Code Monkey home page Code Monkey logo

roryk-bcbiornaseq's Introduction

bcbioRNASeq

Travis CI AppVeyor CI Codecov Project Status: Active - The project has reached a stable, usable state and is being actively developed. Anaconda-Server Badge

Quality control and differential expression for bcbio RNA-seq experiments.

Installation

This is an R package.

source("https://bioconductor.org/biocLite.R")
biocLite("devtools")
biocLite(
    "hbc/bcbioRNASeq",
    dependencies = c("Depends", "Imports", "Suggests")
)

conda method

conda install -c bioconda r-bcbiornaseq

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
flatFiles <- flatFiles(bcb)
saveData(bcb, flatFiles)

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 renderings

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.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.