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pcaExplorer

pcaExplorer - Interactive exploration of Principal Components of Samples and Genes in RNA-seq data

https://github.com/federicomarini/pcaexplorer

Science Score: 49.0%

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Keywords

bioconductor principal-components r reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

Keywords from Contributors

bioconductor-package genomics gene bioinformatics core-package mass-spectrometry metabolomics core-services proteomics immunology
Last synced: 6 months ago · JSON representation

Repository

pcaExplorer - Interactive exploration of Principal Components of Samples and Genes in RNA-seq data

Basic Info
Statistics
  • Stars: 56
  • Watchers: 11
  • Forks: 17
  • Open Issues: 7
  • Releases: 2
Topics
bioconductor principal-components r reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly
Created almost 10 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License Code of conduct

README.md

pcaExplorer - Interactive exploration of Principal Components of Samples and Genes in RNA-seq data

Software status

R build status

| Platforms | OS | R CMD check | |:----------------:|:----------------:|:----------------:| | Bioc (devel) | Multiple | Bioconductor-devel Build Status | | Bioc (release) | Multiple | Bioconductor-release Build Status |

codecov.io

pcaExplorer is a Bioconductor package containing a Shiny application for analyzing expression data in different conditions and experimental factors.

It is a general-purpose interactive companion tool for RNA-seq analysis, which guides the user in exploring the Principal Components of the data under inspection.

pcaExplorer provides tools and functionality to detect outlier samples, genes that show particular patterns, and additionally provides a functional interpretation of the principal components for further quality assessment and hypothesis generation on the input data.

Moreover, a novel visualization approach is presented to simultaneously assess the effect of more than one experimental factor on the expression levels.

Thanks to its interactive/reactive design, it is designed to become a practical companion to any RNA-seq dataset analysis, making exploratory data analysis accessible also to the bench biologist, while providing additional insight also for the experienced data analyst.

Installation

pcaExplorer can be easily installed using BiocManager::install():

r if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("pcaExplorer")

or, optionally,

``` r BiocManager::install("federicomarini/pcaExplorer")

or alternatively...

devtools::install_github("federicomarini/pcaExplorer") ```

Quick start

This command loads the pcaExplorer package

r library("pcaExplorer")

The pcaExplorer app can be launched in different modes:

  • pcaExplorer(dds = dds, dst = dst), where dds is a DESeqDataSet object and dst is a DESeqTransform object, which were created during an existing session for the analysis of an RNA-seq dataset with the DESeq2 package

  • pcaExplorer(dds = dds), where dds is a DESeqDataSet object. The dst object is automatically computed upon launch.

  • pcaExplorer(countmatrix = countmatrix, coldata = coldata), where countmatrix is a count matrix, generated after assigning reads to features such as genes via tools such as HTSeq-count or featureCounts, and coldata is a data frame containing the experimental covariates of the experiments, such as condition, tissue, cell line, run batch and so on.

  • pcaExplorer(), and then subsequently uploading the count matrix and the covariates data frame through the user interface. These files need to be formatted as tab separated files, which is a common format for storing such count values.

Additional parameters and objects that can be provided to the main pcaExplorer function are:

  • pca2go, which is an object created by the pca2go function, which scans the genes with high loadings in each principal component and each direction, and looks for functions (such as GO Biological Processes) that are enriched above the background. The offline pca2go function is based on the routines and algorithms of the topGO package, but as an alternative, this object can be computed live during the execution of the app exploiting the goana function, provided by the limma package. Although this likely provides more general (and probably less informative) functions, it is a good compromise for obtaining a further data interpretation.

  • annotation, a data frame object, with row.names as gene identifiers (e.g. ENSEMBL ids) identical to the row names of the count matrix or dds object, and an extra column gene_name, containing e.g. HGNC-based gene symbols. This can be used for making information extraction easier, as ENSEMBL ids (a usual choice when assigning reads to features) do not provide an immediate readout for which gene they refer to. This can be either passed as a parameter when launching the app, or also uploaded as a tab separated text file.

Contact

For additional details regarding the functions of pcaExplorer, please consult the documentation or write an email to marinif@uni-mainz.de.

Code of Conduct

Please note that the pcaExplorer project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Bug reports/Issues/New features

Please use https://github.com/federicomarini/pcaExplorer/issues for reporting bugs, issues or for suggesting new features to be implemented.

Owner

  • Name: Federico Marini
  • Login: federicomarini
  • Kind: user
  • Location: Mainz
  • Company: University Medical Center, Mainz

Virchow Fellow, Bioinformatician @ Institute of Medical Biostatistics, Epidemiology and Informatics, Mainz (@imbeimainz)

GitHub Events

Total
  • Watch event: 1
  • Push event: 7
Last Year
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  • Push event: 7

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 555
  • Total Committers: 15
  • Avg Commits per committer: 37.0
  • Development Distribution Score (DDS): 0.09
Past Year
  • Commits: 47
  • Committers: 3
  • Avg Commits per committer: 15.667
  • Development Distribution Score (DDS): 0.085
Top Committers
Name Email Commits
Federico Marini m****f@u****e 505
Nitesh Turaga n****a@g****m 14
J Wokaty j****y@s****u 10
Dan Tenenbaum d****a@f****g 4
d.tenenbaum d****m@b****8 4
Herve Pages h****s@f****g 4
vobencha v****a@g****m 2
Hervé Pagès h****s@f****g 2
vobencha v****n@r****g 2
f.marini f****i@b****8 2
A Wokaty a****y@s****u 2
LiNk-NY m****9@g****m 1
Kayla-Morrell k****l@r****g 1
mtmorgan@fhcrc.org m****n@f****g@b****8 1
Martin Morgan m****n@f****g 1
Committer Domains (Top 20 + Academic)
fhcrc.org: 4 roswellpark.org: 2 sph.cuny.edu: 2 fredhutch.org: 1 uni-mainz.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 21
  • Total pull requests: 1
  • Average time to close issues: 2 months
  • Average time to close pull requests: 9 days
  • Total issue authors: 21
  • Total pull request authors: 1
  • Average comments per issue: 4.86
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 9 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
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  • Bot pull requests: 0
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  • danielmart (1)
  • tamuanand (1)
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  • Drepanis (1)
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Pull Request Authors
  • federicomarini (2)
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Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 60,579 total
  • Total dependent packages: 2
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
bioconductor.org: pcaExplorer

Interactive Visualization of RNA-seq Data Using a Principal Components Approach

  • Versions: 5
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Downloads: 60,579 Total
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 5.4%
Downloads: 16.3%
Maintainers (1)
marinif@uni-mainz.de
Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions
  • actions/cache v1 composite
  • actions/checkout v2 composite
  • actions/upload-artifact master composite
  • grimbough/bioc-actions/build-install-check v1 composite
  • grimbough/bioc-actions/run-BiocCheck v1 composite
  • grimbough/bioc-actions/setup-bioc v1 composite
  • r-lib/actions/setup-pandoc v2 composite
DESCRIPTION cran
  • AnnotationDbi * imports
  • DESeq2 * imports
  • DT * imports
  • GO.db * imports
  • GOstats * imports
  • GenomicRanges * imports
  • IRanges * imports
  • NMF * imports
  • S4Vectors * imports
  • SummarizedExperiment * imports
  • base64enc * imports
  • biomaRt * imports
  • genefilter * imports
  • ggplot2 >= 2.0.0 imports
  • ggrepel * imports
  • grDevices * imports
  • heatmaply * imports
  • knitr * imports
  • limma * imports
  • methods * imports
  • pheatmap * imports
  • plotly * imports
  • plyr * imports
  • rmarkdown * imports
  • scales * imports
  • shiny >= 0.12.0 imports
  • shinyAce * imports
  • shinyBS * imports
  • shinydashboard * imports
  • threejs * imports
  • tidyr * imports
  • topGO * imports
  • BiocStyle * suggests
  • airway * suggests
  • htmltools * suggests
  • org.Hs.eg.db * suggests
  • testthat * suggests