GeneToniccan now accept the input of
clusterProfiler’s gene set enrichment analysis functions (
GSEA), as implemented in the
GeneTonicis now available on bioRxiv at https://www.biorxiv.org/content/10.1101/2021.05.19.444862v1 - the citation item has been updated accordingly
GeneTonic’s Shiny app now uses the latest version of
bs4Dash, which introduced some breaking changes. Most elements should be now available as they were in the original implementation
The main function
GeneTonic() gains an extra parameter,
gtl - this can be used to provided a named list object where a single parameter is passed (e.g. after loading in a single serialized object), while the functionality stays unaltered. The same
gtl parameter is also exposed in other functions of the package - see the vignette for some examples, or check the documentation of each specific function. To create this object in a standardized manner, the function
GeneTonic_list() is now available.
A new function to perform fuzzy clustering (following the implementation of DAVID) is added - see
gs_fuzzyclustering(). It returns a table with additional information on the cluster of genesets and the status of each set in the group.
ggs_backbone() function can extract the bipartite graph backbone from the Gene-Geneset graph, this can be further explored below the main element in the Gene-Geneset panel. Once the backbone is created, you are one step away from checking out the genes that act as “hubs” in the Gene-Geneset graph, and possibly identify the nodes playing an essential role based on their connectivity.
A new function,
signature_volcano(), adds a signature volcano plot to the Gene-Geneset panel. This plot displays the genes of a chosen geneset in color, while the remaining genes of the data are shown as shaded dots in the background. The color and transparency of the displayed genes can be chosen by the user, as well as the option to display the gene names of all genes in the geneset.
gs_summary_overview() can also generate bar plots instead of the default segment-dot (lollipop) plots.
A new function,
summarize_ggs_hubgenes(), builds a DT
datatable for the Gene-Geneset panel. This table lists the individual genes of the input data and their respective degree in the Gene-Geneset graph. Furthermore,
action buttons linking to the NCBI, GeneCards and GTEx databases are included for each gene.
gene_plot() gains the extra
labels_display argument to control whether the labels are at all shown; now the display of the labels is also respecting the jitter of the points
gs_heatmap()has now the possibility to set the arguments to the call to heatmap generating function, via ellipsis
gs_heatmap()handles the colors in a consistent way over the different executions, without relying on the random palettes provided by the
Heatmap’s annotation functionality - could have been misleading if encountering too similar hues are randomly picked
gs_volcano()now always display the line segments for the data points to be labeled (increasing the readability - as “matching back the label to the drawed circle” - thanks for the suggestion!)
The geneset distillery is officially open!
GeneTonic offers functionality to aggregate together gene sets into overarching biological themes, based on a network-based refinement of the enrichment map. Corresponding graphical functionalities are also extended to accommodate meta-genesets. An efficient implementation for the Markov clustering on graph objects is also provided
GeneTonic can now receive the input of many other tools for functional enrichment analysis - this includes the output (text export) of DAVID (
shake_davidResult), enrichr (from website and via the package, with
shake_enrichrResult), fgsea (
shake_fgseaResult), and g:Profiler (with
shake_gprofilerResult, which can handle the textual output from the website, as well the one from the call to the
An export button to a
SummarizedExperiment object for
iSEE and its underlying machinery has been added. If the visualization options in
GeneTonic are not exactly what you would expect, you might find an excellent venue in the
GO.dbpackage, or also mistyped if entered by hand at some point.
gs_heatmaphas a new parameter,
plot_title, to override the title to be displayed and set it to any custom string
export_to_sifenables to export a graph object to a text file, encoded with the SIF format
GeneTonichas become a part of Bioconductor!
GeneTonicis now submitted to Bioconductor!
gs_horizonwere internally rewritten to accept correctly the comparison elements
GeneTonic. Feel free to try them out!
GeneTonicto enable finer control of the output aspect
check_colorsverify that color palettes are correctly provided
GeneTonicis now provided with modal dialog windows, rather than in a separate tab
GeneTonicsports a blazing new hex sticker - say bye to the original draft!
datatables has some styling with color bars - e.g. for DE results - to enhance the visual perception of numeric values (e.g. log2FoldChange)
gs_heatmapcan now take a custom list of gene identifiers (when no geneset is passed)
gs_mdsis now optionally returning a data.frame, to be further used for custom plotting or downstream processing
gs_summary_overviewnow has coloring enabled by the variable of choice
gs_spideris equivalent to
gs_sankeyis equivalent to
GeneTonicnow delivers bundled example objects to make examples and tests slim
gs_volcanocan now plot points by different colors according to the columns of interest
GeneTonichas a fully fledged manual describing its functionality and user interface
gs_dendro()to display distance matrices with some visualization sugar, as an alternative to other methods
gs_idsare exposed to more functions to enable custom subsets of the enrichment results to be inspected
gs_heatmapnow relies on
ComplexHeatmap, to avoid the issues with Shiny of not displaying the outputs in the app, and enabling a comfortable heatmap annotation
gs_ggheatmapgot renamed to
GeneTonicnow enforces a format for
res_enrich, and provides some conversion functions,
shake_*(). Requirements are specified in the documentation, if an appropriate converter does not (yet) exist.
gene_plotcan enforce a plot type overriding the default based on the number of samples per condition
bs4Dashand many of its nice features, replacing the previous implementation based on