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The bcbioRNASeq constructor function is the main interface connecting bcbio] output data to interactive use in R. It is highly customizable and supports a number of options for advanced use cases. Consult the documentation for additional details.

Upload directory

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We have designed the constructor to work as simply as possible by default. The only required argument is uploadDir, the path to the bcbio final upload directory specified with upload: in the YAML configuration. Refer to the bcbio configuration documentation for detailed information on how to set up a bcbio run, which is outside the scope of this vignette.

For example, let’s load up the example bcbio dataset stored internally in the package.

bcbio outputs RNA-seq data in a standardized directory structure, which is described in detail in our workflow paper.

Counts level

By default, bcbioRNASeq imports counts at gene level, which are required for standard differential expression analysis (level = 'genes'). For pseudo-aligned counts (e.g. Salmon, Kallisto, Sailfish) (Bray et al. 2016; Patro, Mount, and Kingsford 2014; Patro et al. 2017), tximport(Soneson, Love, and Robinson 2016) is used internally to aggregate transcript-level counts to gene-level counts, and generates length-scaled transcripts per million (TPM) values. For aligned counts processed with featureCounts(Liao, Smyth, and Shi 2014) (e.g. STAR, HISAT2) (Dobin et al. 2013; Dobin and Gingeras 2016; Kim, Langmead, and Salzberg 2015), these values are already returned at gene level, and therefore not handled by tximport. Once the gene-level counts are imported during the bcbioRNASeq call, the DESeq2 package (Love, Huber, and Anders 2014) is then used to generate an internal DESeqDataSet from which we derive normalized and variance-stabilized counts.

Alternatively, if you want to perform transcript-aware analysis, such as differential exon usage or splicing analysis, transcript-level counts can be obtained using level = 'transcripts'. Note that when counts are loaded at transcript level, TPMs are generated with tximport internally, but no additional normalizations or transformations normally calculated for gene-level counts with DESeq2 are generated.

Expression callers

Since bcbio is flexible and supports a number of expression callers, we have provided advanced options in the bcbioRNASeq constructor to support a variety of workflows using the caller argument. Salmon, Kallisto, and Sailfish counts are supported at either gene or transcript level. Internally, these are loaded using tximport. STAR and HISAT2 aligned counts processed with featureCounts are also supported, but only at gene level.

Sample selection and metadata

If you’d like to load up only a subset of samples, this can be done easily using the samples argument. Note that the character vector declared here must match the description column specified in the sample metadata. Conversely, if you’re working with a large dataset and you simply want to drop a few samples, this can be accomplished with the censorSamples argument.

When working with a bcbio run that has incorrect or outdated metadata, the simplest way to fix this issue is to pass in new metadata from an external spreadsheet (CSV or Excel) using the sampleMetadataFile argument. Note that this can also be used to subset the bcbio dataset, similar to the samples argument (see above), based on the rows that are included in the spreadsheet.

Genome annotations

When analyzing a dataset against a well-annotated genome, we recommend importing the corresponding metadata using AnnotationHub and ensembldb. This functionality is natively supported in the bcbioRNASeq constructor with using the organism, ensemblRelease, and genomeBuild arguments. For example, with our internal bcbio dataset, we’re analyzing counts generated against the EnsemblMus musculus GRCm38 genome build (release 87). These parameters can be defined in the object load call to ensure that the annotations match up exactly with the genome used.

This will return a GRanges object using the GenomicRanges package (Lawrence et al. 2013), which contains coordinates and rich metadata for each gene or transcript. These annotations are accessible with the rowRanges and rowData functions defined in the SummarizedExperiment package (Huber et al. 2015).

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Alternatively, transcript-level annotations can also be obtained automatically using this method.

When working with a dataset generated against a poorly-annotated or non-standard genome, we provide a fallback method for loading gene annotations from a general feature format (GFF) file with the gffFile argument. If possible, we recommend providing a general transfer format (GTF) file, which is identical to GFF version 2. GFFv3 is more complicated and non-standard, but Ensembl GFFv3 files are also supported.

If your dataset contains transgenes (e.g. EGFP, TDTOMATO), these features can be defined with the transgeneNames argument, which will automatically populate the rowRanges slot with placeholder metadata.

We recommend loading up data per genome in its own bcbioRNASeq object when possible, so that rich metadata can be imported easily. In the edge case where you need to look at multiple genomes simultaneously, set organism = NULL, and bcbioRNASeq will skip the gene annotation acquisition step.

Refer to the the GenomicRanges and SummarizedExperiment package documentation for more details on working with the genome annotations defined in the rowRanges slot of the object. Here are some useful examples:

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Variance stabilization

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During the bcbioRNASeq constructor call, log2 variance stabilizaton of gene-level counts can be calculated automatically, and is recommended. This is performed internally by the DESeq2 package, using the varianceStabilizingTransformation and/or rlog functions. These transformations will be slotted into assays.

Major changes

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• bcbioRNASeq S4 class object is now extending RangedSummarizedExperiment instead of SummarizedExperiment. Consequently, the row annotations are now stored in the rowRanges slot as GRanges class, instead of in the rowData slot as a DataFrame. The rowData accessor still works and returns a data frame of gene/transcript annotations, but these are now coerced from the internally stored GRanges. The GRanges object is acquired automatically from Ensembl using basejump::ensembl. By default, GRanges are acquired from Ensembl using AnnotationHub and ensembldb. Legacy GRCh37 genome build is supported using the EnsDb.Hsapiens.v75 package.
• assays now only slot matrices. We’ve moved the tximport data from the now defunct bcbio slot to assays. This includes the lengths matrix from tximport. Additionally, we are optionally slotting DESeq2 variance-stabilized counts (“rlog”, 'vst'). DESeq2 normalized counts and edgeR TMM counts are calculated on the fly and no longer stored inside the bcbioRNASeq object.
• colData now defaults to returning as data.frame instead of DataFrame, for easy piping to tidyverse functions.
• bcbio slot is now defunct.
• FASTA spike-ins (e.g. EGFP, ERCCs) can be defined using the isSpike argument during the loadRNASeq data import step.
• Melted counts are now scaled to log2 in the relevant quality control functions rather than using log10. This applies to plotCountsPerGene and plotCountDensity. Note that we are subsetting the nonzero genes as defined by the raw counts here.
• Simplified internal tximport code to no longer attempt to strip transcript versions. This is required for working with C. elegans transcripts.
• Minimal working example dataset is now derived from GSE65267, which is also used in the F1000 paper.
• Added as(object, 'DESeqDataSet') coercion method support for bcbioRNASeq class. This helps us set up the differential expression analysis easily.
• counts function now returns DESeq2 normalized counts (normalized = TRUE) and edgeR TMM counts (normalized = 'tmm') on the fly, as suggested by the F1000 reviewers.
• Design formula can no longer be slotted into bcbioRNASeq object, since we’re not stashing a DESeqDataSet any more.
• Updated Functional Analysis R Markdown template.