knitr::opts_chunk$set( collapse = TRUE, echo = TRUE, message = FALSE, error = FALSE, comment = "#>", fig.path = "man/figures/README-" )
The FacileBiocData
package enables the use of Bioconductor-standard data
containers, like a SummarizedExperiment
, DGEList
, DESeqDataSet
, etc. as
"first-class" data-providers within the facile ecosystem.
The user simply needs to call the facilitate
function on their data container
in order to make its data available via the facile API, so that it can be
analyzed within the facile framework.
library(FacileBiocData) data("airway", package = "airway") airway.facile <- facilitate(airway, assay_type = "rnaseq")
We can now use airway.facile
as a first-class data-providedr within the facile
framework. For instance, we can use the FacileAnalysis to perform a
differential expression analysis using the edgeR or limma based framework:
library(FacileAnalysis) dge.facile <- airway.facile |> flm_def("dex", numer = "trt", denom = "untrt", batch = "cell") |> fdge(method = "voom")
We can extract the statistics from the fdge
result:
tidy(dge.facile) |> select(feature_id, logFC, pval, padj) |> arrange(pval) |> head()
Produce an interactive visual (via using plotly/htmlwidgets) from one of the
results using viz()
viz(dge.facile, "ENSG00000165995")
Or, finally, launch a shiny gadget over the fdge()
result so that we can
interactively explore the differential expression result in all of its glory:
shine(dge.facile)
You can refer to the RNA-seq analysis vignette vignette in the FacileAnalysis package in order to learn how you can interactively analyze and explore RNA-seq data in the facile.bio framework.
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