bcbioRNASeq

R package for bcbio RNA-seq analysis.

Installation

Bioconductor method (recommended)

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("devtools")
biocLite("remotes")
biocLite("GenomeInfoDbData")
biocLite(
    "hbc/bcbioRNASeq",
    dependencies = c("Depends", "Imports", "Suggests")
)

F1000 paper version

# v0.2.4
biocLite("hbc/bcbioRNASeq", ref = "v0.2.4")

conda method

conda install -c bioconda r-bcbiornaseq

To avoid version issues, your .condarc file should only contain the following channels, in this order:
    channels:
        - bioconda
        - conda-forge
        - defaults
        
We recommend installing into a clean conda environment:
    conda create --name bcbiornaseq_env
    conda activate bcbiornaseq_env

Note that there is currently a bug with conda and libgfortran. You may need to install conda's libgfortran-ng to get the bcbioRNASeq package to load in R:
    conda install libgfortran-ng

Load bcbio run

library(bcbioRNASeq)
bcb <- bcbioRNASeq(
    uploadDir = "bcbio_rnaseq_run/final",
    interestingGroups = c("genotype", "treatment"),
    organism = "Homo sapiens"
)
# Back up all data inside bcbioRNASeq object
flat <- flatFiles(bcb)
saveData(bcb, flat)

This will return a bcbioRNASeq object, which is an extension of the Bioconductor RangedSummarizedExperiment container class.

Parameters:

• uploadDir: Path to the bcbio final upload directory.
• interestingGroups: Character vector of the column names of interest in the sample metadata, which is stored in the colData() accessor slot of the bcbioRNASeq object. These values should be formatted in camelCase, and can be reassigned in the object after creation (e.g. interestingGroups(bcb) <- c("batch", "age")). They are used for data visualization in the quality control utility functions.
• organism: Organism name. Use the full latin name (e.g. "Homo sapiens").

Consult help("bcbioRNASeq", "bcbioRNASeq") for additional documentation.

Sample metadata

When loading a bcbio RNA-seq run, the sample metadata will be imported automatically from the project-summary.yaml file in the final upload directory. If you notice any typos in your metadata after completing the run, these can be corrected by editing the YAML file. Alternatively, you can pass in a sample metadata file into bcbioRNASeq() using the sampleMetadataFile argument.

Metadata file example

The samples in the bcbio run must map to the description column. The values provided in description must be unique. These values will be sanitized into syntactically valid names (see help("makeNames", "basejump")), and assigned as the column names of the bcbioRNASeq object. The original values are stored as the sampleName column in colData(), and are used for all plotting functions.

description	genotype
sample1	wildtype
sample2	knockout
sample3	wildtype
sample4	knockout

R Markdown templates

This package provides multiple R Markdown templates, including quality control, differential expression using DESeq2, and functional enrichment analysis.

These are available in RStudio at File -> New File -> R Markdown... -> From Template.

Example reports

• Quality Control
• Differential Expression
• Functional Analysis

Citation

citation("bcbioRNASeq")

Steinbaugh MJ, Pantano L, Kirchner RD, Barrera V, Chapman BA, Piper ME, Mistry M, Khetani RS, Rutherford KD, Hoffman O, Hutchinson JN, Ho Sui SJ. (2017). bcbioRNASeq: R package for bcbio RNA-seq analysis. F1000Research 6:1976.

References

The papers and software cited in our workflows are available as a shared library on Paperpile.