GeneTonic

GeneTonic, main function for the Shiny app

GeneTonic(
  dds = NULL,
  res_de = NULL,
  res_enrich = NULL,
  annotation_obj = NULL,
  gtl = NULL,
  project_id = "",
  size_gtl = 50
)

Arguments

dds

A DESeqDataSet object, normally obtained after running your data through the DESeq2 framework.

res_de

A DESeqResults object. As for the dds parameter, this is also commonly used in the DESeq2 framework.

res_enrich

A data.frame object, storing the result of the functional enrichment analysis. Required columns for enjoying the full functionality of GeneTonic() include:

• a gene set identifier (e.g. GeneOntology id, gs_id) and its term description (gs_description)

• a numeric value for the significance of the enrichment (gs_pvalue)

• a column named gs_genes containing a comma separated vector of the gene names associated to the term, one for each term

• the number of genes in the geneset of interest detected as differentially expressed (gs_de_count), or in the background set of genes (gs_bg_count) See shake_topGOtableResult() or shake_enrichResult() for examples of such formatting helpers

annotation_obj

A data.frame object, containing two columns, gene_id with a set of unambiguous identifiers (e.g. ENSEMBL ids) and gene_name, containing e.g. HGNC-based gene symbols. This object can be constructed via the org.eg.XX.db packages, e.g. with convenience functions such as pcaExplorer::get_annotation_orgdb().

gtl

A GeneTonic-list object, containing in its slots the arguments specified above: dds, res_de, res_enrich, and annotation_obj - the names of the list must be specified following the content they are expecting

project_id

A character string, which can be considered as an identifier for the set/session, and will be e.g. used in the title of the report created via happy_hour()

size_gtl

Numeric value, specifying the maximal size in MB for the accepted GeneTonicList object - this applies when uploading the dataset at runtime

Value

A Shiny app object is returned, for interactive data exploration

Author

Federico Marini

Examples

library("macrophage")
library("DESeq2")
#> Loading required package: S4Vectors
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
#>     union, unique, unsplit, which.max, which.min
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomicRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
library("org.Hs.eg.db")
#> Loading required package: AnnotationDbi
library("AnnotationDbi")

# dds object
data("gse", package = "macrophage")
dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)
#> using 'avgTxLength' from assays(dds), correcting for library size

# annotation object
anno_df <- data.frame(
  gene_id = rownames(dds_macrophage),
  gene_name = mapIds(org.Hs.eg.db,
    keys = rownames(dds_macrophage),
    column = "SYMBOL",
    keytype = "ENSEMBL"
  ),
  stringsAsFactors = FALSE,
  row.names = rownames(dds_macrophage)
)
#> 'select()' returned 1:many mapping between keys and columns


# res object
data(res_de_macrophage, package = "GeneTonic")
res_de <- res_macrophage_IFNg_vs_naive

# res_enrich object
data(res_enrich_macrophage, package = "GeneTonic")
res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive)
#> Found 500 gene sets in `topGOtableResult` object.
#> Converting for usage in GeneTonic...
res_enrich <- get_aggrscores(res_enrich, res_de, anno_df)

# now everything is in place to launch the app
if (interactive()) {
  GeneTonic(
    dds = dds_macrophage,
    res_de = res_de,
    res_enrich = res_enrich,
    annotation_obj = anno_df,
    project_id = "myexample"
  )
}
# alternatively...
gtl_macrophage <- GeneTonicList(
  dds = dds_macrophage,
  res_de = res_de,
  res_enrich = res_enrich,
  annotation_obj = anno_df
)
#> ---------------------------------
#> ----- GeneTonicList object ------
#> ---------------------------------
#> 
#> ----- dds object -----
#> Providing an expression object (as DESeqDataset) of 58294 features over 24 samples
#> 
#> ----- res_de object -----
#> Providing a DE result object (as DESeqResults), 17806 features tested, 928 found as DE
#> Upregulated:     599
#> Downregulated:   329
#> 
#> ----- res_enrich object -----
#> Providing an enrichment result object, 500 reported
#> 
#> ----- annotation_obj object -----
#> Providing an annotation object of 58294 features with information on 2 identifier types
# GeneTonic(gtl = gtl_macrophage)

# if running it "as a server", without input data specified:
if (interactive()) {
  GeneTonic(size_gtl = 300)   # for fairly large gtl objects
}