The DESeq2 package is a method for differential analysis of count data, so it is ideal for RNAseq (and other count-style data such as ChIPSeq). It uses dispersion estimates and relative expression changes to strengthen estimates and modeling with an emphasis on improving gene ranking in results tables. DESeq2 differs from edgeR in that it uses a geometric style normalization in which the per lane scaling factor is computed as the median of the ratios of the gene count over its geometric mean ratio, whereas edgeR uses the weighted one. The two normalization strategies are not mutually exclusive and both make different assumptions about the data. As with any RNAseq or large scale experiment, there is never an "out-of-the-box" best answer. You'll end up testing these methods and maybe others and closely examining results from control genes and cross-validation experiments to see which performs best. The performance will depend greatly on the particular dataset at hand, so the flexible approach we learn here will give you a good idea of how to test the different solutions for yourself.

The process we'll look at in this recipe is somewhat similar to that for edgeR in the preceding Recipe 1. We can use both ExpressionSets and count tables as input to DESeq2 and, when we've prepared them, we have a different set of functions to use to get our data into a DESeqDataSet, not the DGEList as with edgeR.

As in Recipe 1, the data is provided as both a count matrix and an ExpressionSet object and you can see the Appendix at the end of this book for further information on these object types. The data is in this book's code and data repository at https://github.com/PacktPublishing/R_Bioinformatics_Cookbook under datasets/ch1/modencodefly_eset.RData , datasets/ch1/modencodefly_count_table.txt, and datasets/ch1/modencodelfy_phenodata.txt. Again, we'll use readr and magrittr and, from Bioconductor, SummarizedExperiement, and DESeq2.

Estimating differential expressions with DESeq2 can be done in two ways, as shown in the following section.

Estimating differential expressions with DESeq2 from a count table (for example, in a text file), we will use the following steps:

1. Load count data:

count_dataframe <- readr::read_tsv(file.path(getwd(), "datasets", "ch1", "modencodefly_count_table.txt" ))
genes <- count_dataframe[['gene']]
count_dataframe[['gene']] <- NULL
count_matrix <- as.matrix(count_dataframe)
rownames(count_matrix) <- genes
pheno_data <- readr::read_table2(file.path(getwd(), "datasets", "ch1", "modencodefly_phenodata.txt"))

2. Specify experiments of interest:

experiments_of_interest <- c("L1Larvae", "L2Larvae")
columns_of_interest <- which( pheno_data[['stage']] %in% experiments_of_interest ) 

3. Form the grouping factor:

library(magrittr)
grouping <- pheno_data[['stage']][columns_of_interest] %>% 
 forcats::as_factor()

4. Form the subset of count data:

counts_of_interest <- count_matrix[,columns_of_interest]

5. Build the DESeq object:

library("DESeq2")
dds <- DESeqDataSetFromMatrix(countData = counts_of_interest,
 colData = grouping,
 design = ~ stage)

6. Carry out the analysis:

dds <- DESeq(dds)

7. Extract the results:

res <- results(dds, contrast=c("stage","L2Larvae","L1Larvae"))

To estimate differential expressions with DESeq2 from an ExpressionSet object, we will use the following steps:

1. Load the eset data and convert into DESeqDataSet():

library(SummarizedExperiment)
load(file.path(getwd(), "datasets/ch1/modencodefly_eset.RData"))
summ_exp <- makeSummarizedExperimentFromExpressionSet(modencodefly.eset)
ddsSE <- DESeqDataSet(summ_exp, design= ~ stage)

2. Carry out analysis and extract results:

ddsSE <- DESeq(ddsSE)
resSE <- results(ddsSE, contrast=c("stage","L2Larvae","L1Larvae"))

In the first section of this recipe, we used DESeq1 starting with our data in a text file; as you'll notice steps 1 to 4 are identical to those in the previous section.

In Step 1, we use the readr package's read_tsv() function to load the tab-delimited text file of counts into a dataframe called count_dataframe. Then, from that, we extract the 'gene' column to a new variable, genes, and erase it from count_dataframe, by assigning NULL. This is all done so we can easily convert into the count_matrix matrix with the base as.matrix() function and add the gene information back as rownames. Finally, we load the phenotype data we'll need from the file using the readr read_table2() function.

Step 2 is concerned with working out which columns in count_matrix we want to use. We define a variable, experiments_of_interest, that holds the column names we want and then use the %in% operator and which() functions to create a binary vector that matches the number of columns. If, say the third column of the columns_of_interest vector is 'TRUE', it indicates the name was in the experiments_of interest variable.

Step 3 begins with loading the magrittr package to get the %>% operator, which will allow piping. We then use R indexing with the binary columns_of_interest factor to select the names of columns we want and send it to the forcats as_factor() function to get a factor object for our grouping variable. Sample grouping information is basically a factor that tells us which samples are replications of the same thing and it's important for the experimental design description. You can see an expanded description of these grouping/factor objects in step 3 in Recipe 1.

In Step 4, we use indexing to extract the columns of data we want to actually analyze.

By Step 5, we are into the actual analysis section. First, we convert our matrix of counts into a DESeqDataSet object; this can be done with the conversion function, DESeqDataSetFromMatrix(), passing in the counts, the groups, and a design. The design is in the form of an R formula, hence, the ~ stage annotation.

In Step 6, we perform the actual analysis using the DESeq() function on the dds DESeqDataSet object and in Step 7, we get the results into the res variable using the results() function. The output has the following six columns:

baseMean log2FoldChange lfcSE stat pvalue padj

This shows the mean counts, the log2 fold change between samples for a gene, the standard error of the log2 fold change, the Wald statistic, and the raw and adjusted P value. The padj column for adjusted P values is the one most commonly used for concluding about significance.

Steps 1 and 2 show how to do the same procedure starting from the eset object. It only takes two short steps because DESeq2 is set up to work a lot more nicely with Bioconductor objects than edgeR is. In step 8, we load the eset data with the load() function. Then we use the makeSummarizedExperimentFromExpressionSet() function from the SummarizedExperiment Bioconductor package to convert eset into SummarizedExperiment, which can be used directly in the DESeq() function in step 9. This step works exactly as steps 6 and 7.