:5,5:1)
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  plot(1:5,5:1, col=”red”, type=”l”)
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  plot(1:5,5:1, col=”red”, type=”l”, main="Title of this plot", xlab="x axis", ylab="y axis")
• 22.
  Basic graphics With Ryou can plot almost any object Multidimensional variables like matrixes can be plotted with matplot() Other often used plot functions are: boxplot(), hist(), levelplot(), heatmap()
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  Advanced plotting
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  Advanced plotting
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  Advanced plotting
• 26.
  Before the example Helppage for functions in R can be called: ?plot, ?hist, ?vector Examples for most functions can be runned: example(plot) Text search for functions can be done by performing: ??plot
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  Example Some example Affymetrixdataset to play with Checking distribution of data Plotting data Clustering data Correlate data
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  Read file library(affy) library(affydata) data(Dilution) print(Dilution)
• 29.
  Read file dil =pm(Dilution)[1:2000,] dil.ex = exprs(Dilution)[1:2000,] rownames(dil.ex) = row.names(probes(Dilution))[1:2000]
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  Summary Checking what wegot summary(dil) mva.pairs(dil) Or: boxplot(log(dil.ex)) Or: hist(dil.ex, xlim=c(0,500), breaks=1000)
• 31.
  We need tonormalise first For almost all experiments you have to apply some sort of normalisation dil.norm = maffy.normalize(dil, subset=1:nrow(dil)) colnames(dil.norm) = colnames(dil) mva.pairs(dil.norm)
• 32.
  Most equal samples Applyingeuclidian distance to detect most equal samples dil.norm.dist = dist(t(dil.norm)) dil.norm.dist.hc = hclust(dil.norm.dist) plot(dil.norm.dist.hc) Do the same for the non normalised dataset
• 33.
  Checking expression Heatmap representationof expression levels for different probes heatmap(dil.ex.norm[1:50,]) You could apply a T-test for example to rank to only plot the most significant probes
• 34.
  Checking expression Heatmap representationof expression levels for different probes heatmap(dil.ex.norm[1:50,]) You could apply a T-test for example to rank to only plot the most significant probes
• 35.
  Checking expression You couldapply a T-test for example to rank to only plot the most significant probes library(genefilter) f = factor(c(1,1,2,2)) dil.exp.norm.t = rowttests(dil.exp.norm, fac=f) heatmap(dil.exp.norm[order(dil.exp.norm.t $dm)[1:10],])
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  Want to knowmore? Using R will benefit all PhD’s in this room Learning by doing Loads of basic examples at: http://addictedtor.free.fr/graphiques/ http://www.mayin.org/ajayshah/KB/R/ index.html http://www.r-project.org/
• 37.
  Just keep inmind......
• 38.
  Questions? Contact me: swtimmer@ebi.ac.uk http://www.ebi.ac.uk/~swtimmer/ forslides or http://www.slideshare.net/swtimmer