Workshop: R and Bioinformatics

Transcription

1 Workshop: R and Bioinformatics Jean Monlong & Simon Papillon Human Genetics department October 28,

2 Why using R for bioinformatics? I Flexible statistics and data visualization software. I Many packages and a vast community: Bioconductor. I Simple and easy, compared at other computing languages. 2

3 Today s workshop Goal I Explain and demonstrate some key principles to do good bioinformatics. I How to structure the analysis, how to write e explore your data. I What to do and not to do... cient script, I This is NOT a package tutorial but will point at useful resources. Learning from your mistakes You will get errors today, before raising your hand: I Check your command for typos. I Try to understand the error message. I Check your input/objects. 3

4 Functions Previously on HGSS workshops: function To define functions. return Define what will be returned by the function. All the object created within the function are temporary. Structure myfunctionname <- function(input.obj1,second.input.obj ) { Intructions on input.obj1 and second.input.obj... return(my.output.obj) } myfunctionname(1,c(2,4,5)) 4

5 Conditions Logical tests == Are both values equal. > or >= Is the left value greater (greater or equal) than the right value. < or <= Is the left value smaller (smaller or equal) than the left value.! Is a NOT operator that negates the value of a test. Is an OR operator used to combine logical tests. Returns TRUE if either are TRUE. & Is an AND operator used to combine logical tests. Returns TRUE if both are TRUE Example test < == 4!test test!test test &!test ## (TRUE) ## (FALSE) ## (TRUE) ## (FALSE) 5

6 Conditions Boolean Any logical tests can be vectorized (compare 2 vectors). Example which Returns the index of the vectors with TRUE values. 5:8 == 6 ## FALSE,TRUE,FALSE,FALSE 5:8 >= 6 & 5:8<=7 ## FALSE,TRUE,TRUE,FALSE c(true, TRUE) & c(true, FALSE) ## TRUE,FALSE c(true, FALSE) c(false, FALSE) ## TRUE,FALSE which(5:10 == 6) ## 2 which(5:10 > 6) ## 3,4,5,6 6

7 Conditions - Exercise Exercise Create a function that: 1. remove values below 3 from a vector. 2. remove values below a specified threshold from a vector. For more advanced users Have a look at these functions on logical vectors: I any, %in%. I sum, mean, table. Extra tips I Don t filter out, keep in. I Use the boolean vector directly between [ ]. 7

8 Testing conditions if else Test a condition, if TRUE run some instruction, if FALSE something else (or nothing). if( Condition ){... Instructions } Example if(length(luckynumbers)>3){ cat("too many lucky numbers.") luckynumbers = luckynumbers[1:3] } else if(length(luckynumbers)==3){ cat("just enough lucky numbers.") } else { cat("you need more lucky numbers.") } 8

9 Loops for loops Iterate over the element of a container and run instructions. for(v in vec){... Instruction } while loops Run instructions as long as a condition is TRUE. while( CONDITION ){... Instruction } Example facto = 1 for(n in 1:10){ facto = facto * n } 9

10 Exercises if else Create a function that classify the average value of a vector. It returns: I low if the average if below 3. I medium if the average if between 3 and 7. I high if the average if above 7. Loops Write a function that computes the mean values of a matrix columns: 1. using the apply function. 2. using a for loop. 3. (using a while loop.) 10

11 Important principles Scripting Write scripts of your analysis: I Keeping track, easy rerun, easy parameter tweaking. I Rstudio or other interfaces (Emacs+ESS,...). Clear and modular code E I Define clear analysis steps. I Write function(s) for each step. I Keeps the data and parameters used clear (for you and for R). I No confusing temporary objects. I No repeating code. I More suitable for apply-like usage. I Easy parameter tweaking. ciency matters I Data structure and manipulation. I Especially relevant with our large data. 11

12 Data exploration - All you can plot Answer your questions with plots! Utility I Get an idea of the quality of the data and potential issues. I Get a full answer. I Detect potential biases. I (Find unexpected results.) Through all your analysis I Quality Control plots at the beginning. I Control plot after each steps. I Awesome plot with your results. 12

13 Today s dataset - Methylation analysis Goal Analyze methylation data following those principles. DNA methylation data I Identify sample outliers (and remove them). I Identify co-variates (sex, age) using PCA. I Use heatmap to plot sample groupings. I Point out di erentially methylated sites. I Plot methylation levels of interesting sites. The data set beta_value # The methylation data probe_data # The annotation of each probe pheno_data # The annotation of each sample (metadata) Get to know your data Check the first few rows of each object (remember the head function). Check the type and size of your data(str). 13

14 Importing large files more e ciently Extra parameters in read.table colclasses a vector with the data type of each column: e.g. character, numeric. nrows the number of rows to read. Potentially in combination with system and wc. Read a file line by line (or by chunk) con = file(file.name) while(length(line = readlines(con,n=1))>0){... Instructions } To test the performance system.time({... Instructions }) 14

15 Manipulating large files: a classical error Don t do this! Concatenate iteratively on big data. mymatrix = NULL for(i in 1:100000){... Instructions mymatrix = rbind(mymatrix, mynewline) } Instead do this Create the data and then fill it. mymatrix = matrix(na,100000,100) for(i in 1:100000){... Instructions mymatrix[i,] = mynewline } 15

16 Identify outliers Checking the distribution I Identify the case samples. I Take a look at the density of those samples. I Are there any samples that stand out? Useful functions density(x) # Compute the density of x plot(x) # Create a plot of x plot(density(x)) # Plot the density of x 16

17 Quality Control plots Aim Assess the quality of your data and potential artefacts that could bias your analysis. Basic approaches I Principal Component Analysis: representing the largest variation in the data. I Clustering: summarizing similarity relations between samples/genes. I Heatmaps: summary of the clustering on both row and columns. I Testing metadata: gender, age,... 17

18 Quality Control plots - Functions PCA using prcomp PCA of the matrix columns; plot of the variance explained by the first PCs; representation of the rows using the first two PCs. peeseaaye = prcomp(input.matrix) plot(peeseaaye) plot(peeseaaye$x,type="n") text(peeseaaye$x,labels=rownames(input.matrix)) Clustering using hclust Clustering using a distance matrix, e.g. from correlation between columns. cor.dist = as.dist(1-cor(input.matrix)) euc.dist = dist(input.matrix) kleusteur = hclust(cor.dist,method="ward") plot(kleusteur) library(mass) mdees = isomds(cor.dist) plot(mdees$points) 18

19 Heatmaps Using heatmap function heatmap(input.matrix) 19

20 Application - Identify outliers Using the PCA I Using all samples, plot the first 2 Principle Components. I Color the samples according to their Sample Group status. I How do the outliers identified previously behave? I Remove those samples from your data set (dont forget to propagate the changes in all objects!). PCA example pca <- prcomp(t(beta_value)) plot(pca$x) 20

21 Application - Identify phenotypic co-variates Using the PCA I Re-compute the PCA without the outlier samples. I Plot the 1st PC with the age of the samples (do you notice something?) I Color the sample according to their Sample Group status I Re-compute the PCA using control samples only. I Does sex have an e ect on data? I Knowing that females have 2 X chromosomes, 1 silenced by methylation are you able to predict the sex of the samples? I Check your prediction in the PCA plot 21

22 Application - Predicting the sex Some tips # Here we assume that the X chromosome is more methylated # in females x_probes <- probe_data$chrom == "chrx" summary(x_probes) controls <- pheno_data$sample_group == "control" summary(controls) pca <- prcomp(t(beta_value[x_probes, controls])) mean_x_meth <- apply(beta_value[x_probes, controls], 2, mean) color_vec <- ifelse(mean_x_meth >= 0.45, "red", "blue") plot(pca$x, col=color_vec) 22

23 Automated approach to identify metadata co-variates Linear regression For example, between Principal Component and metadata. I Can be automated to test other Principal Components and numerous metadata. I To make a decision on borderline/unclear cases. Application summary(lm(pca$x[,1]~pheno_data$age)) summary(lm(pca$x[,2]~pheno_data$age)) summary(lm(pca$x[,3]~pheno_data$age)) covar.test = summary(lm(pca$x~pheno_data$age)) covar.test.pv = unlist(lapply(covar.test,function(l) 1-pf(l$fstatistic[1],l$fstatistic[2],l$fstatistic[3]))) covar.test.pv[covar.test.pv<.01] 23

24 Cluster the samples Using the heatmap I We dont need to use the full data set (too big) I Let s find the 1,000 most variant sites and use them for clustering I Make the heatmap (see previous examples) I Color the sample according to their Sample Group status (use ColSideColors=color vec in heatmap) Predicting the sex # Getting the 1,000 most variant sites # # Compute the variance probe_var <- apply(beta_value, 1, var) # Order them decreasingly probe_var <- order(probe_var, decreasing = T) # Get the top 1,000 probe_var <- probe_var[1:1000] 24

25 Find di erentially methylated sites At-testcompares2distributions I We want to compare cases vs controls at each site (loop) I First, make a boolean vector for cases and one for controls (we saw how in previous slides) t-test, an example t.test(rnorm(100), rnorm(100)) # Getting only the p-value t.test(rnorm(100), rnorm(100))$p.value Non-parametric alternative Wilcoxon test using wilcox.test is a rank based test. 25

26 Plot di erentially methylated sites Using boxplots I Using a previously identified di erentially methylated site make a boxplot of this site for cases and controls. methylation control ETMR 26

27 GenomicRanges... Introduction Represents genomic intervals. All annotation can be represented through GenomicRanges objects. Creation mygr = GRanges(chrs, IRanges(start=starts,end=ends)) Overlaping intervals myoverlap = findoverlaps(mygr, genes.gr) queryhits(myoverlap) length(unique(queryhits(myoverlap))) Distance to nearest disttogene = distancetonearest(mygr, genes.gr) which(disttogene < 100) 27

28 ...and annotation Introduction Many annotation are already available directly from R, see Bioconductor website. Else you can create your own GenomicRanges object. TxDb Gene annotation. source(" bioclite("txdb.hsapiens.ucsc.hg19.knowngene") library(txdb.hsapiens.ucsc.hg19.knowngene) AnnotationHub Many di erent tracks, including most of Encode s. source(" bioclite("annotationhub") library(annotationhub) ah = AnnotationHub() ctcf.tfbs = ah$goldenpath.hg19.encodedcc.wgencodeuwtfbs. wgencodeuwtfbsmcf7ctcfstdpkrep1.narrowpeak_0.0.1.rdata 28

29 Extra: ggplot2 Introduction A package to constuct beautiful and/or complex graphs. Many aspects of the graph are arranged automatically but everything can be specified. Easy layers addition cg cg cg cg cg cg cg cg cg cg cg cg methylation density cg cg cg cg cg cg control ETMR control ETMR control ETMR methylation control ETMR control ETMR 29

30 Extra: ggplot2 - data.frame only data.frame The input object is always a data.frame, with each rows being one point to represent and each column the di erent information on it. data.frame: in its simple version, a matrix with di erent data type possible in each column. Useful functions data.frame To create a data.frame. subset To subset a data.frame using condition on the columns. melt/reshape To deconstruct a matrix into data.frame, or the opposite. reshape package. aggregate To compute summary statistics on subset of the data.frame. ddply apply-like function on subset of the data.frame. plyr package. Example gene.expression.df = data.frame(gene=c("a","b","c"), gene.expression=1:3) dim(gene.expression.df) gene.expression.df = subset(gene.expression.df, gene.expression>1) 30

31 Extra: ggplot2 Simple graph - Histogram df.to.plot = data.frame(value=rnorm(1000)) library(ggplot2) ggplot(df.to.plot,aes(x=value)) + geom_histogram() More complex graph Putting a color for each group and di erent panels for each probe. ggplot(beta.df, aes(x=methylation)) + geom_density(aes(fill=group,colour=group),alpha=.7) + facet_wrap(~probe) Learn from the examples there I I 31

32 Online tutorials R I : small video-tutorials. I : R and statistics small web-tutorials. I : Coursera Computing for Data Analysis videos. Other interesting videos, e.g. ggplot2. I : R manual. R and Bioinformatics I List of online resources for Bioinformatics. I : Bioinformatics workshop material. I : Pieces of code for bioinformatics analysis, plots. Including Bioconductor. I : Bioinformatics tutorials material: pdf and R scripts. 32

33 Thank you!! If you re interested in potentially more sessions, in di erent format (more often, more specific), maybe some kind of Rclub,letus know through the survey or by . 33

34 Lists Flexible container A list can contain any element type. It does not require elements to be of the same type. Example list Create a list. l[[i]] Get or set the i th object of the list. l$toto Get or set the element labeled as toto. names Get or set the names of the list elements. length Get the number of element in the list. str Output the structure of a R object. l = list(vec=1:10,mat=matrix(runif(25),5)) str(l) l l$vec = 1 l 34

35 Functions - lapply apply for lists I Useful way to iterate through lists. Example file_list <- list.files(. ) files_content <- lapply(file_list, function(file) \{ data <- read.csv(file) #Do something with the data return(data) \}) 35