Beginner's Guide To Using The DESeq2 Package DEGSeq2
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Beginner’s guide to using the DESeq2 package Michael Love1∗ , Simon Anders2 , Wolfgang Huber2 1 Department of Biostatistics, Dana Farber Cancer Institute and Harvard School of Public Health, Boston, US; 2 European Molecular Biology Laboratory (EMBL), Heidelberg, Germany ∗ michaelisaiahlove (at) gmail.com May 13, 2014 Abstract This vignette describes the statistical analysis of count matrices for systematic changes between conditions using the DESeq2 package, and includes recommendations for producing count matrices from raw sequencing data. This vignette is designed for users who are perhaps new to analyzing RNA-Seq or high-throughput sequencing data in R, and so goes at a slower pace, explaining each step in detail. Another vignette, “Differential analysis of count data – the DESeq2 package” covers more of the advanced details at a faster pace. DESeq2 version: 1.4.5 If you use DESeq2 in published research, please cite: M. I. Love, W. Huber, S. Anders: Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. bioRxiv (2014). doi:10.1101/002832 [1] 1 Beginner’s guide to using the DESeq2 package 2 Contents 1 Introduction 2 2 Input data 2.1 Preparing count matrices . . . . . . . . . . . . . 2.2 Aligning reads to a reference . . . . . . . . . . . . 2.3 Counting reads in genes . . . . . . . . . . . . . . 2.4 Experiment data . . . . . . . . . . . . . . . . . . 2.4.1 The DESeqDataSet, column metadata, and 2.4.2 Starting from SummarizedExperiment . . . 2.4.3 Starting from count tables . . . . . . . . . 2.5 Collapsing technical replicates . . . . . . . . . . . 3 Running the DESeq2 pipeline 3.1 Preparing the data object for the analysis 3.2 Running the pipeline . . . . . . . . . . . 3.3 Inspecting the results table . . . . . . . 3.4 Other comparisons . . . . . . . . . . . . 3.5 Multiple testing . . . . . . . . . . . . . 3.6 Diagnostic plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . the design . . . . . . . . . . . . . . . . . . of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 3 4 7 7 8 10 12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 13 14 15 16 17 19 . . . . . . . . . . . . . . . . . . . . . . . . 4 Independent filtering 22 4.1 Adding gene names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Exporting results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5 Working with rlog-transformed data 25 5.1 The rlog transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Sample distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.3 Gene clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6 Session Info 1 31 Introduction In this vignette, you will learn how to produce a read count table – such as arising from a summarized RNA-Seq experiment – analyze count tables for differentially expressed genes, visualize the results, add extra gene annotations, and cluster samples and genes using transformed counts. 2 Input data Beginner’s guide to using the DESeq2 package 2.1 3 Preparing count matrices As input, the DESeq2 package expects count data as obtained, e. g., from RNA-Seq or another highthroughput sequencing experiment, in the form of a matrix of integer values. The value in the i-th row and the j-th column of the matrix tells how many reads have been mapped to gene i in sample j. Analogously, for other types of assays, the rows of the matrix might correspond e. g. to binding regions (with ChIP-Seq) or peptide sequences (with quantitative mass spectrometry). The count values must be raw counts of sequencing reads. This is important for DESeq2 ’s statistical model to hold, as only the actual counts allow assessing the measurement precision correctly. Hence, please do not supply other quantities, such as (rounded) normalized counts, or counts of covered base pairs – this will only lead to nonsensical results. 2.2 Aligning reads to a reference The computational analysis of an RNA-Seq experiment begins earlier however, with a set of FASTQ files, which contain the bases for each read and their quality scores. These reads must first be aligned to a reference genome or transcriptome. It is important to know if the sequencing experiment was single-end or paired-end, as the alignment software will require the user specify both FASTQ files for a paired-end experiment. A number of software programs exist to align reads to the reference genome, and the development is too rapid for this document to provide a current list. We recommend reading benchmarking papers which discuss the advantages and disadvantages of each software, which include accuracy, ability to align reads over splice junctions, speed, memory footprint, and many other features. We have experience using the TopHat2 spliced alignment software1 [2] in combination with the Bowtie index available at the Illumina iGenomes page2 . For full details on this software and on the iGenomes, users should follow the links to the manual and information provided in the links in the footnotes. For example, the paired-end RNA-Seq reads for the parathyroidSE package were aligned using TopHat2 with 8 threads, with the call: tophat2 -o file_tophat_out -p 8 genome file_1.fastq file_2.fastq samtools sort -n file_tophat_out/accepted_hits.bam _sorted The second line sorts the reads by name rather than by genomic position, which is necessary for counting paired-end reads within Bioconductor. This command uses the SAMtools software3 [3]. The BAM files for a number of sequencing runs can then be used to generate coun matrices, as described in the following section. 1 http://tophat.cbcb.umd.edu/ http://tophat.cbcb.umd.edu/igenomes.html 3 http://samtools.sourceforge.net 2 Beginner’s guide to using the DESeq2 package 2.3 4 Counting reads in genes Once the reads have been aligned, there are a number of tools which can be used to count the number of reads which can be unambiguously assigned to genomic features for each sample. These often take as input BAM or SAM alignment files and a file specifiying the genomic features, e.g. GFF3 or GTF files specifying a gene model. The following tools can be used generate count tables: function summarizeOverlaps htseq-count[4] featureCounts[5] simpleRNASeq[6] package GenomicAlignments (Bioc) HTSeq (Python) Rsubread (Bioc) easyRNASeq (Bioc) output SummarizedExperiment count files count matrix SummarizedExperiment DESeq2 input function DESeqDataSet DESeqDataSetFromHTSeq DESeqDataSetFromMatrix DESeqDataSet In order to produce correct counts, it is important to know if the experiment was strand-specific or not. For example, summarizeOverlaps has the argument ignore.strand, which should be set to TRUE if the experiment was not strand-specific and FALSE if the experiment was strand-specific. Similarly, htseq-count has the argument --stranded yes/no/reverse, where strand-specific experiments should use --stranded yes and where reverse indicates that the positive strand reads should be counted to negative strand features. The following example uses summarizeOverlaps for read counting, while produces a SummarizedExperiment object. This class of object contains a variety of information about an experiment, and will be described in more detail below. We will demonstrate using example BAM files from the parathyroidSE data package. First, we read in the gene model from a GTF file, using makeTranscriptDbFromGFF. Alternatively the makeTranscriptDbFromBiomart function can be used to automatically pull a gene model from Biomart. However, keeping the GTF file on hand has the advantage of bioinformatic reproducibility: the same gene model can be made again, while past versions of gene models might not always be available on Biomart. These GTF files can be downloaded from Ensembl’s FTP site or other gene model repositories. The third line here produces a GRangesList of all the exons grouped by gene. library( "GenomicFeatures" ) hse <- makeTranscriptDbFromGFF( "/path/to/your/genemodel.GTF", format="gtf" ) exonsByGene <- exonsBy( hse, by="gene" ) We specify the BAM files which will be used for counting. fls <- list.files( "/path/to/bam/files", pattern="bam$", full=TRUE ) We indicate in Bioconductor that these fls are BAM files using the BamFileList function. Here we also specify details about how the BAM files should be treated, e.g., only process 100000 reads at a time. library( "Rsamtools" ) bamLst <- BamFileList( fls, yieldSize=100000 ) We call summarizeOverlaps to count the reads. We use the counting mode "Union" which indicates that reads which overlap any portion of exactly one feature are counted. For more ihnforma- Beginner’s guide to using the DESeq2 package 5 colData rowData assay(s) e.g. 'counts' Figure 1: Diagram of SummarizedExperiment Here we show the component parts of a SummarizedExperiment object, and also its subclasses, such as the DESeqDataSet which is explained in the next section. The assay(s) (red block) contains the matrix (or matrices) of summarized values, the rowData (blue block) contains information about the genomic ranges, and the colData (purple block) contains information about the samples or experiments. The highlighted line in each block represents the first row (note that the first row of colData lines up with the first column of the assay. tion on the various counting modes, see the help page for summarizeOverlaps. As this experiment was a paired-end, we specify singleEnd=FALSE. As it was not a strand-specific protocol, we specify ignore.strand=TRUE. fragments=TRUE indicates that we also want to count reads with unmapped pairs. This last argument is only for use with paired-end experiments. library( "GenomicAlignments" ) se <- summarizeOverlaps( exonsByGene, bamLst, mode="Union", singleEnd=FALSE, ignore.strand=TRUE, fragments=TRUE ) This example code above actually only counts a small subset of reads from the original experiment: for 3 samples and for 100 genes. Nevertheless, we can still investigate the resulting SummarizedExperiment by looking at the counts in the assay slot, the phenotypic data about the samples in colData slot (in this case an empty DataFrame), and the data about the genes in the rowData slot. Beginner’s guide to using the DESeq2 package se ## ## ## ## ## ## ## ## ## class: SummarizedExperiment dim: 100 3 exptData(0): assays(1): counts rownames(100): ENSG00000000003 ENSG00000000005 ... ENSG00000005469 ENSG00000005471 rowData metadata column names(0): colnames(3): SRR479052.bam SRR479053.bam SRR479054.bam colData names(0): head( assay(se) ) ## ## ## ## ## ## ## ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457 ENSG00000000460 ENSG00000000938 SRR479052.bam SRR479053.bam SRR479054.bam 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 colSums( assay(se) ) ## SRR479052.bam SRR479053.bam SRR479054.bam ## 31 21 27 colData(se) ## DataFrame with 3 rows and 0 columns rowData(se) ## GRangesList of length 100: ## $ENSG00000000003 ## GRanges with 17 ranges and 2 metadata columns: ## seqnames ranges strand | exon_id exon_name ##| ## [1] X [99883667, 99884983] | 664095 ENSE00001459322 ## [2] X [99885756, 99885863] | 664096 ENSE00000868868 ## [3] X [99887482, 99887565] | 664097 ENSE00000401072 ## [4] X [99887538, 99887565] | 664098 ENSE00001849132 ## [5] X [99888402, 99888536] | 664099 ENSE00003554016 ## ... ... ... ... ... ... ... ## [13] X [99890555, 99890743] | 664106 ENSE00003512331 ## [14] X [99891188, 99891686] | 664108 ENSE00001886883 ## [15] X [99891605, 99891803] | 664109 ENSE00001855382 ## [16] X [99891790, 99892101] | 664110 ENSE00001863395 ## [17] X [99894942, 99894988] | 664111 ENSE00001828996 6 Beginner’s guide to using the DESeq2 package ## ## ## ## ## ## ## ... <99 more elements> --seqlengths: 1 249250621 2 ... 243199373 ... 7 LRG_98 18750 LRG_99 13294 Note that the rowData slot is a GRangesList, which contains all the information about the exons for each gene, i.e., for each row of the count table. This SummarizedExperiment object se is then all we need to start our analysis. In the following section we will show how to create the data object which is used in DESeq2 , either using the SummarizedExperiment, or in general, a count table which has been loaded into R. 2.4 2.4.1 Experiment data The DESeqDataSet, column metadata, and the design formula Each Bioconductor software package often has a special class of data object, which contains special slots and requirements. The data object class in DESeq2 is the DESeqDataSet, which is built on top of the SummarizedExperiment. One main differences is that the assay slot is instead accessed using the count accessor, and the values in this matrix must non-negative integers. A second difference is that the DESeqDataSet has an associated “design formula”. The design is specified at the beginning of the analysis, as this will inform many of the DESeq2 functions how to treat the samples in the analysis (one exception is the size factor estimation – adjustment for differing library sizes – which does not depend on the design formula). The design formula tells which variables in the column metadata table (colData) specify the experimental design and how these factors should be used in the analysis. The simplest design formula for differential expression would be ∼ condition, where condition is a column in colData(dds) which specifies which of two (or more groups) the samples belong to. For the parathyroid experiment, we will specify ∼ patient + treatment, which means that we want to test for the effect of treatment (the last factor), controlling for the effect of patient (the first factor). You can use R’s formula notation to express any experimental design that can be described within an ANOVA-like framework. Note that DESeq2 uses the same kind of formula as in base R, e.g., for use by the lm function. If the question of interest is whether a fold change due to treatment is different across groups, for example across patients, “interaction terms” can be included using models such as ∼ patient + treatment + patient:treatment. More complex designs such as these are covered in the other DESeq2 vignette. In the following section, we will demonstrate the construction of the DESeqDataSet from two starting points: 1. from a SummarizedExperiment object created by, e.g., summarizeOverlaps in the above example Beginner’s guide to using the DESeq2 package 8 2. more generally, from a count table (i.e. matrix) and a column metadata table which have been loaded into R For a full example of using the HTSeq Python package4 for read counting, please see the pasilla or parathyroid data package. For an example of generating the DESeqDataSet from files produced by htseq-count, please see the other DESeq2 vignette. 2.4.2 Starting from SummarizedExperiment We load a prepared SummarizedExperiment, which was generated using summarizeOverlaps from publicly available data from the article by Felix Haglund et al., “Evidence of a Functional Estrogen Receptor in Parathyroid Adenomas”, J Clin Endocrin Metab, Sep 20125 . Details on the generation of this object can be found in the vignette for the parathyroidSE package. The purpose of the experiment was to investigate the role of the estrogen receptor in parathyroid tumors. The investigators derived primary cultures of parathyroid adenoma cells from 4 patients. These primary cultures were treated with diarylpropionitrile (DPN), an estrogen receptor β agonist, or with 4-hydroxytamoxifen (OHT). RNA was extracted at 24 hours and 48 hours from cultures under treatment and control. The blocked design of the experiment allows for statistical analysis of the treatment effects while controlling for patient-to-patient variation. We first load the DESeq2 package and the data package parathyroidSE , which contains the example data set. library( "DESeq2" ) library( "parathyroidSE" ) The data command loads a preconstructed data object: data( "parathyroidGenesSE" ) se <- parathyroidGenesSE colnames(se) <- se$run Supposing we have constructed a SummarizedExperiment using one of the methods described in the previous section, we now ensure that the object contains the necessary information about the samples, i.e., a table with metadata on the count table’s columns stored in the colData slot: colData(se)[1:5,1:4] ## ## ## ## ## ## ## DataFrame with 5 rows and 4 columns run experiment patient treatment SRR479052 SRR479052 SRX140503 1 Control SRR479053 SRR479053 SRX140504 1 Control SRR479054 SRR479054 SRX140505 1 DPN SRR479055 SRR479055 SRX140506 1 DPN 4 5 described in [4] http://www.ncbi.nlm.nih.gov/pubmed/23024189 Beginner’s guide to using the DESeq2 package ## SRR479056 SRR479056 SRX140507 9 1 OHT This object does, because it was prepared so, as can be seen in the parathyroidSE vignette. However, users will most likely have to add pertinent sample/phenotypic information for the experiment at this stage. We highly recommend keeping this information in a comma-separated value (CSV) or tabseparated value (TSV) file, which can be exported from an Excel spreadsheet. The advantage of this over typing out the characters into an R script, is that while scrolling through a script, accidentally typed spaces or characters could lead to changes to the sample phenotypic information, leading to spurious results. Suppose we have a CSV file which contains such data, we could read this file in using the base R read.csv function: sampleInfo <- read.csv( "/path/to/file.CSV" ) Here we show a toy example of such a table. Note that the order is not the same as in the SummarizedExperiment. Here instead of run, most users will have the filename of the BAM files used for counting. We convert the sampleInfo object into a DataFrame which is the format of the colData. head( sampleInfo ) ## ## ## ## ## ## ## 1 2 3 4 5 6 run SRR479078 SRR479077 SRR479076 SRR479075 SRR479074 SRR479073 pheno pheno1 pheno2 pheno1 pheno2 pheno1 pheno2 head( colnames(se) ) ## [1] "SRR479052" "SRR479053" "SRR479054" "SRR479055" "SRR479056" "SRR479057" sampleInfo <- DataFrame( sampleInfo ) We create an index which will put them in the same order: the SummarizedExperiment comes first because this is the sample order we want to achieve. We then check to see that we have lined them up correctly, and then we can add the new data to the existing colData. seIdx <- match(colnames(se), sampleInfo$run) head( cbind( colData(se)[ , 1:3 ], sampleInfo[ seIdx, ] ) ) ## ## ## ## ## ## ## ## DataFrame with 6 rows and 5 columns run experiment patient SRR479052 SRR479052 SRX140503 1 SRR479053 SRR479053 SRX140504 1 SRR479054 SRR479054 SRX140505 1 SRR479055 SRR479055 SRX140506 1 SRR479056 SRR479056 SRX140507 1 run.1 pheno SRR479052 pheno1 SRR479053 pheno2 SRR479054 pheno1 SRR479055 pheno2 SRR479056 pheno1 Beginner’s guide to using the DESeq2 package ## SRR479057 SRR479057 SRX140508 10 1 SRR479057 pheno2 colData(se) <- cbind( colData(se), sampleInfo[ seIdx, ] ) The following line builds the DESeqDataSet from a SummarizedExperiment se and specifying a design formula, as described in the previous section. The names of variables used in the design formula must be the names of columns in the colData of se. ddsFull <- DESeqDataSet( se, design = ~ patient + treatment ) 2.4.3 Starting from count tables In the following section, we will show how to build an DESeqDataSet from a count table and a table of sample information. While the previous section would be used to contruct a DESeqDataSet from a DESeqDataSet, here we first extract the individual object (count table and sample info) from a SummarizedExperiment in order to build it back up into a new object for demonstration purposes. In practice, the count table would either be read in from a file or perhaps generated by an R function like featureCounts from the Rsubread package. The information in a SummarizedExperiment object can be accessed with accessor functions. For example, to see the actual data, i.e., here, the read counts, we use the assay function. (The head function restricts the output to the first few lines.) countdata <- assay( parathyroidGenesSE ) head( countdata ) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457 ENSG00000000460 ENSG00000000938 ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457 ENSG00000000460 ENSG00000000938 ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457 ENSG00000000460 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] 792 1064 444 953 519 855 413 365 278 1173 463 316 4 1 2 3 3 1 0 1 0 0 0 0 294 282 164 263 179 217 277 204 189 601 257 183 156 184 93 145 75 122 228 171 116 422 182 122 396 207 210 212 221 173 611 199 426 1391 286 417 3 8 2 5 0 4 13 22 3 38 13 10 [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] 987 424 305 391 586 714 957 346 433 402 0 0 0 0 0 0 1 0 0 0 588 275 263 281 406 568 764 288 259 250 441 211 131 115 196 266 347 133 168 148 1452 238 188 102 389 294 778 162 85 339 26 13 7 3 10 18 15 7 8 7 [,23] [,24] [,25] [,26] [,27] 277 511 366 271 492 0 0 0 0 0 147 271 227 197 363 83 184 136 118 195 75 154 314 117 233 Beginner’s guide to using the DESeq2 package ## ENSG00000000938 5 13 8 11 7 8 In this count table, each row represents an Ensembl gene, each column a sequenced RNA library, and the values give the raw numbers of sequencing reads that were mapped to the respective gene in each library. We also have metadata on each of the samples (the “columns” of the count table): coldata <- colData( parathyroidGenesSE ) rownames( coldata ) <- coldata$run colnames( countdata ) <- coldata$run head( coldata[ , c( "patient", "treatment", "time" ) ] ) ## ## ## ## ## ## ## ## ## DataFrame with 6 rows and 3 columns patient treatment time SRR479052 1 Control 24h SRR479053 1 Control 48h SRR479054 1 DPN 24h SRR479055 1 DPN 48h SRR479056 1 OHT 24h SRR479057 1 OHT 48h We now have all the ingredients to prepare our data object in a form that is suitable for analysis, namely: countdata: a table with the read counts coldata: a table with metadata on the count table’s columns To now construct the data object from the matrix of counts and the metadata table, we use: ddsFullCountTable <- DESeqDataSetFromMatrix( countData = countdata, colData = coldata, design = ~ patient + treatment) ddsFullCountTable ## ## ## ## ## ## ## ## class: DESeqDataSet dim: 63193 27 exptData(0): assays(1): counts rownames(63193): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99 rowData metadata column names(0): colnames(27): SRR479052 SRR479053 ... SRR479077 SRR479078 colData names(8): run experiment ... study sample We will continue with the object generated from the SummarizedExperiment section. Beginner’s guide to using the DESeq2 package 2.5 12 Collapsing technical replicates There are a number of samples which were sequenced in multiple runs. For example, sample SRS308873 was sequenced twice. To see, we list the respective columns of the colData. (The use of as.data.frame forces R to show us the full list, not just the beginning and the end as before.) as.data.frame( colData( ddsFull )[ ,c("sample","patient","treatment","time") ] ) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## SRR479052 SRR479053 SRR479054 SRR479055 SRR479056 SRR479057 SRR479058 SRR479059 SRR479060 SRR479061 SRR479062 SRR479063 SRR479064 SRR479065 SRR479066 SRR479067 SRR479068 SRR479069 SRR479070 SRR479071 SRR479072 SRR479073 SRR479074 SRR479075 SRR479076 SRR479077 SRR479078 sample patient treatment time SRS308865 1 Control 24h SRS308866 1 Control 48h SRS308867 1 DPN 24h SRS308868 1 DPN 48h SRS308869 1 OHT 24h SRS308870 1 OHT 48h SRS308871 2 Control 24h SRS308872 2 Control 48h SRS308873 2 DPN 24h SRS308873 2 DPN 24h SRS308874 2 DPN 48h SRS308875 2 OHT 24h SRS308875 2 OHT 24h SRS308876 2 OHT 48h SRS308877 3 Control 24h SRS308878 3 Control 48h SRS308879 3 DPN 24h SRS308880 3 DPN 48h SRS308881 3 OHT 24h SRS308882 3 OHT 48h SRS308883 4 Control 48h SRS308884 4 DPN 24h SRS308885 4 DPN 48h SRS308885 4 DPN 48h SRS308886 4 OHT 24h SRS308887 4 OHT 48h SRS308887 4 OHT 48h We recommend to first add together technical replicates (i.e., libraries derived from the same samples), such that we have one column per sample. We have implemented a convenience function for this, which can take am object, either SummarizedExperiment or DESeqDataSet, and a grouping factor, in this case the sample name, and return the object with the counts summed up for each unique sample. This will also rename the columns of the object, such that they match the unique names which were used in the grouping factor. Optionally, we can provide a third argument, run, which can be used to paste together the names of the runs which were collapsed to create the new object. Note that dds$variable is equivalent to colData(dds)$variable. Beginner’s guide to using the DESeq2 package 13 ddsCollapsed <- collapseReplicates( ddsFull, groupby = ddsFull$sample, run = ddsFull$run ) head( as.data.frame( colData(ddsCollapsed)[ ,c("sample","runsCollapsed") ] ), 12 ) ## ## ## ## ## ## ## ## ## ## ## ## ## SRS308865 SRS308866 SRS308867 SRS308868 SRS308869 SRS308870 SRS308871 SRS308872 SRS308873 SRS308874 SRS308875 SRS308876 sample runsCollapsed SRS308865 SRR479052 SRS308866 SRR479053 SRS308867 SRR479054 SRS308868 SRR479055 SRS308869 SRR479056 SRS308870 SRR479057 SRS308871 SRR479058 SRS308872 SRR479059 SRS308873 SRR479060,SRR479061 SRS308874 SRR479062 SRS308875 SRR479063,SRR479064 SRS308876 SRR479065 We can confirm that the counts for the new object are equal to the summed up counts of the columns that had the same value for the grouping factor: original <- rowSums( counts(ddsFull)[ , ddsFull$sample == "SRS308873" ] ) all( original == counts(ddsCollapsed)[ ,"SRS308873" ] ) ## [1] TRUE 3 Running the DESeq2 pipeline Here we will analyze a subset of the samples, namely those taken after 48 hours, with either control, DPN or OHT treatment, taking into account the multifactor design. 3.1 Preparing the data object for the analysis of interest First we subset the relevant columns from the full dataset: dds <- ddsCollapsed[ , ddsCollapsed$time == "48h" ] Sometimes it is necessary to drop levels of the factors, in case that all the samples for one or more levels of a factor in the design have been removed. If time were included in the design formula, the following code could be used to take care of dropped levels in this column. dds$time <- droplevels( dds$time ) Beginner’s guide to using the DESeq2 package 14 It will be convenient to make sure that Control is the first level in the treatment factor, so that the default log2 fold changes are calculated as treatment over control and not the other way around. The function relevel achieves this: dds$treatment <- relevel( dds$treatment, "Control" ) A quick check whether we now have the right samples: as.data.frame( colData(dds) ) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## SRS308866 SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 SRS308878 SRS308880 SRS308882 SRS308883 SRS308885 SRS308887 SRS308866 SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 SRS308878 SRS308880 SRS308882 SRS308883 SRS308885 SRS308887 3.2 run SRR479053 SRR479055 SRR479057 SRR479059 SRR479062 SRR479065 SRR479067 SRR479069 SRR479071 SRR479072 SRR479074 SRR479077 sample SRS308866 SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 SRS308878 SRS308880 SRS308882 SRS308883 SRS308885 SRS308887 experiment patient treatment time submission study SRX140504 1 Control 48h SRA051611 SRP012167 SRX140506 1 DPN 48h SRA051611 SRP012167 SRX140508 1 OHT 48h SRA051611 SRP012167 SRX140510 2 Control 48h SRA051611 SRP012167 SRX140512 2 DPN 48h SRA051611 SRP012167 SRX140514 2 OHT 48h SRA051611 SRP012167 SRX140516 3 Control 48h SRA051611 SRP012167 SRX140518 3 DPN 48h SRA051611 SRP012167 SRX140520 3 OHT 48h SRA051611 SRP012167 SRX140521 4 Control 48h SRA051611 SRP012167 SRX140523 4 DPN 48h SRA051611 SRP012167 SRX140525 4 OHT 48h SRA051611 SRP012167 run.1 runsCollapsed SRR479053 SRR479053 SRR479055 SRR479055 SRR479057 SRR479057 SRR479059 SRR479059 SRR479062 SRR479062 SRR479065 SRR479065 SRR479067 SRR479067 SRR479069 SRR479069 SRR479071 SRR479071 SRR479072 SRR479072 SRR479074 SRR479074,SRR479075 SRR479077 SRR479077,SRR479078 Running the pipeline Finally, we are ready to run the differential expression pipeline. With the data object prepared, the DESeq2 analysis can now be run with a single call to the function DESeq: dds <- DESeq(dds) This function will print out a message for the various steps it performs. These are described in more detail in the manual page for DESeq, which can be accessed by typing ?DESeq. Briefly these are: the Beginner’s guide to using the DESeq2 package 15 estimation of size factors (which control for differences in the library size of the sequencing experiments), the estimation of dispersion for each gene, and fitting a generalized linear model. A DESeqDataSet is returned which contains all the fitted information within it, and the following section describes how to extract out results tables of interest from this object. 3.3 Inspecting the results table Calling results without any arguments will extract the estimated log2 fold changes and p values for the last variable in the design formula. If there are more than 2 levels for this variable – as is the case in this analysis – results will extract the results table for a comparison of the last level over the first level. The following section describes how to extract other comparisons. res <- results( dds ) res ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## log2 fold change (MAP): treatment OHT vs Control Wald test p-value: treatment OHT vs Control DataFrame with 4000 rows and 6 columns baseMean log2FoldChange lfcSE stat pvalue ENSG00000000003 616.390 -0.04917 0.0868 -0.5667 0.57094 ENSG00000000005 0.553 -0.08167 0.1658 -0.4927 0.62225 ENSG00000000419 305.400 0.10779 0.0945 1.1405 0.25410 ENSG00000000457 184.341 0.00189 0.1196 0.0158 0.98742 ENSG00000000460 208.305 0.44504 0.1371 3.2456 0.00117 ... ... ... ... ... ... ENSG00000112851 1071.1 -0.04915 0.0628 -0.7827 0.434 ENSG00000112852 11.8 0.24846 0.3184 0.7803 0.435 ENSG00000112855 257.8 0.03485 0.1019 0.3421 0.732 ENSG00000112874 311.4 0.00817 0.1058 0.0772 0.938 ENSG00000112877 44.4 -0.01252 0.2085 -0.0600 0.952 padj ENSG00000000003 0.9984 ENSG00000000005 NA ENSG00000000419 0.9894 ENSG00000000457 0.9984 ENSG00000000460 0.0995 ... ... ENSG00000112851 0.998 ENSG00000112852 NA ENSG00000112855 0.998 ENSG00000112874 0.998 ENSG00000112877 NA Beginner’s guide to using the DESeq2 package 16 As res is a DataFrame object, it carries metadata with information on the meaning of the columns: mcols(res, use.names=TRUE) ## ## ## ## ## ## ## ## ## DataFrame with 6 rows and 2 columns type description baseMean intermediate the base mean over all rows log2FoldChange results log2 fold change (MAP): treatment OHT vs Control lfcSE results standard error: treatment OHT vs Control stat results Wald statistic: treatment OHT vs Control pvalue results Wald test p-value: treatment OHT vs Control padj results BH adjusted p-values The first column, baseMean, is a just the average of the normalized count values, dividing by size factors, taken over all samples. The remaining four columns refer to a specific contrast, namely the comparison of the levels DPN versus Control of the factor variable treatment. See the help page for results (by typing ?results) for information on how to obtain other contrasts. The column log2FoldChange is the effect size estimate. It tells us how much the gene’s expression seems to have changed due to treatment with DPN in comparison to control. This value is reported on a logarithmic scale to base 2: for example, a log2 fold change of 1.5 means that the gene’s expression is increased by a multiplicative factor of 21.5 ≈ 2.82. Of course, this estimate has an uncertainty associated with it, which is available in the column lfcSE, the standard error estimate for the log2 fold change estimate. We can also express the uncertainty of a particular effect size estimate as the result of a statistical test. The purpose of a test for differential expression is to test whether the data provides sufficient evidence to conclude that this value is really different from zero. DESeq2 performs for each gene a hypothesis test to see whether evidence is sufficient to decide against the null hypothesis that there is no effect of the treatment on the gene and that the observed difference between treatment and control was merely caused by experimental variability (i. e., the type of variability that you can just as well expect between different samples in the same treatment group). As usual in statistics, the result of this test is reported as a p value, and it is found in the column pvalue. (Remember that a p value indicates the probability that a fold change as strong as the observed one, or even stronger, would be seen under the situation described by the null hypothesis.) We note that a subset of the p values in res are NA (“not available”). This is DESeq’s way of reporting that all counts for this gene were zero, and hence not test was applied. In addition, p values can be assigned NA if the gene was excluded from analysis because it contained an extreme count outlier. For more information, see the outlier detection section of the advanced vignette. 3.4 Other comparisons In general, the results for a comparison of any two levels of a variable can be extracted using the contrast argument to results. The user should specify three values: the name of the variable, the Beginner’s guide to using the DESeq2 package 17 name of the level in the numerator, and the name of the level in the denominator. Here we extract results for the log2 of the fold change of DPN / Control. res <- results( dds, contrast = c("treatment", "DPN", "Control") ) res ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## log2 fold change (MAP): treatment DPN vs Control Wald test p-value: treatment DPN vs Control DataFrame with 4000 rows and 6 columns baseMean log2FoldChange lfcSE stat pvalue ENSG00000000003 616.390 -0.01579 0.0856 -0.1844 0.8537 ENSG00000000005 0.553 -0.00836 0.1703 -0.0491 0.9609 ENSG00000000419 305.400 -0.01647 0.0935 -0.1762 0.8601 ENSG00000000457 184.341 -0.09290 0.1180 -0.7874 0.4310 ENSG00000000460 208.305 0.33561 0.1360 2.4669 0.0136 ... ... ... ... ... ... ENSG00000112851 1071.1 -0.0420 0.062 -0.677 0.498 ENSG00000112852 11.8 0.2125 0.317 0.671 0.502 ENSG00000112855 257.8 -0.0832 0.101 -0.826 0.409 ENSG00000112874 311.4 0.1196 0.104 1.153 0.249 ENSG00000112877 44.4 0.0542 0.204 0.266 0.790 padj ENSG00000000003 0.971 ENSG00000000005 NA ENSG00000000419 0.973 ENSG00000000457 0.879 ENSG00000000460 0.250 ... ... ENSG00000112851 0.892 ENSG00000112852 NA ENSG00000112855 0.872 ENSG00000112874 0.777 ENSG00000112877 NA If results for an interaction term are desired, the name argument of results should be used. Please see the more advanced vignette for more details. 3.5 Multiple testing Novices in high-throughput biology often assume that thresholding these p values at a low value, say 0.01, as is often done in other settings, would be appropriate – but it is not. We briefly explain why: There are 121 genes with a p value below 0.01 among the 4000 genes, for which the test succeeded in reporting a p value: Beginner’s guide to using the DESeq2 package 18 sum( res$pvalue < 0.01, na.rm=TRUE ) ## [1] 121 table( is.na(res$pvalue) ) ## ## FALSE ## 4000 Now, assume for a moment that the null hypothesis is true for all genes, i.e., no gene is affected by the treatment with DPN. Then, by the definition of p value, we expect up to 1% of the genes to have a p value below 0.01. This amounts to 40 genes. If we just considered the list of genes with a p value below 0.01 as differentially expressed, this list should therefore be expected to contain up to 40/121 = 33% false positives! DESeq2 uses the so-called Benjamini-Hochberg (BH) adjustment; in brief, this method calculates for each gene an adjusted p value which answers the following question: if one called significant all genes with a p value less than or equal to this gene’s p value threshold, what would be the fraction of false positives (the false discovery rate, FDR) among them (in the sense of the calculation outlined above)? These values, called the BH-adjusted p values, are given in the column padj of the results object. Hence, if we consider a fraction of 10% false positives acceptable, we can consider all genes with an adjusted p value below 10%=0.1 as significant. How many such genes are there? sum( res$padj < 0.1, na.rm=TRUE ) ## [1] 73 We subset the results table to these genes and then sort it by the log2 fold change estimate to get the significant genes with the strongest down-regulation resSig <- res[ which(res$padj < 0.1 ), ] head( resSig[ order( resSig$log2FoldChange ), ] ) ## ## ## ## ## ## ## ## ## ## ## ## ## ## log2 fold change (MAP): treatment DPN vs Control Wald test p-value: treatment DPN vs Control DataFrame with 6 rows and 6 columns baseMean log2FoldChange lfcSE stat pvalue ENSG00000091137 1150 -0.642 0.100 -6.39 1.65e-10 ENSG00000041982 1384 -0.639 0.173 -3.69 2.24e-04 ENSG00000105655 5217 -0.546 0.182 -3.00 2.70e-03 ENSG00000035928 2180 -0.543 0.153 -3.55 3.87e-04 ENSG00000111799 822 -0.527 0.108 -4.89 1.03e-06 ENSG00000107518 198 -0.481 0.143 -3.37 7.56e-04 padj ENSG00000091137 1.32e-07 Beginner’s guide to using the DESeq2 package ## ## ## ## ## ENSG00000041982 ENSG00000105655 ENSG00000035928 ENSG00000111799 ENSG00000107518 19 1.99e-02 9.25e-02 3.09e-02 2.76e-04 4.32e-02 and with the strongest upregulation tail( resSig[ order( resSig$log2FoldChange ), ] ) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## log2 fold change (MAP): treatment DPN vs Control Wald test p-value: treatment DPN vs Control DataFrame with 6 rows and 6 columns baseMean log2FoldChange lfcSE stat pvalue ENSG00000044574 4691 0.482 0.0642 7.51 5.87e-14 ENSG00000103942 238 0.486 0.1404 3.46 5.36e-04 ENSG00000070669 284 0.520 0.1521 3.42 6.31e-04 ENSG00000103257 169 0.781 0.1544 5.06 4.22e-07 ENSG00000101255 256 0.837 0.1590 5.27 1.40e-07 ENSG00000092621 561 0.873 0.1203 7.26 3.99e-13 padj ENSG00000044574 1.41e-10 ENSG00000103942 4.02e-02 ENSG00000070669 4.21e-02 ENSG00000103257 1.26e-04 ENSG00000101255 4.79e-05 ENSG00000092621 4.79e-10 3.6 Diagnostic plots A so-called MA plot provides a useful overview for an experiment with a two-group comparison: plotMA( res, ylim = c(-1, 1) ) The plot (Fig. 2) represents each gene with a dot. The x axis is the average expression over all samples, the y axis the log2 fold change between treatment and control. Genes with an adjusted p value below a threshold (here 0.1, the default) are shown in red. This plot demonstrates that only genes with a large average normalized count contain sufficient information to yield a significant call. Also note DESeq2 ’s shrinkage estimation of log fold changes (LFCs): When count values are too low to allow an accurate estimate of the LFC, the value is “shrunken” towards zero to avoid that these values, which otherwise would frequently be unrealistically large, dominate the top-ranked log fold changes. Beginner’s guide to using the DESeq2 package 20 Figure 2: MA-plot The MA-plot shows the log2 fold changes from the treatment over the mean of normalized counts, i.e. the average of counts normalized by size factor. The DESeq2 package incorporates a prior on log2 fold changes, resulting in moderated estimates from genes with low counts and highly variable counts, as can be seen by the narrowing of spread of points on the left side of the plot. Whether a gene is called significant depends not only on its LFC but also on its within-group variability, which DESeq2 quantifies as the dispersion. For strongly expressed genes, the dispersion can be understood as a squared coefficient √ of variation: a dispersion value of 0.01 means that the gene’s expression tends to differ by typically 0.01 = 10% between samples of the same treatment group. For weak genes, the Poisson noise is an additional source of noise, which is added to the dispersion. The function plotDispEsts visualizes DESeq2 ’s dispersion estimates: plotDispEsts( dds, ylim = c(1e-6, 1e1) ) The black points are the dispersion estimates for each gene as obtained by considering the information from each gene separately. Unless one has many samples, these values fluctuate strongly around their true values. Therefore, we fit the red trend line, which shows the dispersions’ dependence on the mean, and then shrink each gene’s estimate towards the red line to obtain the final estimates (blue points) that are then used in the hypothesis test. The blue circles above the main “cloud” of points are genes which have high gene-wise dispersion estimates which are labelled as dispersion outliers. These estimates are therefore not shrunk toward the fitted trend line. Another useful diagnostic plot is the histogram of the p values (Fig. 4). Beginner’s guide to using the DESeq2 package 21 Figure 3: Plot of dispersion estimates See text for details 150 0 50 Frequency 250 350 Histogram of res$pvalue 0.0 0.2 0.4 0.6 0.8 1.0 res$pvalue Figure 4: Histogram of the p values returned by the test for differential expression 0.02 0.04 0.06 0.08 22 0.00 ratio of small $p$ values Beginner’s guide to using the DESeq2 package ~0 ~3 ~38 ~131 ~282 ~493 ~860 ~74699 mean normalized count Figure 5: Ratio of small p values for groups of genes binned by mean normalized count hist( res$pvalue, breaks=20, col="grey" ) 4 Independent filtering The MA plot (Figure 2) highlights an important property of RNA-Seq data. For weakly expressed genes, we have no chance of seeing differential expression, because the low read counts suffer from so high Poisson noise that any biological effect is drowned in the uncertainties from the read counting. We can also show this by examining the ratio of small p values (say, less than, 0.01) for genes binned by mean normalized count: # create bins using the quantile function qs <- c( 0, quantile( res$baseMean[res$baseMean > 0], 0:7/7 ) ) # "cut" the genes into the bins bins <- cut( res$baseMean, qs ) # rename the levels of the bins using the middle point levels(bins) <- paste0("~",round(.5*qs[-1] + .5*qs[-length(qs)])) # calculate the ratio of £p£ values less than .01 for each bin ratios <- tapply( res$pvalue, bins, function(p) mean( p < .01, na.rm=TRUE ) ) # plot these ratios barplot(ratios, xlab="mean normalized count", ylab="ratio of small $p$ values") At first sight, there may seem to be little benefit in filtering out these genes. After all, the test found them to be non-significant anyway. However, these genes have an influence on the multiple testing adjustment, whose performance improves if such genes are removed. By removing the weakly-expressed Beginner’s guide to using the DESeq2 package 23 70 ● ● ● 60 ● ● ● ● ● ● ● ● 40 50 ● ● ● ● ● ● 30 number of rejections ● ● ● 0.0 0.2 0.4 0.6 0.8 quantiles of 'baseMean' Figure 6: Independent filtering. DESeq2 automatically determines a threshold, filtering on mean normalized count, which maximizes the number of genes which will have an adjusted p value less than a critical value. genes from the input to the FDR procedure, we can find more genes to be significant among those which we keep, and so improved the power of our test. This approach is known as independent filtering. The DESeq2 software automatically performs independent filtering which maximizes the number of genes which will have adjusted p value less than a critical value (by default, alpha is set to 0.1). This automatic independent filtering is performed by, and can be controlled by, the results function. We can observe how the number of rejections changes for various cutoffs based on mean normalized count. The following optimal threshold and table of possible values is stored as an attribute of the results object. attr(res,"filterThreshold") ## 40% ## 165 plot(attr(res,"filterNumRej"),type="b", xlab="quantiles of 'baseMean'", ylab="number of rejections") The term independent highlights an important caveat. Such filtering is permissible only if the filter criterion is independent of the actual test statistic [7]. Otherwise, the filtering would invalidate the test and consequently the assumptions of the BH procedure. This is why we filtered on the average over all Beginner’s guide to using the DESeq2 package 24 samples: this filter is blind to the assignment of samples to the treatment and control group and hence independent. 4.1 Adding gene names Our result table only uses Ensembl gene IDs, but gene names may be more informative. Bioconductor’s biomaRt package can help with mapping various ID schemes to each other. First, we split up the rownames of the results object, which contain ENSEMBL gene ids, separated by the plus sign, +. The following code then takes the first id for each gene by invoking the open square bracket function "[" and the argument, 1. res$ensembl <- sapply( strsplit( rownames(res), split="\\+" ), "[", 1 ) The following chunk of code uses the ENSEMBL mart, querying with the ENSEMBL gene id and requesting the Entrez gene id and HGNC gene symbol. library( "biomaRt" ) ensembl = useMart( "ensembl", dataset = "hsapiens_gene_ensembl" ) genemap <- getBM( attributes = c("ensembl_gene_id", "entrezgene", "hgnc_symbol"), filters = "ensembl_gene_id", values = res$ensembl, mart = ensembl ) idx <- match( res$ensembl, genemap$ensembl_gene_id ) res$entrez <- genemap$entrezgene[ idx ] res$hgnc_symbol <- genemap$hgnc_symbol[ idx ] Now the results have the desired external gene ids: head(res,4) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## log2 fold change (MAP): treatment DPN vs Control Wald test p-value: treatment DPN vs Control DataFrame with 4 rows and 9 columns baseMean log2FoldChange lfcSE stat pvalue ENSG00000000003 616.390 -0.01579 0.0856 -0.1844 0.854 ENSG00000000005 0.553 -0.00836 0.1703 -0.0491 0.961 ENSG00000000419 305.400 -0.01647 0.0935 -0.1762 0.860 ENSG00000000457 184.341 -0.09290 0.1180 -0.7874 0.431 padj ensembl entrez hgnc_symbol ENSG00000000003 0.971 ENSG00000000003 7105 TSPAN6 ENSG00000000005 NA ENSG00000000005 64102 TNMD ENSG00000000419 0.973 ENSG00000000419 8813 DPM1 ENSG00000000457 0.879 ENSG00000000457 57147 SCYL3 Beginner’s guide to using the DESeq2 package 4.2 25 Exporting results Finally, we note that you can easily save the results table in a CSV file, which you can then load with a spreadsheet program such as Excel: res[1:2,] ## ## ## ## ## ## ## ## ## ## ## log2 fold change (MAP): treatment DPN vs Control Wald test p-value: treatment DPN vs Control DataFrame with 2 rows and 9 columns baseMean log2FoldChange lfcSE stat pvalue ENSG00000000003 616.390 -0.01579 0.0856 -0.1844 0.854 ENSG00000000005 0.553 -0.00836 0.1703 -0.0491 0.961 padj ensembl entrez hgnc_symbol ENSG00000000003 0.971 ENSG00000000003 7105 TSPAN6 ENSG00000000005 NA ENSG00000000005 64102 TNMD write.csv( as.data.frame(res), file="results.csv" ) 5 5.1 Working with rlog-transformed data The rlog transform Many common statistical methods for exploratory analysis of multidimensional data, especially methods for clustering and ordination (e. g., principal-component analysis and the like), work best for (at least approximately) homoskedastic data; this means that the variance of an observable quantity (i.e., here, the expression strength of a gene) does not depend on the mean. In RNA-Seq data, however, variance grows with the mean. For example, if one performs PCA directly on a matrix of normalized read counts, the result typically depends only on the few most strongly expressed genes because they show the largest absolute differences between samples. A simple and often used strategy to avoid this is to take the logarithm of the normalized count values plus a small pseudocount; however, now the genes with low counts tend to dominate the results because, due to the strong Poisson noise inherent to small count values, they show the strongest relative differences between samples. As a solution, DESeq2 offers the regularized-logarithm transformation, or rlog for short. For genes with high counts, the rlog transformation differs not much from an ordinary log2 transformation. For genes with lower counts, however, the values are shrunken towards the genes’ averages across all samples. Using an empirical Bayesian prior in the form of a ridge penality, this is done such that the rlog-transformed data are approximately homoskedastic. Note that the rlog transformation is provided for applications other than differential testing. For differential testing we recommend the DESeq function applied to raw counts, as described earlier in this Beginner’s guide to using the DESeq2 package Figure 7: Scatter plot of sample 2 vs sample 1. Right: Using the rlog transformation. 26 Left: using an ordinary log2 transformation. vignette, which also takes into account the dependence of the variance of counts on the mean value during the dispersion estimation step. The function rlogTransform returns a SummarizedExperiment object which contains the rlog-transformed values in its assay slot: rld <- rlog( dds ) head( assay(rld) ) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457 ENSG00000000460 ENSG00000000938 ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457 ENSG00000000460 ENSG00000000938 SRS308866 SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 9.800 9.778 9.918 9.095 9.163 9.062 -0.922 -0.812 -0.904 -0.893 -0.975 -0.975 8.055 8.080 8.117 8.238 8.297 8.379 7.414 7.255 7.312 7.869 7.739 7.907 7.573 7.704 7.727 8.076 8.287 8.079 3.225 3.108 3.114 3.995 3.571 3.573 SRS308878 SRS308880 SRS308882 SRS308883 SRS308885 SRS308887 8.883 8.752 8.809 9.049 9.073 8.857 -0.975 -0.977 -0.975 -0.975 -0.977 -0.977 8.323 8.313 8.420 8.272 8.157 8.323 7.163 7.296 7.383 7.617 7.490 7.501 7.057 7.433 7.630 6.904 7.347 7.654 3.025 3.356 3.287 3.331 3.435 3.288 To show the effect of the transformation, we plot the first sample against the second, first simply using the log2 function (after adding 1, to avoid taking the log of zero), and then using the rlog-transformed values. Beginner’s guide to using the DESeq2 package 27 0 Count Color Key and Histogram 0 40 Value 12 11 10 7 8 9 5 6 4 2 1 3 OHT−1 Control−1 DPN−1 Control−2 OHT−2 DPN−2 OHT−3 DPN−3 Control−3 Control−4 DPN−4 OHT−4 Figure 8: Heatmap of Euclidean sample distances after rlog transformation. par( mfrow = c( 1, 2 ) ) plot( log2( 1+counts(dds, normalized=TRUE)[, 1:2] ), col="#00000020", pch=20, cex=0.3 ) plot( assay(rld)[, 1:2], col="#00000020", pch=20, cex=0.3 ) Note that, in order to make it easier to see where several points are plotted on top of each other, we set the plotting color to a semi-transparent black (encoded as #00000020) and changed the points to solid disks (pch=20) with reduced size (cex=0.3)6 . In Figure 7, we can see how genes with low counts seem to be excessively variable on the ordinary logarithmic scale, while the rlog transform compresses differences for genes for which the data cannot provide good information anyway. 5.2 Sample distances A useful first step in an RNA-Seq analysis is often to assess overall similarity between samples: Which samples are similar to each other, which are different? Does this fit to the expectation from the experiment’s design? We use the R function dist to calculate the Euclidean distance between samples. To avoid that the distance measure is dominated by a few highly variable genes, and have a roughly equal contribution 6 The function heatscatter from the package LSD offers a colorful alternative. Beginner’s guide to using the DESeq2 package 28 from all genes, we use it on the rlog-transformed data: sampleDists <- dist( t( assay(rld) ) ) sampleDists ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 SRS308878 SRS308880 SRS308882 SRS308883 SRS308885 SRS308887 SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 SRS308878 SRS308880 SRS308882 SRS308883 SRS308885 SRS308887 SRS308866 SRS308868 SRS308870 SRS308872 SRS308874 SRS308876 SRS308878 10.55 8.97 10.98 34.38 35.82 35.34 34.64 35.29 35.21 10.02 33.99 35.48 34.90 9.04 9.96 47.60 49.41 48.57 48.35 49.78 48.53 47.84 49.23 48.64 49.04 50.14 49.32 9.69 47.71 49.12 48.48 49.25 50.32 49.36 11.15 40.27 42.28 41.69 39.70 41.14 39.63 33.32 38.97 40.28 40.10 38.63 39.50 38.75 33.71 40.02 41.56 41.06 40.14 41.27 39.97 33.51 SRS308880 SRS308882 SRS308883 SRS308885 8.21 33.56 32.98 32.79 33.80 33.42 32.58 10.35 11.91 10.44 Note the use of the function t to transpose the data matrix. We need this because dist calculates distances between data rows and our samples constitute the columns. We visualize the distances in a heatmap, using the function heatmap.2 from the gplots package. sampleDistMatrix <- as.matrix( sampleDists ) rownames(sampleDistMatrix) <- paste( rld$treatment, rld$patient, sep="-" ) colnames(sampleDistMatrix) <- NULL library( "gplots" ) library( "RColorBrewer" ) colours = colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) heatmap.2( sampleDistMatrix, trace="none", col=colours) Note that we have changed the row names of the distance matrix to contain treatment type and patient number instead of sample ID, so that we have all this information in view when looking at the heatmap (Fig. 8). Beginner’s guide to using the DESeq2 package 29 1 : Control 1 : DPN 1 : OHT 2 : Control 2 : DPN 2 : OHT 3 : Control 3 : DPN 3 : OHT 4 : Control 4 : DPN 4 : OHT 10 ● ● ● ● ● PC2 5 0 ● ● −5 −10 −15 ● ● −10 0 10 20 PC1 Figure 9: Principal components analysis (PCA) of samples after rlog transformation. Another way to visualize sample-to-sample distances is a principal-components analysis (PCA). In this ordination method, the data points (i.e., here, the samples) are projected onto the 2D plane such that they spread out optimally (Fig. 9). If we only had two treatments, the plotPCA will automatically use a paired color palette for each combination of the levels of patient and treatment. As we have three treatments, we supply a vector which specifies three shades of red, green, blue and purple for each patient. ramp <- 1:3/3 cols <- c(rgb(ramp, 0, 0), rgb(0, ramp, 0), rgb(0, 0, ramp), rgb(ramp, 0, ramp)) print( plotPCA( rld, intgroup = c( "patient", "treatment"), col=cols ) ) Here, we have used the function plotPCA which comes with DESeq2. The two terms specified as intgroup are column names from our sample data; they tell the function to use them to choose colours. From both visualizations, we see that the differences between patients is much larger than the difference between treatment and control samples of the same patient. This shows why it was important to account for this paired design (“paired”, because each treated sample is paired with one control sample from the same patient). We did so by using the design formula ∼ patient + treatment when setting up the data object in the beginning. Had we used an un-paired analysis, by specifying only ∼ treatment, we Beginner’s guide to using the DESeq2 package 30 Figure 10: Heatmap with gene clustering. would not have found many hits, because then, the patient-to-patient differences would have drowned out any treatment effects. Here, we have performed this sample distance analysis towards the end of our analysis. In practice, however, this is a step suitable to give a first overview on the data. Hence, one will typically carry out this analysis as one of the first steps in an analysis. To this end, you may also find the function arrayQualityMetrics, from the package of the same name, useful. 5.3 Gene clustering In the heatmap of Fig. 8, the dendrogram at the side shows us a hierarchical clustering of the samples. Such a clustering can also be performed for the genes. Since the clustering is only relevant for genes that actually carry signal, one usually carries it out only for a subset of most highly variable genes. Here, for demonstration, let us select the 35 genes with the highest variance across samples: library( "genefilter" ) topVarGenes <- head( order( rowVars( assay(rld) ), decreasing=TRUE ), 35 ) The heatmap becomes more interesting if we do not look at absolute expression strength but rather at the amount by which each gene deviates in a specific sample from the gene’s average across all samples. Hence, we center and scale each genes’ values across samples, and plot a heatmap. Beginner’s guide to using the DESeq2 package 31 heatmap.2( assay(rld)[ topVarGenes, ], scale="row", trace="none", dendrogram="column", col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) We can now see (Fig. 10) blocks of genes which covary across patients. Often, such a heatmap is insightful, even though here, seeing these variations across patients is of limited value because we are rather interested in the effects between the treatments from each patient. References [1] Wolfgang Huber Michael I Love and Simon Anders. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. bioRxiv preprint, 2014. URL: http://dx.doi.org/ 10.1101/002832. [2] Daehwan Kim, Geo Pertea, Cole Trapnell, Harold Pimentel, Ryan Kelley, and Steven Salzberg. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14(4):R36+, 2013. URL: http://dx.doi.org/10.1186/ gb-2013-14-4-r36, doi:10.1186/gb-2013-14-4-r36. [3] Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin, and 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16):2078–2079, 2009. URL: http://bioinformatics.oxfordjournals.org/content/25/ 16/2078.abstract, arXiv:http://bioinformatics.oxfordjournals.org/content/25/16/ 2078.full.pdf+html, doi:10.1093/bioinformatics/btp352. [4] Paul Theodor Pyl Simon Anders and Wolfgang Huber. HTSeq - A Python framework to work with high-throughput sequencing data. bioRxiv preprint, 2014. URL: http://dx.doi.org/10.1101/ 002824. [5] Y. Liao, G. K. Smyth, and W. Shi. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, Nov 2013. [6] N. Delhomme, I. Padioleau, E. E. Furlong, and L. M. Steinmetz. easyRNASeq: a bioconductor package for processing RNA-Seq data. Bioinformatics, 28(19):2532–2533, Oct 2012. [7] Richard Bourgon, Robert Gentleman, and Wolfgang Huber. Independent filtering increases detection power for high-throughput experiments. PNAS, 107(21):9546–9551, 2010. URL: http://www. pnas.org/content/107/21/9546.long. 6 Session Info As last part of this document, we call the function sessionInfo, which reports the version numbers of R and all the packages used in this session. It is good practice to always keep such a record as it will Beginner’s guide to using the DESeq2 package 32 help to trace down what has happened in case that an R script ceases to work because a package has been changed in a newer version. The session information should also always be included in any emails to the Bioconductor mailing list. R version 3.1.0 (2014-04-10), x86_64-unknown-linux-gnu Locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=C, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8, LC_IDENTIFICATION=C Base packages: base, datasets, grDevices, graphics, methods, parallel, stats, utils Other packages: AnnotationDbi 1.26.0, BSgenome 1.32.0, Biobase 2.24.0, BiocGenerics 0.10.0, Biostrings 2.32.0, DESeq2 1.4.5, GenomeInfoDb 1.0.2, GenomicAlignments 1.0.1, GenomicFeatures 1.16.0, GenomicRanges 1.16.3, IRanges 1.22.6, RColorBrewer 1.0-5, Rcpp 0.11.1, RcppArmadillo 0.4.300.0, Rsamtools 1.16.0, XVector 0.4.0, biomaRt 2.20.0, genefilter 1.46.1, gplots 2.13.0, knitr 1.5, parathyroidSE 1.2.0, pasilla 0.4.0, vsn 3.32.0 Loaded via a namespace (and not attached): BBmisc 1.6, BatchJobs 1.2, BiocInstaller 1.14.2, BiocParallel 0.6.0, BiocStyle 1.2.0, DBI 0.2-7, DESeq 1.16.0, KernSmooth 2.23-12, RCurl 1.95-4.1, RSQLite 0.11.4, XML 3.98-1.1, affy 1.42.2, affyio 1.32.0, annotate 1.42.0, bitops 1.0-6, brew 1.0-6, caTools 1.17, codetools 0.2-8, digest 0.6.4, evaluate 0.5.5, fail 1.2, foreach 1.4.2, formatR 0.10, gdata 2.13.3, geneplotter 1.42.0, grid 3.1.0, gtools 3.4.0, highr 0.3, iterators 1.0.7, lattice 0.20-29, limma 3.20.1, locfit 1.5-9.1, plyr 1.8.1, preprocessCore 1.26.1, rtracklayer 1.24.0, sendmailR 1.1-2, splines 3.1.0, stats4 3.1.0, stringr 0.6.2, survival 2.37-7, tools 3.1.0, xtable 1.7-3, zlibbioc 1.10.0
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File Type : PDF File Type Extension : pdf MIME Type : application/pdf PDF Version : 1.4 Linearized : No Page Count : 32 Page Mode : UseOutlines Author : Michael Love1*, Simon Anders2, Wolfgang Huber2 , [1em] 1 Department of Biostatistics, Dana Farber Cancer Institute and , Harvard School of Public Health, Boston, US; , 2 European Molecular Biology Laboratory (EMBL), Heidelberg, Germany , *michaelisaiahlove (at) gmail.com Title : Beginner's guide to using the DESeq2 package Subject : Creator : LaTeX with hyperref package Producer : pdfTeX-1.40.10 Create Date : 2014:05:13 00:08:37-07:00 Modify Date : 2014:05:13 00:08:37-07:00 Trapped : False PTEX Fullbanner : This is pdfTeX, Version 3.1415926-1.40.10-2.2 (TeX Live 2009/Debian) kpathsea version 5.0.0EXIF Metadata provided by EXIF.tools