Introduction to R. Richard Wang, PhD CBI fellow Nelson & Miceli labs

Transcription

1 Introduction to R Richard Wang, PhD CBI fellow Nelson & Miceli labs

2 Workshop 3: Introduction to R Day 2 Statistical methods Probability distributions Random number generation Common statistical tests Fisher s exact test, chi square, qvalue, etc Visualization Plotting functions Customizing plots Saving plots for publication IGV CummeRbund

3 Lists Ordered collection of components Each component can be of a different type Each component can A dataframe is actually a list with more structure > lst = list(a=1:3, b="cairo", c=sqrt) > lst[1] #get a sublist $a [1] > lst[[1]] #returns the object of type stored in 1st position (ie numeric) [1] > lst$a > lst[["a"]] > class(lst[1]); class(lst[[1]]); # compare the two

4 Probability distributions For every statistical distribution in R, these functions are available cumulative distribution function density quantile random numbers Example distributions Gaussian or normal binomial poisson gamma beta t chi-square

5 Probability distributions For example in normal distribution (gaussian) there is a family for 4 functions pnorm() = P(X < x) dnorm() = P(x) qnorm() = quantile rnorm()

6 Statistics Many distributions and tests built in Others added as packages Interpreting output, names, pvalues

7 Basic stat functions summary() var() sd() mean() median()

8 Common statistical tests cor.test() t.test() fisher.test() chisq.test() wilcox.test() ks.test() p.adjust() regression: lm(y ~ x) anova: aov()

9 Try it out: ALL data x = read.table("all_subset.txt", header=t) Compare two samples by t.test t.test(x[,1], x[,2]) Compute the correlation between two samples cor.test(x[,1], x[,2]) Compute all pairwise correlations, returns matrix cor(x)

10 A chisquare example > mat = matrix( c(1463, 48486, 7892, ), nrow=2, ncol=2) > mat [,1] [,2] [1,] [2,] > chisq.test(mat) Pearson's Chi-squared test with Yates' continuity correction data: mat X-squared = , df = 1, p-value < 2.2e-16 > mat.chisq = chisq.test(mat)

11 A chisquare example > mat.chisq = chisq.test(mat) > names(res.mat) [1] "statistic" "parameter" "p.value" "method" "data.name" "observed" [7] "expected" "residuals" "stdres" > res.mat$p.value [1] e-17

12 Simple Regression > data(iris) > head(iris) > # attach(iris) did not run > names(iris) [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" > lm(sepal.length ~ Petal.Length) Call: lm(formula = Sepal.Length ~ Petal.Length) Coefficients: (Intercept) Petal.Length

13 Simple Regression > a = lm(sepal.length~ Petal.Length) > names(a) [1] "coefficients" "residuals" "effects" "rank" "fitted.values" "assign" "qr" "df.residual" "xlevels" [10] "call" "terms" "model" > summary(a) Call: lm(formula = Sepal.Length ~ Petal.Length) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) <2e-16 *** Petal.Length <2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 148 degrees of freedom Multiple R-squared: 0.76, Adjusted R-squared: F-statistic: on 1 and 148 DF, p-value: < 2.2e-16

14 Plotting A great strength of R is visualization Many type of plots: line histogram scatter plot topographical A great display of R graphs at

15 Histogram Very useful! > a = rnorm(100) > hist(a) > hist(a, breaks=10, col="red", main="my hist") breaks can be customized > br = c(-30, 0, 1,4) > br [1] > res = hist(a, breaks=br, col="red")

16 Histogram returns an object of class "histogram" > result.a = hist(a) > class(result.a) [1] "histogram" > names(result.a) [1] "breaks" "counts" "intensities" "density" "mids" [6] "xname" "equidist"

17 Histogram > result.a $breaks [1] $counts [1] $intensities [1] $density [1] $mids [1] $xname [1] "a" $equidist [1] TRUE attr(,"class") [1] "histogram" >

18 Histogram hist() can plot counts as well as frequency but frequency definition may not be what you expect > z = hist(c(rep(1,20), 2,3), freq=f) > sum(z$density) [1] 2 The AUC sums to 1. Distance between breaks is 0.5 sum(z$density) * 0.5 > sum(z$density)*0.5 [1] 1 If you want percentage, better to do this br = c(0,1,2,3) z = hist(c(rep(1,20), 2,3), breaks=br) barplot(z$counts/sum(z$counts), names.arg=c(1,2,3))

19 Try it out #Read in the ALL dataset > x = read.table("all_subset.txt") # how many expts are there? how many probes? > dim(all) # plot histogram > hist(x[,1], xlim=c(0,25)) > hist(x[,2], xlim=c(0,25), col="blue", add=t) > hist(x[,3], xlim=c(0,25), col="red", add=t) # box plot of all expts; three expts > boxplot(x); > boxplot(x[, c(1,12,3)])

20 Plotting plots histogram boxplot barplot scatter plot heatmap cluster points, lines, segments abline qqplot & more customizations axes header text/title margin text identify display symbols layout colors

21 A plain scatter plot x = rnorm(10) y = rnorm(10) plot(x,y)

22 Graphical parameters R for Beginners E. Paradis

23 Printing character you can specify the pch parameter a number (pch=1 gives a circle) a text character (pch="v" uses the letter "v")

24 Try it out #Read in the ALL dataset > x = read.table("all_subset.txt") # how many expts are there? how many probes? > dim(all) # plot the first three expts against the first on the same page > par(mfrow=c(1,3)); > plot(x[,1], x[,2]); > plot(x[,1], x[,3]); > plot(x[,1], x[,4]) #close the window > dev.off() # plot one graph again, what's different? plot(x[,1], x[,2]) # change some plotting options > plot(x[,1], x[,2], pch=".", col="blue", xlab="expt1", ylab="expt2") # add a title with the correlation in it > x.cor = cor.test(x[,1], x[,2]) > title(paste("expt 1 versus Expt 2\n cor=", round(x.cor$estimate, 2))) # log transform > plot(log2(x[,1]), log2(x[,2]), pch=".", col="blue", xlab="expt1", ylab="expt2", main="expt1 versus Expt2")

25 Let's add options plot(x,y, xlab="ten random values", ylab="ten other values", xlim=c(-2,2), ylim=c(-2,2), pch=22, col="red", bg="yellow", bty="l", tcl=0.4, main="a customized plot", las=1, cex=1.5)

26 Plotting using base graphics > opar = par() > par(bg="lightgray", mar=c(2.5, 1.5, 2.5, 0.25)) > plot(x, y, type="n", xlab="", ylab="", xlim=c(-2,2), ylim=c(-2,2), xaxt="n", yaxt="n") > rect(-3, -3, 3, 3, col="cornsilk") > points(x, y, pch=10, col="red", cex=2) > axis(side=1, c(-2, 0, 2), tcl=-0.2, labels=false) > axis(side=2, -1:1, tcl=-0.2, labels=false) > title("manually customizing a plot", font.main=4, adj=1, cex.main=1) > mtext("ten random values", side=1, line=1, at=1, cex=0.9, font=3) > mtext("ten other values", side=2, line=0.5, at=-1.8, cex=0.9, font=3) > mtext(c(-2, 0, 2), side=1, las=1, at=c(-2,0,2), line=0.4, col="blue", cex=0.9) > mtext(-1:1, side=2, las=1, at=-1:1, line=0.2, col="blue", cex=0.9) > par(opar)

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28 Scatter plots, line plots the type parameter controls the scatter plot type options include points, lines, points+lines and histogram > somedata = rnorm(25) > plot(somedata, type="p") > plot(somedata, type="l") > plot(somedata, type="b") > plot(somedata, type="h")

29 A few tips If you have many points ( 's), the plot may become obscured Potential remedies: downsample your data: suppose you have 1000 rows of data in a data frame, you can pick some indices to display # this will pick 25 numbers between 1 and 100 ids = sample(1:100, 25) try a smaller plotting symbol: pch="." will use a dot plot(x[ids, 1],x[ids,2], pch=".")

30 smoothscatter x1 = rnorm(100000) y1 = rnorm(100000) x2 = rnorm(100000, mean=2, sd=1.5) y2 = rnorm(100000, mean=3, sd=1.5) xy = rbind(cbind(x1, y1), cbind(x2, y2)) smoothscatter(xy) Alpha values # colors are given as amount of # Red, Green, Blue and Alpha with range [0,1]. # you can specify maxcolorvalue=255 if you want to use [0,255] plot(x,y, col=rgb(0,0.5,0,0.1))

31 lines() abline(lm (y ~x)) points() legend() qqnorm identify() barplot(), stacked barplot pie() boxplot()

32 Adding additional points to a plot points() lets you add points to an existing plot x = rnorm(10); y=rnorm(10) xx = rnorm(10); yy = rnorm(10) plot(x, y) points(xx, yy, col="green", pch="v") if nothing shows up, your axes might not contain the data area

33 Adding additional lines to a plot data(orange) tree1 = which(orange$tree == 1) tree2 = which(orange$tree == 2) lines(orange$age[tree1], Orange$circumference[tree1], type="b", lwd=1.5, lty=1, col=2) lines(orange$age[tree2], Orange$circumference[tree2], type="b", lwd=1.5, lty=1, col=3) legend("bottomright", legend=c("old", "young"), fill = c(2,3), col=c(2,3))

34 Adding a regression line Earlier we plotted the iris dataset plot(iris$petal.length, iris$sepal.length) Compute the OLS regression and add to plot model = lm(iris$sepal.length ~ iris$petal.length) summary(model) abline(model, lwd=3, lty=4)

35 More plots Barplot barplot(rbind(1:4, 3:6)) barplot(rbind(1:4, 3:6), horiz=t) Boxplots data(iris) boxplot(iris$sepal.length ~ iris$species) QQnorm qqnorm(rnorm(100)) QQplot y <- rt(200, df = 5) qqnorm(y); qqline(y, col = 2) qqplot(y, rt(300, df = 5)) #lengths need not be identical

36 More plots Hierarchical clustering all = read.table("all_subset.txt") hier1 = hclust( dist(1-cor(all))) plot(hier1) Heatmap heatmap(cor(all), symm=t)

37 Multiple plots per page Use par par(mfrow=c(2,1)) Another method is to designate matrix of slots layout( matrix(c(1,2,3,4), ncol=2, nrow=2)) for(i in 1:4){ plot(rnorm(10)) } # compare with layout(matrix(c(1,1,2,3),2,2) Once you close a window, settings disappear

38 Graphics devices R will "write" plots to a graphics device If you don't have one, for example you ssh into a remote machine, it will write to a file called Rplots.pdf in your current directory. # show your current graphic device dev.cur() # show all devices dev.list() # select a device (from already open) dev.set() # create a new window x11() window() quartz()

39 Output formats pdf(), png(), bmp(), tiff(), jpeg(), formats all supported png(filename="file.png", width=800, height=800) dev.cur(), dev.off(), dev.set() To create a graphic, think of it as writing an image to a file pdf(file="foo.pdf") plot(rnorm(100)) dev.off() Don't forget to dev.off() your device, otherwise your figure will get corrupted!

40 Fancier graphics There are more sophisticated plotting paradigms for multipanel, multidimensional data lattice trellis ggplot

41 Genome visualization Suppose you identify a genomic region of interest and you want to know what features are near it high homozygosity, associated SNPs, Chip-Seq peak UCSC genome browser use custom tracks to upload your data in BED format Broad IGV Great for seeing a ROI in context Does not generate publication quality images

42 IGV

43 GWAS format IGV accepts many types of formats GWAS format is simply chrom, sart, snpid, pval

44 IGV

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46 CummeRbund Designed for Cufflinks output Visualization of data quick run through see protocol manual part of bioconductor

47 Extending R Many distributions and tests built in Others added as packages available from CRAN bioconductor is specialized for life sciences Packages essentially add new functionality to R

48 Installing package from CRAN installing packages is usually easy biggest problems are due to incompatibility of versions of R with versions of a package eg MASS ver 2 requires R version >= 2.13 source code is always available install.packages() where do files get stored?

49 Finding packages

50 Finding packages

51 Using the GUI Use "Package" menu select a mirror (aka geographically close repository) Repo should include CRAN Install packages allows you to select Update packages allows you to select which packages to update

52 Troubleshooting install problems Usually a dependency is missing missing dev package out of date package sometimes R needs access to a C or Fortran compilier need administrator access

53 Installing from bioconductor set of packages tailored to life sciences requires its own installation method but relies on many tools from CRAN navigating the bioconductor website source(" example datasets

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56 Exercise Try and install bioconductor packages GenomicFeatures cummerbund DEseq Both will require several (many?) additional packages, so let s them install source(" # this installs core bioconductor packages bioclite() # install Rsamtools bioclite("rsamtools") # GenomicFeatures and gene positions bioclite("genomicfeatures") bioclite("txdb.hsapiens.ucsc.hg19.knowngene") # this installs DEseq2 bioclite("deseq2") # other useful data sets bioclite(c("pasilla", "parathyroidse") # this would install cummerbund bioclite("cummerbund")

57 Exercises If you run into trouble installing packages On a PC running Windows Try running R in administrator mode From the start menu, right click on the R program and select Run as administrator

58 Exercises If you run into trouble installing packages On a Mac You may need administrator privileges On Unix/Linux You may need to become root to install to the correct directory Try running sudo either > sudo R OR > sudo su > R And then installing packages

59 Exercises did it work? if install works, issuing these commands will not give you an error library("deseq2") library("txdb.hsapiens.ucsc.hg19.knowngene")

60 Additional resources Many resources but sometimes difficult to access R-bloggers.com R-project.org FAQ and manuals bioconductor.org UCLA ATS ( learning modules Quick R manuals.bioinformatics.ucr.edu/workshops Good free books An Introduction to R (on r-project.org) Venables, Smith, R Core Using R R for Beginners by Emmanuel Paradis

61 Some R books R in Action, Robert I. Kabacoff R Cookbook, Paul Teetor The R book, Michael Crawley Introduction to the R Project for Statistical Computing for Use at the ITC, Rossiter

62 More Resources software-carpentry.org rosalind project