Roswell Park Cancer Institute, Buffalo, NY

5 - 9 October, 2015" output: BiocStyle::html_document: toc: true toc_depth: 2 vignette: > % \VignetteIndexEntry{6. RNA-Seq Differential Expression} % \VignetteEngine{knitr::rmarkdown} --- ```{r style, echo = FALSE, results = 'asis'} BiocStyle::markdown() options(width=100, max.print=1000) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE"))) ``` ```{r setup, echo=FALSE, messages=FALSE, warnings=FALSE} suppressPackageStartupMessages({ library(DESeq2) library(limma) library(airway) library(gplots) library(RColorBrewer) library(ggplot2) library(genefilter) library(org.Hs.eg.db) }) ``` The material in this course requires R version 3.2 and Bioconductor version 3.2 ```{r configure-test} stopifnot( getRversion() >= '3.2' && getRversion() < '3.3', BiocInstaller::biocVersion() == "3.2" ) ``` # Background [Google Doc](https://docs.google.com/document/d/1di3O0mGQwLW80uVXUtdZVEitU-9Gn9hjzHV-Ae6YIcg/edit?usp=sharing) For Review: - [Overall Workflow](S1-RNASeq-Workflow.html) - [Specifying Experimental Design in _R_](S2-RNASeq-Experimental-Design.html) This lab is derived from: [RNA-Seq workflow: gene-level exploratory analysis and differential expression](http://bioconductor.org/help/workflows/rnaseqGene/), by Michael Love, Simon Anders, Wolfgang Huber; modified by Martin Morgan, October 2015. This lab will walk you through an end-to-end RNA-Seq differential expression workflow, using [DESeq2][] along with other _Bioconductor_ packages. The complete work flow starts from the FASTQ files, but we will start after reads have been aligned to a reference genome and reads overlapping known genes have been counted. We will perform exploratory data analysis (EDA), differential gene expression analysis with [DESeq2][], and visually explore the results. A number of other _Bioconductor_ packages are important in statistical inference of differential expression at the gene level, including [Rsubread][], [edgeR][], [limma][], [BaySeq][], and others. # Experimental data The data used in this workflow is an RNA-Seq experiment of airway smooth muscle cells treated with dexamethasone, a synthetic glucocorticoid steroid with anti-inflammatory effects. Glucocorticoids are used, for example, in asthma patients to prevent or reduce inflammation of the airways. In the experiment, four primary human airway smooth muscle cell lines were treated with 1 micromolar dexamethasone for 18 hours. For each of the four cell lines, we have a treated and an untreated sample. The reference for the experiment is: Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. "RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells." PLoS One. 2014 Jun 13;9(6):e99625. PMID: [24926665](http://www.ncbi.nlm.nih.gov/pubmed/24926665). GEO: [GSE52778](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52778). # Preparing count matrices As input, [DESeq2][] package expects count data as obtained, e.g., from RNA-Seq or another high-throughput 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. We will discuss how to summarize data from BAM files to a count table later in ther course. Here we'll 'jump right in' and start with a prepared `SummarizedExperiment`. # Starting from `SummarizedExperiment` We now use R's `data()` command to load a prepared `SummarizedExperiment` that was generated from the publicly available sequencing data files associated with the Himes et al. paper, described above. The steps we used to produce this object were equivalent to those you worked through in the previous sections, except that we used all the reads and all the genes. For more details on the exact steps used to create this object type `vignette("airway")` into your R session. ```{r} library(airway) data("airway") se <- airway ``` 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.) ```{r} head(assay(se)) ``` In this count matrix, 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 matrix). If you've counted reads with some other software, you need to check at this step that the columns of the count matrix correspond to the rows of the column metadata. We can quickly check the millions of fragments which uniquely aligned to the genes. ```{r} colSums(assay(se)) ``` Supposing we have constructed a `SummarizedExperiment` using one of the methods described in the previous section, we now need to make sure that the object contains all the necessary information about the samples, i.e., a table with metadata on the count matrix's columns stored in the `colData` slot: ```{r} colData(se) ``` Here we see that this object already contains an informative `colData` slot -- because we have already prepared it for you, as described in the [airway][] vignette. However, when you work with your own data, you will have to add the pertinent sample / phenotypic information for the experiment at this stage. We highly recommend keeping this information in a comma-separated value (CSV) or tab-separated value (TSV) file, which can be exported from an Excel spreadsheet, and the assign this to the `colData` slot, making sure that the rows correspond to the columns of the `SummarizedExperiment`. We made sure of this correspondence by specifying the BAM files using a column of the sample table. Check out the `rowRanges()` of the summarized experiment; these are the genomic ranges over which counting occurred. ```{r rowRanges`} rowRanges(se) ``` # From `SummarizedExperiment` to `DESeqDataSet` We will use the [DESeq2][] package for assessing differential expression. The package uses an extended version of the `SummarizedExperiment` calass, called `DESeqDataSet`. It's easy to go from a `SummarizedExperiment` to `DESeqDataSet`: ```{r} library("DESeq2") dds <- DESeqDataSet(se, design = ~ cell + dex) ``` # Visually exploring the dataset ### The rlog transformation 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 observed quantity (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 (principal components analysis) 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 on inter-sample differences in the form of a *ridge penalty*, this is done such that the rlog-transformed data are approximately homoskedastic. See the help for `?rlog` for more information and options. Another transformation, the *variance stabilizing transformation* (`vsn()`), is discussed alongside the `rlog()` in the [DESeq2][] vignette. **Note:** 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 later in this workflow, which also takes into account the dependence of the variance of counts on the mean value during the dispersion estimation step. The function `rlog()` returns a `SummarizedExperiment` object which contains the rlog-transformed values in its `assay()` slot: ```{r} rld <- rlog(dds) head(assay(rld)) ``` 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. For the `log2()` method, we need estimate size factors to account for sequencing depth (this is done automatically for the `rlog()` method). ```{r rldplot, fig.width=10, fig.height=5} opar <- par( mfrow = c( 1, 2 ) ) dds <- estimateSizeFactors(dds) plot( log2( 1 + counts(dds, normalized=TRUE)[ , 1:2] ), col=rgb(0,0,0,.2), pch=16, cex=0.3 ) plot( assay(rld)[ , 1:2], col=rgb(0,0,0,.2), pch=16, cex=0.3 ) par(opar) ``` 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 and changed the points to solid circles (`pch=16`) with reduced size (`cex=0.3`). 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. ### 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 from all genes, we use it on the rlog-transformed data: ```{r} sampleDists <- dist( t( assay(rld) ) ) sampleDists ``` 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. ```{r} library("gplots") library("RColorBrewer") ``` We have to provide a hierarchical clustering `hc` to the `heatmap.2()` function based on the sample distances, or else the `heatmap.2()` function would calculate a clustering based on the distances between the rows/columns of the distance matrix. ```{r distheatmap, fig.width=8} sampleDistMatrix <- as.matrix( sampleDists ) rownames(sampleDistMatrix) <- paste( rld$dex, rld$cell, sep="-" ) colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) hc <- hclust(sampleDists) heatmap.2( sampleDistMatrix, Rowv=as.dendrogram(hc), symm=TRUE, trace="none", col=colors, margins=c(2,10), labCol=FALSE ) ``` 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. ### PCA plot 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 in the two directions which explain most of the differences in the data. The x-axis is the direction (or principal component) which separates the data points the most. The amount of the total variance which is contained in the direction is printed in the axis label. ```{r plotpca, fig.width=6, fig.height=4.5} plotPCA(rld, intgroup = c("dex", "cell")) ``` Here, we have used the function `plotPCA()` which comes with [DESeq2][]. The two terms specified by `intgroup` are the interesting groups for labelling the samples; they tell the function to use them to choose colors. We can also build the PCA plot from scratch using `r CRANpkg("ggplot2")`. This is done by asking the `plotPCA()` function to return the data used for plotting rather than building the plot. See the [ggplot2][] [documentation](http://docs.ggplot2.org/current/) for more details. ```{r} (data <- plotPCA(rld, intgroup = c( "dex", "cell"), returnData=TRUE)) percentVar <- round(100 * attr(data, "percentVar")) ``` We can then use this data to build up the plot, specifying that the color of the points should reflect dexamethasone treatment and the shape should reflect the cell line. ```{r} library("ggplot2") ``` ```{r ggplotpca, fig.width=6, fig.height=4.5} qplot(PC1, PC2, color=dex, shape=cell, data=data) + xlab(paste0("PC1: ", percentVar[1], "% variance")) + ylab(paste0("PC2: ", percentVar[2], "% variance")) ``` From both visualizations, we see that the differences between cells are considerable, though not stronger than the differences due to treatment with dexamethasone. This shows why it will be important to account for this in differential testing by using a paired design ("paired", because each dex treated sample is paired with one untreated sample from the *same* cell line). We are already set up for this by using the design formula `~ cell + dex` when setting up the data object in the beginning. # Differential expression analysis It will be convenient to make sure that `untrt` is the first level in the `dex` factor, so that the default log2 fold changes are calculated as treated over untreated (by default R will chose the first alphabetical level, remember: computers don't know what to do unless you tell them). The function `relevel()` achieves this: ```{r} dds$dex <- relevel(dds$dex, "untrt") ``` In addition, if you have at any point subset the columns of the `DESeqDataSet` you should similarly call `droplevels()` on the factors if the subsetting has resulted in some levels having 0 samples. ## 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()`: ```{r} 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 `?DESeq`. Briefly these are: the 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. ## Building 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, `results()` will extract the results table for a comparison of the last level over the first level. ```{r} (res <- results(dds)) ``` As `res` is a `DataFrame` object, it carries metadata with information on the meaning of the columns: ```{r} mcols(res, use.names=TRUE) ``` 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 `trt` level over the `untrt` level for the factor variable `dex`. 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 dexamethasone in comparison to untreated samples. 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 $2^{1.5} \approx 2.82$. 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 can also summarize the results with the following line of code, which reports some additional information. ```{r} summary(res) ``` Note that there are many genes with differential expression due to dexamethasone treatment at the FDR level of 10%. This makes sense, as the smooth muscle cells of the airway are known to react to glucocorticoid steroids. However, there are two ways to be more strict about which set of genes are considered significant: * lower the false discovery rate threshold (the threshold on `padj` in the results table) * raise the log2 fold change threshold from 0 using the `lfcThreshold` argument of `results()`. See the [DESeq2][] vignette for a demonstration of the use of this argument. Sometimes a subset of the *p* values in `res` will be `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 vignette. ## 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 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 one cell line over another: ```{r} results(dds, contrast=c("cell", "N061011", "N61311")) ``` If results for an interaction term are desired, the `name` argument of `results()` should be used. Please see the help for the `results()` function for more details. ## Multiple testing Novices in high-throughput biology often assume that thresholding these *p* values at a low value, say 0.05, as is often done in other settings, would be appropriate -- but it is not. We briefly explain why: There are `r sum(res$pvalue < .05, na.rm=TRUE)` genes with a *p* value below 0.05 among the `r sum(!is.na(res$pvalue))` genes, for which the test succeeded in reporting a *p* value: ```{r} sum(res$pvalue < 0.05, na.rm=TRUE) sum(!is.na(res$pvalue)) ``` Now, assume for a moment that the null hypothesis is true for all genes, i.e., no gene is affected by the treatment with dexamethasone. Then, by the definition of *p* value, we expect up to 5% of the genes to have a *p* value below 0.05. This amounts to `r round(sum(!is.na(res$pvalue)) * .05 )` genes. If we just considered the list of genes with a *p* value below 0.05 as differentially expressed, this list should therefore be expected to contain up to `r round(sum(!is.na(res$pvalue)) * .05)` / `r sum(res$pvalue < .05, na.rm=TRUE)` = `r round(sum(!is.na(res$pvalue))*.05 / sum(res$pvalue < .05, na.rm=TRUE) * 100)`% false positives. [DESeq2][] uses the Benjamini-Hochberg (BH) adjustment as described in the base R *p.adjust* function; 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 `res` 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? ```{r} sum(res$padj < 0.1, na.rm=TRUE) ``` We subset the results table to these genes and then sort it by the log2 fold change estimate to get the significant genes with the strongest down-regulation. ```{r} resSig <- subset(res, padj < 0.1) head(resSig[ order( resSig$log2FoldChange ), ]) ``` ...and with the strongest upregulation. The `order()` function gives the indices in increasing order, so a simple way to ask for decreasing order is to add a `-` sign. Alternatively, you can use the argument `decreasing=TRUE`. ```{r} head(resSig[ order( -resSig$log2FoldChange ), ]) ``` # Diagnostic plots A quick way to visualize the counts for a particular gene is to use the `plotCounts()` function, which takes as arguments the `DESeqDataSet`, a gene name, and the group over which to plot the counts. ```{r plotcounts, fig.width=5, fig.height=5} topGene <- rownames(res)[which.min(res$padj)] data <- plotCounts(dds, gene=topGene, intgroup=c("dex"), returnData=TRUE) ``` We can also make more customizable plots using the `ggplot()` function from the [ggplot2][] package: ```{r ggplotcountsdot, fig.height=5} ggplot(data, aes(x=dex, y=count, fill=dex)) + scale_y_log10() + geom_dotplot(binaxis="y", stackdir="center") ``` An "MA-plot" provides a useful overview for an experiment with a two-group comparison. The log2 fold change for a particular comparison is plotted on the y-axis and the average of the counts normalized by size factor is shown on the x-axis ("M" for minus, because a log ratio is equal to log minus log, and "A" for average). ```{r plotma, eval=FALSE} plotMA(res, ylim=c(-5,5)) ``` Each gene is represented with a dot. Genes with an adjusted $p$ value below a threshold (here 0.1, the default) are shown in red. The [DESeq2][] package incorporates a prior on log2 fold changes, resulting in moderated log2 fold changes 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. This plot demonstrates that only genes with a large average normalized count contain sufficient information to yield a significant call. We can label individual points on the MA plot as well. Here we use the `with()` R function to plot a circle and text for a selected row of the results object. Within the `with()` function, only the `baseMean` and `log2FoldChange` values for the selected rows of `res` are used. ```{r plotma2, eval=FALSE} plotMA(res, ylim=c(-5,5)) with(res[topGene, ], { points(baseMean, log2FoldChange, col="dodgerblue", cex=2, lwd=2) text(baseMean, log2FoldChange, topGene, pos=2, col="dodgerblue") }) ``` 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 $\sqrt{0.01} = 10\%$ between samples of the same treatment group. For weak genes, the Poisson noise is an additional source of noise. The function `plotDispEsts()` visualizes [DESeq2][]'s dispersion estimates: ```{r plotdispests} plotDispEsts(dds) ``` 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. ```{r histpvalue} hist(res$pvalue, breaks=20, col="grey50", border="white") ``` This plot becomes a bit smoother by excluding genes with very small counts: ```{r histpvalue2} hist(res$pvalue[res$baseMean > 1], breaks=20, col="grey50", border="white") ``` # Gene clustering In the sample distance heatmap made previously, the dendrogram at the side shows us a hierarchical clustering of the samples. Such a clustering can also be performed for the genes. Since the clustering is only relevant for genes that actually carry signal, one usually carries it out only for a subset of most highly variable genes. Here, for demonstration, let us select the 35 genes with the highest variance across samples. We will work with the `rlog()` transformed counts: ```{r} library("genefilter") topVarGenes <- head(order(-rowVars(assay(rld))),35) ``` The heatmap becomes more interesting if we do not look at absolute expression strength but rather at the amount by which each gene deviates in a specific sample from the gene's average across all samples. Hence, we center each genes' values across samples, and plot a heatmap. We provide the column side colors to help identify the treated samples (in blue) from the untreated samples (in grey). ```{r genescluster, fig.height=9} colors <- colorRampPalette( rev(brewer.pal(9, "PuOr")) )(255) sidecols <- c("grey","dodgerblue")[ rld$dex ] mat <- assay(rld)[ topVarGenes, ] mat <- mat - rowMeans(mat) colnames(mat) <- paste0(rld$dex,"-",rld$cell) heatmap.2(mat, trace="none", col=colors, ColSideColors=sidecols, labRow=FALSE, mar=c(10,2), scale="row") ``` We can now see blocks of genes which covary across patients. Note that a set of genes at the top of the heatmap are separating the N061011 cell line from the others. At the bottom of the heatmap, we see a set of genes for which the treated samples have higher gene expression. # Independent filtering The MA plot 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: ```{r sensitivityovermean, fig.height=4} # 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$pvalue, bins, function(p) mean(p < .01, na.rm=TRUE)) # plot these ratios barplot(ratios, xlab="mean normalized count", ylab="ratio of small p values") ``` At first sight, there may seem to be little benefit in filtering out these genes. After all, the test found them to be non-significant anyway. However, these genes have an influence on the multiple testing adjustment, whose performance improves if such genes are removed. By removing the weakly-expressed genes from the input to the FDR procedure, we can find more genes to be significant among those which we keep, and so improved the power of our test. This approach is known as *independent filtering*. The term *independent* highlights an important caveat. Such filtering is permissible only if the filter criterion is independent of the actual test statistic. Otherwise, the filtering would invalidate the test and consequently the assumptions of the BH procedure. This is why we filtered on the average over *all* samples: this filter is blind to the assignment of samples to the treatment and control group and hence independent. The independent filtering software used inside [DESeq2][] comes from the `r Biocpkg("genefilter")` package, which contains a reference to a paper describing the statistical foundation for independent filtering. # Annotation: adding gene names Our result table only uses Ensembl gene IDs, but gene names may be more informative. _Bioconductor_'s annotation packages help with mapping various ID schemes to each other. We load the `r Biocpkg("AnnotationDbi")` package and the annotation package `r Biocannopkg("org.Hs.eg.db")`: ```{r} library(org.Hs.eg.db) ``` This is the organism annotation package ("org") for *Homo sapiens* ("Hs"), organized as an [AnnotationDbi][] database package ("db"), using Entrez Gene IDs ("eg") as primary key. To get a list of all available key types, use: ```{r} columns(org.Hs.eg.db) res$hgnc_symbol <- unname(mapIds(org.Hs.eg.db, rownames(res), "SYMBOL", "ENSEMBL")) res$entrezgene <- unname(mapIds(org.Hs.eg.db, rownames(res), "ENTREZID", "ENSEMBL")) ``` Now the results have the desired external gene ids: ```{r} resOrdered <- res[order(res$pvalue),] head(resOrdered) ``` # Exporting results You can easily save the results table in a CSV file, which you can then load with a spreadsheet program such as Excel. The call to *as.data.frame* is necessary to convert the *DataFrame* object (`r Biocpkg("IRanges")` package) to a *data.frame* object which can be processed by *write.csv*. ```{r eval=FALSE} write.csv(as.data.frame(resOrdered), file="results.csv") ``` # Session information 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 help to trace down what has happened in case that an R script ceases to work because the functions have been changed in a newer version of a package. The session information should also **always** be included in any emails to the [Bioconductor support site](https://support.bioconductor.org) along with all code used in the analysis. ```{r} sessionInfo() ``` # Resources Acknowledgements - Core (Seattle): Sonali Arora, Marc Carlson, Nate Hayden, Jim Hester, Valerie Obenchain, Hervé Pagès, Paul Shannon, Dan Tenenbaum. - The research reported in this presentation was supported by the National Cancer Institute and the National Human Genome Research Institute of the National Institutes of Health under Award numbers U24CA180996 and U41HG004059, and the National Science Foundation under Award number 1247813. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation. [airway]: https://bioconductor.org/packages/airway [gplots]: https://cran.r-project.org/package=gplots [ggplot2]:https://cran.r-project.org/package=ggplot2 [AnnotationDbi]: https://bioconductor.org/packages/AnnotationDbi [Rsubread]: https://bioconductor.org/packages/Rsubread [edgeR]: https://bioconductor.org/packages/edgeR [limma]: https://bioconductor.org/packages/limma [BaySeq]: https://bioconductor.org/packages/BaySeq [DESeq2]: https://bioconductor.org/packages/DESeq2