Package 'OmicNavigator'

Description

Package options to control package-wide behavior are described below.

Details

The default prefix for OmicNavigator study packages is "ONstudy". If you would prefer to use a different prefix, you can change the package option OmicNavigator.prefix. For example, to use the prefix "OmicNavigatorStudy", you could add the following line to your .Rprofile file.

options(OmicNavigator.prefix = "OmicNavigatorStudy")

Author(s)

Maintainer: John Blischak [email protected] (ORCID)

Authors:

• Terrence Ernst (Web application)

• Paul Nordlund (Web application)

• Justin Moore (UpSet-related functions and web application)

• Joe Dalen (Barcode functionality and web application)

• Akshay Bhamidipati (Web application)

• Brett Engelmann [email protected]

• Marco Curado (Improved plotting capabilities)

• Joe LoGrasso (Support for plotly)

Other contributors:

• AbbVie Inc. [copyright holder, funder]

See Also

Useful links:

• https://github.com/abbvie-external/OmicNavigator

• Report bugs at https://github.com/abbvie-external/OmicNavigator/issues

Description

Add annotations

Usage

addAnnotations(study, annotations, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

Add assays

Usage

addAssays(study, assays, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

The app can display a barcode plot of the enrichment results for a given annotation term. The metadata in barcodes instructs the app how to create and label the barcode plot.

Usage

addBarcodes(study, barcodes, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

Add enrichment results

Usage

addEnrichments(study, enrichments, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

You can provide additional information on the annotation terms in your study by providing linkouts to external resources. These will be embedded directly in the enrichments table.

Usage

addEnrichmentsLinkouts(study, enrichmentsLinkouts, reset = FALSE)

Arguments

Details

For each linkout, the URL pattern you provide will be concatenated with the value of the termID column. As an example, if you used the annotation database AmiGO 2 for your enrichments analysis, you can provide a linkout for each termID using the following pattern:

go = "https://amigo.geneontology.org/amigo/term/"

As another example, if you used the annotation database Reactome for your enrichments analysis, you can provide a linkout for each termID using the following pattern:

reactome = "https://reactome.org/content/detail/"

Note that you can provide more than one linkout per termID.

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

See Also

addAnnotations, addEnrichments

Examples

study <- createStudy("example")
  enrichmentsLinkouts <- list(
    gobp = c("https://amigo.geneontology.org/amigo/term/",
             "https://www.ebi.ac.uk/QuickGO/term/"),
    reactome = "https://reactome.org/content/detail/"
  )
  study <- addEnrichmentsLinkouts(study, enrichmentsLinkouts)

Description

Add feature metadata

Usage

addFeatures(study, features, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

Add mapping object

Usage

addMapping(study, mapping, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

See Also

getPlottingData, plotStudy

Description

The meta-features table is useful anytime there are metadata variables that cannot be mapped 1:1 to your features. For example, a peptide may be associated with multiple proteins.

Usage

addMetaFeatures(study, metaFeatures, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

You can provide additional information on the metaFeatures in your study by providing linkouts to external resources. These will be embedded directly in the metaFeatures table.

Usage

addMetaFeaturesLinkouts(study, metaFeaturesLinkouts, reset = FALSE)

Arguments

Details

For each linkout, the URL pattern you provide will be concatenated with the value of that column for each row. As an example, if your metaFeatures table included a column named "ensembl" that contained the Ensembl Gene ID for each feature, you could create a linkout to Ensembl using the following pattern:

ensembl = "https://ensembl.org/Homo_sapiens/Gene/Summary?g="

As another example, if you had a column named "entrez" that contained the Entrez Gene ID for each feature, you could create a linkout to Entrez using the following pattern:

entrez = "https://www.ncbi.nlm.nih.gov/gene/"

Note that you can provide more than one linkout per column.

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

See Also

addMetaFeatures

Examples

study <- createStudy("example")
  metaFeaturesLinkouts <- list(
    default = list(
      ensembl = c("https://ensembl.org/Homo_sapiens/Gene/Summary?g=",
                  "https://www.genome.ucsc.edu/cgi-bin/hgGene?hgg_gene="),
      entrez = "https://www.ncbi.nlm.nih.gov/gene/"
    )
  )
  study <- addMetaFeaturesLinkouts(study, metaFeaturesLinkouts)

Description

Add models

Usage

addModels(study, models, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Examples

study <- createStudy("example")
  models <- list(
    model_01 = "Name of first model",
    model_02 = "Name of second model"
  )
  study <- addModels(study, models)

  # Alternative: provide additional metadata about each model
  models <- list(
    model_01 = list(
      description = "Name of first model",
      data_type = "transcriptomics"
    ),
    model_02 = list(
      description = "Name of second model",
      data_type = "proteomics"
    )
  )

Description

The app's network view of the enrichments results requires pairwise overlap metrics between all the terms of each annotation in order to draw the edges between the nodes/terms. These overlaps are calculated automatically when installing or exporting an OmicNavigator study. If you'd like, you can manually calculate these pairwise overlaps by calling addOverlaps prior to installing or exporting your study.

Usage

addOverlaps(study, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

addPlots() adds custom plotting functions and plot metadata to an OmicNavigator study.

Usage

addPlots(study, plots, reset = FALSE)

Arguments

Details

Custom plotting functions must be constructed to accept as the first argument the value returned from getPlottingData(). Custom plotting functions can have additional arguments, but these must be provided with default values. The end-user should call getPlottingData() when testing their custom plotting function. The end-user should consider the nature of the plot, i.e. the plotType and (rarely) models values (see getPlottingData()). For example, a custom plotting function meant to produce a multiTest plot should accept the output of a getPlottingData() call with multiple testIDs assigned to the testID argument. See the details section of plotStudy() for a description of how plotType dictates the way a custom plotting function is invoked by the app.

Note that any ggplot2 plots will require extra care. This is because the plotting code will be inserted into a study package, and thus must follow the best practices for using ggplot2 within packages. Specifically, when you refer to columns of the data frame, e.g. aes(x = group), you need to prefix it with .data$, so that it becomes aes(x = .data$group). Fortunately this latter code will also run fine as you interactively develop the function.

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

See Also

getPlottingData, plotStudy

Description

You can include reports of the analyses you performed to generate the results.

Usage

addReports(study, reports, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

Add inference results

Usage

addResults(study, results, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

You can provide additional information on the features in your study by providing linkouts to external resources. These will be embedded directly in the results table.

Usage

addResultsLinkouts(study, resultsLinkouts, reset = FALSE)

Arguments

Details

For each linkout, the URL pattern you provide will be concatenated with the value of that column for each row. As an example, if your features table included a column named "ensembl" that contained the Ensembl Gene ID for each feature, you could create a linkout to Ensembl using the following pattern:

ensembl = "https://ensembl.org/Homo_sapiens/Gene/Summary?g="

As another example, if you had a column named "entrez" that contained the Entrez Gene ID for each feature, you could create a linkout to Entrez using the following pattern:

entrez = "https://www.ncbi.nlm.nih.gov/gene/"

Note that you can provide more than one linkout per column.

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

See Also

addFeatures

Examples

study <- createStudy("example")
  resultsLinkouts <- list(
    default = list(
      ensembl = c("https://ensembl.org/Homo_sapiens/Gene/Summary?g=",
                  "https://www.genome.ucsc.edu/cgi-bin/hgGene?hgg_gene="),
      entrez = "https://www.ncbi.nlm.nih.gov/gene/"
    )
  )
  study <- addResultsLinkouts(study, resultsLinkouts)

Description

Add sample metadata

Usage

addSamples(study, samples, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Description

Add tests

Usage

addTests(study, tests, reset = FALSE)

Arguments

Value

Returns the original onStudy object passed to the argument study, but modified to include the newly added data

Examples

study <- createStudy("example")
  tests <- list(
    default = list(
      test_01 = "Name of first test",
      test_02 = "Name of second test"
    )
  )
  study <- addTests(study, tests)

  # Alternative: provide additional metadata about each test
  tests <- list(
    default = list(
      test_01 = list(
        description = "Name of first test",
        comparison_type = "treatment vs control",
        effect_size = "beta"
      ),
      test_02 = list(
        description = "Name of second test",
        comparison_type = "treatment vs control",
        effect_size = "logFC"
      )
    )
  )

Description

A subset of the object basal.vs.lp from Bioconductor workflow RNAseq123.

Usage

basal.vs.lp

Format

A data frame with 24 rows and 8 columns:

ENTREZID

Entrez ID of mouse gene

SYMBOL

Symbol of mouse gene

TXCHROM

Chromosome location of mouse gene

logFC

Log fold change

AveExpr

Average expression level of the gene across all samples

t

Moderated t-statistic

P.Value

p-value

adj.P.Val

Adjusted p-value

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

head(basal.vs.lp)
  str(basal.vs.lp)

Description

A subset of the object basal.vs.ml from Bioconductor workflow RNAseq123.

Usage

basal.vs.ml

Format

A data frame with 24 rows and 8 columns:

ENTREZID

Entrez ID of mouse gene

SYMBOL

Symbol of mouse gene

TXCHROM

Chromosome location of mouse gene

logFC

Log fold change

AveExpr

Average expression level of the gene across all samples

t

Moderated t-statistic

P.Value

p-value

adj.P.Val

Adjusted p-value

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

head(basal.vs.ml)
  str(basal.vs.ml)

Description

A subset of the object cam.BasalvsLP from Bioconductor workflow RNAseq123.

Usage

cam.BasalvsLP

Format

A data frame with 4 rows and 4 columns:

NGenes

Number of genes in each term

Direction

Direction of the enrichment

PValue

Nominal p-value

FDR

Multiple-testing adjusted p-value

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

head(cam.BasalvsLP)
  str(cam.BasalvsLP)

Description

A subset of the object cam.BasalvsML from Bioconductor workflow RNAseq123.

Usage

cam.BasalvsML

Format

A data frame with 4 rows and 4 columns:

NGenes

Number of genes in each term

Direction

Direction of the enrichment

PValue

Nominal p-value

FDR

Multiple-testing adjusted p-value

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

head(cam.BasalvsML)
  str(cam.BasalvsML)

Description

Create a new OmicNavigator study by combining two or more existing study objects.

Usage

combineStudies(...)

Arguments

Details

This is a convenience function to quickly and conveniently combine studies. However, it is naive, and you will likely need to edit the new study after combining. When there are conflicting elements (e.g. different study names or different maintainers), then the value for the latter study is kept. As a concrete example, if you combined 5 studies, the name of the combined study would be the name of the 5th study.

The behavior is more complex for study elements that are nested lists of data frames (e.g. results). If the 5 studies included a results table for the same modelID/testID combination, then only the results from the 5th study would be retained. However, if they each defined a different modelID, then the results for all 5 modelIDs would be included in the combined study. Please note that you should be extra cautious in the situation where the studies have the same modelID/testID combination. Ideally they should all have the same column names. Since a data frame is technically a list, the workhorse function modifyList will retain any uniquely named columns from earlier studies along with the columns from the final study.

Note that as a shortcut you can also combine studies using the S3 method c.

If a study you would like to combine is already installed, you can convert it to a study object by importing it with importStudy.

Value

Returns a new combined OmicNavigator study object, which is a named nested list with class onStudy

See Also

createStudy, importStudy

Examples

# Define threee study objects
studyOne <- createStudy(name = "One",
                        description = "First study",
                        studyMeta = list(metafield1 = "metavalue1"))

studyTwo <- createStudy(name = "Two",
                        description = "Second study",
                        maintainer = "The Maintainer",
                        studyMeta = list(metafield2 = "metavalue2"))

studyThree <- createStudy(name = "Three",
                          description = "Third study",
                          studyMeta = list(metafield3 = "metavalue3"))

# Combine the three studies
combineStudies(studyOne, studyTwo, studyThree)

# Equivalently, can use c()
c(studyOne, studyTwo, studyThree)

Description

Create a new OmicNavigator study.

Usage

createStudy(
  name,
  description = name,
  samples = list(),
  features = list(),
  models = list(),
  assays = list(),
  tests = list(),
  annotations = list(),
  results = list(),
  enrichments = list(),
  metaFeatures = list(),
  plots = list(),
  mapping = list(),
  barcodes = list(),
  reports = list(),
  resultsLinkouts = list(),
  enrichmentsLinkouts = list(),
  metaFeaturesLinkouts = list(),
  version = NULL,
  maintainer = NULL,
  maintainerEmail = NULL,
  studyMeta = list()
)

Arguments

Details

You can add metadata to describe your study by passing a named list to to the argument studyMeta. The names of the list cannot contain spaces or colons, and they can't start with # or -. The values of each list should be a single value. Also, your metadata fields cannot use any of the reserved fields for R's DESCRIPTION file.

Value

Returns a new OmicNavigator study object, which is a named nested list with class onStudy

See Also

addSamples, addFeatures, addModels, addAssays, addTests, addAnnotations, addResults, addEnrichments, addMetaFeatures, addPlots, addMapping, addBarcodes, addReports, addResultsLinkouts, addEnrichmentsLinkouts, addMetaFeaturesLinkouts, exportStudy, installStudy

Examples

study <- createStudy(name = "ABC",
                     description = "An analysis of ABC")

# Define a version and study metadata
study <- createStudy(name = "ABC",
                     description = "An analysis of ABC",
                     version = "0.1.0",
                     maintainer = "My Name",
                     maintainerEmail = "[email protected]",
                     studyMeta = list(department = "immunology",
                                      organism = "Mus musculus"))

Description

Export a study

Usage

exportStudy(
  study,
  type = c("tarball", "package"),
  path = NULL,
  requireValid = TRUE
)

Arguments

Value

Invisibly returns the name of the tarball file ("tarball") or the path to the package directory ("package")

See Also

validateStudy

Description

Get annotations from a study

Usage

getAnnotations(study, annotationID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get assays from a study

Usage

getAssays(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get data for barcode and violin plots

Usage

getBarcodeData(study, modelID, testID, annotationID, termID)

Arguments

Value

A list with the following components:

See Also

addBarcodes, getBarcodes

Description

Get barcodes from a study

Usage

getBarcodes(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get enrichments from a study

Usage

getEnrichments(
  study,
  modelID = NULL,
  annotationID = NULL,
  testID = NULL,
  quiet = FALSE,
  libraries = NULL
)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

getEnrichmentsIntersection

Usage

getEnrichmentsIntersection(
  study,
  modelID,
  annotationID,
  mustTests,
  notTests,
  sigValue,
  operator,
  type
)

Arguments

Value

Returns a data frame with the enrichments, similar to getEnrichmentsTable. Only rows that pass all the filters are included.

See Also

getEnrichmentsTable

Description

Get enrichments table linkouts from a study

Usage

getEnrichmentsLinkouts(
  study,
  annotationID = NULL,
  quiet = FALSE,
  libraries = NULL
)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get enrichments network from a study

Usage

getEnrichmentsNetwork(study, modelID, annotationID, libraries = NULL)

Arguments

Value

Returns a list with the following components:

Description

Get enrichments table from a study

Usage

getEnrichmentsTable(
  study,
  modelID,
  annotationID,
  type = "nominal",
  libraries = NULL
)

Arguments

Value

A data frame of enrichments with the following columns:

Description

getEnrichmentsUpset

Usage

getEnrichmentsUpset(
  study,
  modelID,
  annotationID,
  sigValue,
  operator,
  type,
  tests = NULL
)

Arguments

Value

No return value. This function is called for the side effect of creating an UpSet plot.

Description

To enhance the display of the linkouts in the app's tables, it can fetch the favicon URL for each website.

Usage

getFavicons(linkouts)

Arguments

Value

The URLs to the favicons for each linkout. The output returned will always be the same class and structure as the input.

See Also

getResultsLinkouts, getEnrichmentsLinkouts

Description

Get features from a study

Usage

getFeatures(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

A data frame (if modelID is specified) or a list of data frames. All the columns will be character strings, even if the values appear numeric.

Description

Get installed OmicNavigator studies

Usage

getInstalledStudies(libraries = NULL)

Arguments

Value

Returns a character vector of the installed OmicNavigator study packages

Description

Get the shared features in a network link

Usage

getLinkFeatures(study, annotationID, termID1, termID2)

Arguments

Value

Returns a character vector with the features included in both termIDs (i.e. the intersection)

See Also

getNodeFeatures

Description

Get mapping object from a study

Usage

getMapping(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get metaFeatures from a study

Usage

getMetaFeatures(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get metaFeatures table linkouts from a study

Usage

getMetaFeaturesLinkouts(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get metaFeatures for a given feature

Usage

getMetaFeaturesTable(study, modelID, featureID)

Arguments

Value

Returns a data frame with the metaFeatures for the provided featureID. If the featureID is not found in the metaFeatures table, the data frame will have zero rows.

See Also

addMetaFeatures, getMetaFeatures

Description

Get models from a study

Usage

getModels(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get the features in a network node

Usage

getNodeFeatures(study, annotationID, termID, libraries = NULL)

Arguments

Value

Returns a character vector with the features in the termID

See Also

getLinkFeatures

Description

Get overlaps from a study

Usage

getOverlaps(study, annotationID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

This is a convenience function for the app. It is easier to always call the OmicNavigator package functions via OpenCPU than to call the utils package for this one endpoint.

Usage

getPackageVersion()

Value

Returns a one-element character vector with the version of the currently installed OmicNavigator R package

Description

Get plots from a study

Usage

getPlots(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Returns assay, sample, feature, and result data that may be used for plotting. This function is called by plotStudy() and the output is passed to custom plotting functions. It should be used directly when interactively creating custom plotting functions.

Usage

getPlottingData(study, modelID, featureID, testID = NULL, libraries = NULL)

Arguments

Details

The end-user should call this function and populate the first argument of their custom plotting function with the output. When building functions, the end-user should understand the category of plotting function they are creating (e.g. singleFeature or multiFeature, see addPlots()) and call getPlottingData() accordingly.

Custom plots that accept data from multiple models and a single test (⁠plotType = c(‘multiModel’, ‘singleTest’)⁠; see addPlots()) should be built to accept output from getPlottingData() where modelID is vector of length n and testID is a vector of length n, where n is the number of models. Custom plots that accept data from multiple models and multiple tests (⁠plotType = c(‘multiModel’, ‘multiTest’)⁠) should be built to accept output from getPlottingData() where modelID and testID vectors are length m, where m is the total number of tests considered across all models (note that testIDs must be repeated across models for the plotting function to work in the app). The index positions of these two vectors should correspond. That is, testID position 1 should be found in the model specified by modelID position 1, etc. See addPlots() for information about the assignment of plotTypes for your custom plots.

Value

Returns a list of at least 4 objects:

The data frame results is only returned if you pass a testID. By default the app will always pass the currently selected testID.

See Also

addPlots, plotStudy

Description

Get link to report

Usage

getReportLink(study, modelID)

Arguments

Value

Returns a one-element character vector with either a path to a report file or a URL to a report web page. If no report is available for the modelID, an empty character vector is returned.

Description

Get reports from a study

Usage

getReports(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get results from a study

Usage

getResults(
  study,
  modelID = NULL,
  testID = NULL,
  quiet = FALSE,
  libraries = NULL
)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

getResultsIntersection

Usage

getResultsIntersection(
  study,
  modelID,
  anchor,
  mustTests,
  notTests,
  sigValue,
  operator,
  column
)

Arguments

Value

Returns a data frame with the results, similar to getResultsTable. Only rows that pass all the filters are included. The new column Set_Membership is a comma-separated field that includes the testIDs in which the featureID passed the filters.

See Also

getResultsTable

Description

Get results table linkouts from a study

Usage

getResultsLinkouts(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get results table from a study

Usage

getResultsTable(
  study,
  modelID,
  testID,
  annotationID = NULL,
  termID = NULL,
  libraries = NULL
)

Arguments

Value

A data frame which includes the columns from the features table followed by the columns from the results table. All the columns from the features table will be character strings, even if the values appear numeric.

If the optional arguments annotationID and termID are provided, the table will be filtered to only include features in that annotation term.

Description

getResultsUpset

Usage

getResultsUpset(study, modelID, sigValue, operator, column, legacy = FALSE)

Arguments

Value

Invisibly returns the output from upset

Description

Get samples from a study

Usage

getSamples(study, modelID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Get tests from a study

Usage

getTests(study, modelID = NULL, testID = NULL, quiet = FALSE, libraries = NULL)

Arguments

Value

The object returned depends on the data available and any filters (e.g. the argument modelID):

If no filters are specified, then the object returned is a nested list, similar to the original input object.

If one or more filters are applied, then only a subset of the original nested list is returned. Technically, each filter applied is used to subset the original nested list using [[.

If no data is available, an empty list is returned (list()).

Description

Determine the common columns across all tests of a model that are available for filtering with UpSet.

Usage

getUpsetCols(study, modelID)

Arguments

Value

Returns a character vector with the names of the common columns

Description

A subset of the object group from Bioconductor workflow RNAseq123.

Usage

group

Format

A factor with 3 levels:

Basal

Basal cells

LP

Luminal progenitor cells

ML

Mature luminal cells

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

table(group)
  str(group)

Description

Create an onStudy object by importing an installed study package

Usage

importStudy(study, libraries = NULL)

Arguments

Value

Returns the onStudy object imported from the OmicNavigator study package

Description

In order to run the OmicNavigator web app on your local machine, the app must be installed in the www/ subdirectory of the R package. If you installed the release tarball from the GitHub Releases page, then you already have the app installed. If you installed directly from GitHub with install_github, or if you want to use a different version of the app, you can manually download and install the app.

Usage

installApp(version = NULL, overwrite = FALSE, lib.loc = NULL, ...)

Arguments

Value

A one-element character vector with the absolute path to the directory in which the app files were installed

Description

Install a study as an R package

Usage

installStudy(study, library = .libPaths()[1])

Arguments

Value

Invisibly returns the original onStudy object that was passed to the argument study

Description

A subset of the object lane from Bioconductor workflow RNAseq123.

Usage

lane

Format

A factor with 3 levels:

L004

Sample sequenced on lane 4

L006

Sample sequenced on lane 6

L008

Sample sequenced on lane 8

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

table(lane)
  str(lane)

Description

A subset of the object lcpm from Bioconductor workflow RNAseq123.

Usage

lcpm

Format

A matrix with 24 rows and 9 columns

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

head(lcpm)
  str(lcpm)

Description

List available studies and their metadata

Usage

listStudies(libraries = NULL)

Arguments

Value

Returns a nested list with one element per installed OmicNavigator study package. Each study package entry has the following sublist components:

Description

A subset of the object Mm.c2 from Bioconductor workflow RNAseq123.

Usage

Mm.c2

Format

A list of 4 character vectors

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

Mm.c2[[1]]
  str(Mm.c2)

Description

plotStudy() invokes a custom plotting function saved within an OmicNavigator study. This function is called by the app using the study-model-test selection, feature selections, and plotting function metadata (see addPlots()) to define arguments.

Usage

plotStudy(study, modelID, featureID, plotID, testID = NULL, libraries = NULL)

Arguments

Details

The arguments study, modelID, featureID, and testID are passed to the function getPlottingData(). The list returned by getPlottingData() is passed as the first argument to a custom plotting function. Some custom plotTypes (see addPlots()) require care when being invoked and attention should be paid to how a custom plot will be rendered by the app. Custom plots with ⁠plotType = c(‘multiModel’, ‘singleTest’)⁠ accept a modelID vector of length n and a vector of testIDs length n, where n is the number of models. Custom plots with ⁠plotType = c(‘multiModel’, ‘multiTest’)⁠ accept modelID and testID vectors of length m, where m is the total number of tests considered across all models (note testIDs are often repeated across models). Note that the index positions of these two vectors should correspond. That is, testID position 1 should be found in the model specified by modelID position 1, etc.

The app will invoke custom plotting functions via plotStudy() using the current menu selections and plot metadata (see addPlots()). Plots with ⁠plotType = ‘multiTest’⁠ will be invoked with all testIDs found within the currently selected model. Plots with ⁠plotType = c(‘multiModel’,‘singleTest’)⁠ will be invoked with all modelIDs within the study (unless the plot has specified a list of models via models) and the currently selected testID (an error will result if the currently selected testID is not present in all relevant models for the plot). Plots with ⁠plotType = c(‘multiModel’, ‘multiTest’)⁠ will be invoked with all modelIDs within the study (unless the plot has specified a list of models via models) and all identical testIDs across models (if there are no matching testIDs across models an error will result).

Value

This function is called for the side effect of creating a plot. It invisibly returns the result from the custom plotting function specified by plotID. Previously it invisibly returned the study object. It's unlikely you relied on this behavior. For a ggplot2 plot, the return value will be the plotting object with class "ggplot". For a plotly plot, the return value will be the json schema used for plotting with class ⁠“json”⁠.

See Also

addPlots, getPlottingData

Description

Remove an installed study R package

Usage

removeStudy(study, library = .libPaths()[1])

Arguments

Value

Invisibly returns the path of the removed study package

Description

A subset of the object samplenames from Bioconductor workflow RNAseq123.

Usage

samplenames

Format

A character vector containing the unique sample identifiers

Source

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html

References

Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR version 3; peer review: 3 approved. F1000Research 2018, 5:1408 doi:10.12688/f1000research.9005.3

Sheridan, J.M., Ritchie, M.E., Best, S.A. et al. A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer 2015, 15:221 doi:10.1186/s12885-015-1187-z

Examples

head(samplenames)
  str(samplenames)

Description

After you have installed at least one OmicNavigator study package with installStudy, you can explore the results in the app. The function startApp starts a local instance of the app running on your current machine. It will automatically open the app in your default browser. For the best experience, use Google Chrome. From the dropdown menu, you will be able to select from any of the studies you have installed on your machine. When you are finished, you can stop the web server by returning to the R console and pressing the Esc key (Windows) or Ctrl-C (Linux, macOS).

Usage

startApp(...)

Arguments

Details

Note that the app can't be run from within RStudio Server.

The app requires some additional R packages to run. If you receive an error about a missing package, please install it with install.packages. To ensure you have all the extra packages installed, you can run the command below:

install.packages(c("faviconPlease", "opencpu", "UpSetR"))

Value

No return value. This function is only called for the side effect of running a local instance of the app.

Description

Displays a tree-like summary of the elements that have been added to an OmicNavigator study.

Usage

## S3 method for class 'onStudy'
summary(object, elements = NULL, ...)

Arguments

Value

Invisibly returns the original onStudy object

Description

Validate a study

Usage

validateStudy(study)

Arguments

Value

For a valid study object, the logical value TRUE is invisibly returned. For an invalid study object, there is no return value because an error is thrown.