Start the happy hour, creating a report containing a document full of goodies derived from the provided objects.
happy_hour(
dds,
res_de,
res_enrich,
annotation_obj,
gtl = NULL,
project_id,
mygenesets,
mygenes,
mygroup = NULL,
usage_mode = "batch_mode",
input_rmd = NULL,
output_file = "my_first_GeneTonic_happyhour.html",
output_dir = tempdir(),
output_format = NULL,
force_overwrite = FALSE,
knitr_show_progress = FALSE,
ignore_pandoc = FALSE,
open_after_creating = TRUE,
...
)A DESeqDataSet object, normally obtained after running your data
through the DESeq2 framework.
A DESeqResults object. As for the dds parameter, this is
also commonly used in the DESeq2 framework.
A data.frame object, storing the result of the functional
enrichment analysis. See GeneTonic() for the formatting requirements.
A data.frame object with the feature annotation
information, with at least two columns, gene_id and gene_name. See
GeneTonic() for the formatting requirements.
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
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()
A vector of character strings, containing the genesets to focus on in the report - for each geneset, e.g. a signature heatmap can be created.
A vector of character strings, containing the genes to focus on in the report - for each gene, the plot of the expression values is included.
A character string, or a vector thereof. Contains the experimental variables to be used to split into groups the expression data, and color accordingly.
A character string, which controls the behavior of the Rmd document, based on whether the rendering is triggered while using the app ("shiny_mode"), or offline, in batch mode. Defaults to "batch_mode".
Character string with the path to the RMarkdown (.Rmd) file
that will be used as the template for generating the report. Defaults to NULL,
which will then use the one provided with the GeneTonic package.
Character string, specifying the file name of the output
report. The file name extension must be either .html or .pdf, and consistent
with the value of output_format.
Character, defining the path to the output directory where
the report will be generated. Defaults to the temp directory (tempdir()).
The format of the output report. Either html_document
or pdf_document. The file name extension of output_file must be consistent
with this choice. Can also be left empty and determined accordingly.
Logical, whether to force overwrite an existing report with the same name in the output directory. Defaults to FALSE.
Logical, whether to display the progress of knitr
while generating the report. Defaults to FALSE.
Logical, controlling how the report generation function
will behave if pandoc or pandoc-citeproc are missing.
Logical, whether to open the report in the default browser after being generated. Defaults to TRUE.
Other arguments that will be passed to rmarkdown::render().
Generates a fully fledged report in the output_dir directory, called
output_file and returns (invisibly) the name of the generated report.
When happy_hour is called, a RMarkdown template file will be copied
into the output directory, and rmarkdown::render() will be called to
generate the final report.
As a default template, happy_hour uses the one delivered together with the
GeneTonic package, which provides a comprehensive overview of what the user
can extract. Experienced users can take that as a starting point to further
edit and customize.
If there is already a .Rmd file with the same name in the output directory, the function will raise an error and stop, to avoid overwriting the existing file. The reason for this behaviour is that the copied template in the output directory will be deleted once the report is generated.
Credits to the original implementation proposed by Charlotte Soneson, upon which this function is heavily inspired.
library("macrophage")
library("DESeq2")
library("org.Hs.eg.db")
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)
if (FALSE) { # \dontrun{
happy_hour(
dds = dds_macrophage,
res_de = res_de,
res_enrich = res_enrich,
annotation_obj = anno_df,
project_id = "examplerun",
mygroup = "condition",
# mygroup = "line", # alternatively
mygenesets = res_enrich$gs_id[c(1:5, 11, 31)],
mygenes = c(
"ENSG00000125347",
"ENSG00000172399",
"ENSG00000137496"
)
)
} # }