| Title: | Explorer for Your Bulk RNA Seq Analyses |
|---|---|
| Description: | Interactive exploration of a Summarized experiment and associated DE analyses. A shiny app to inspect and interact with pre-calculated differential expression and functional enrichment results, and to facilitate comparisons between multiple models. This is meant to be a tool for exploration and discussion, rather than analysis. |
| Authors: | Jasper Spitzer [aut, cre] (ORCID: <https://orcid.org/0000-0001-9696-2092>) |
| Maintainer: | Jasper Spitzer <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.99.12 |
| Built: | 2026-10-09 16:34:47 UTC |
| Source: | https://github.com/bioc/exploreSE |
Example DE results for airway data set
A DESEqResults
This is a DESEqResults object, with the simplest model, using ~dex as
the design. Data is from the airway package, DE results are generated with DESeq2.
A DESEqResults
GO enrichments based on the baseline DE results
A EnrichResults
GO enrichmens are generated with clusterProfiler, filtered on down-regulated
genes only; genes that underwent DE testing were taken as the universe.
The code to create said object can be found in the folder /inst/scripts in
the exploreSE package, the file is called creating_dataset_examples.R.
A EnrichResults object
GO enrichments based on the baseline DE results
A EnrichResults
GO enrichmens are generated with clusterProfiler, filtered on up-regulated
genes only; genes that underwent DE testing were taken as the universe.
The code to create said object can be found in the folder /inst/scripts in
the exploreSE package, the file is called creating_dataset_examples.R.
A EnrichResults object
Example DE results for airway data set
A DESEqResults
This is a DESEqResults object, using ~ cell + dex as the design.
Data is from the airway package, DE results are generated with DESeq2,
using the dex untrt vs trt contrast
A DESEqResults
GO enrichments based on the cell_controlled DE results
A EnrichResults
GO enrichmens are generated with clusterProfiler, filtered on down-regulated
genes only; genes that underwent DE testing were taken as the universe.
The code to create said object can be found in the folder /inst/scripts in
the exploreSE package, the file is called creating_dataset_examples.R.
A EnrichResults object
GO enrichments based on the cell_controlled DE results
A EnrichResults
GO enrichmens are generated with clusterProfiler, filtered on up-regulated
genes only; genes that underwent DE testing were taken as the universe.
The code to create said object can be found in the folder /inst/scripts in
the exploreSE package, the file is called creating_dataset_examples.R.
A EnrichResults object
This runs the explorer app. You can add a path to a .rds file or a the relevant R object as an argument to load it on start-up. The app looks for DE results in the metadata of the metadata(object)$de_results and, if it is a DeeDeeExperiment, in the DEA slots of the object. Similarly, the enrichment results are looked for in the metadata(object)$fe_results or FEA slots (DeeDeeExperiment). As the DeeDeeExperiment structure does a lot of work to clean and standardize the inputs and outputs for both DE and enrichment results, using it is highly recommended.
exploreSE(file = NULL, object = NULL)exploreSE(file = NULL, object = NULL)
file |
a string leading to a file you want loaded as a default |
object |
a summarizedExperiment object (or DeeDeeExperiment) to load in |
an application
app <- exploreSE() if(interactive()){ shiny::runApp(app, port = 1234) }app <- exploreSE() if(interactive()){ shiny::runApp(app, port = 1234) }
A function to calculate GO term enrichments from a given summarizedExperiment
or DeeDee experiment. By default, it calculates separate enrichments for
up- downregulated genes, at the cutoffs of |logFC| > 1 and adjusted p-
value < 0.05. It utilises the clusterProfiler::enrichGO() function.
get.gos(NAME, obj, species = "hs", gene_type = "SYMBOL")get.gos(NAME, obj, species = "hs", gene_type = "SYMBOL")
NAME |
character, name of the DE comparison |
obj |
DeeDeeExperiment or SummarizedExperiment, the object |
species |
character, the species hs/mm |
gene_type |
character, the type of gene identifier |
a DeeDeeExperiment or SummarizedExperiment
library(airway) library(DeeDeeExperiment) data(airway) data("baseline_de", package = "exploreSE") se_with_de <- addDEA(DeeDeeExperiment(airway), baseline) se_with_go <- get.gos(obj = se_with_de, NAME = "baseline", gene_type = "ENSEMBL")library(airway) library(DeeDeeExperiment) data(airway) data("baseline_de", package = "exploreSE") se_with_de <- addDEA(DeeDeeExperiment(airway), baseline) se_with_go <- get.gos(obj = se_with_de, NAME = "baseline", gene_type = "ENSEMBL")
Calculates the GSEA enrichments with the counts within a summarizedExperiment
or DeeDeeExperiment and adds them in the respective slots. It follows
the original signal-to-noise ratio of the method; other approaches for
ranking of genes are available in other implementations,
such as the clusterProfiler::GSEA() implementation which calls for
logFC ranked genes.
get.gsea( NAME, obj, type = "HALLMARK", conditions, species = "hs", condition_var = "condition", gene_type = "SYMBOL" )get.gsea( NAME, obj, type = "HALLMARK", conditions, species = "hs", condition_var = "condition", gene_type = "SYMBOL" )
NAME |
character, name of the DE comparison |
obj |
DeeDeeExperiment or SummarizedExperiment, the object |
type |
character, the type of gene set, HALLMARK or REACTOME |
conditions |
a vector of length 2, which levels of the condition variable are being compared, |
species |
character, supported values are "hs" and "mm" |
condition_var |
symbol or character, the variable name for this comparison |
gene_type |
character, the type of gene identifier |
a DeeDeeExperiment or SummarizedExperiment
library(airway) library(DeeDeeExperiment) data(airway) data("baseline_de", package = "exploreSE") se_with_de <- addDEA(DeeDeeExperiment(airway), baseline) se_with_gsea <- get.gsea(NAME = "baseline", obj = se_with_de, type = "HALLMARK", condition_var = "dex", conditions = c("trt", "untrt"), gene_type = "ENSEMBL")library(airway) library(DeeDeeExperiment) data(airway) data("baseline_de", package = "exploreSE") se_with_de <- addDEA(DeeDeeExperiment(airway), baseline) se_with_gsea <- get.gsea(NAME = "baseline", obj = se_with_de, type = "HALLMARK", condition_var = "dex", conditions = c("trt", "untrt"), gene_type = "ENSEMBL")