# ideal User’s Guide

Package: ideal
Authors: Federico Marini [aut, cre] (ORCID: https://orcid.org/0000-0003-3252-7758)
Version: 2.6.0
Compiled date: 2026-04-29
Last edited: 2019-02-06
License: MIT + file LICENSE

# Getting started

ideal is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:

    if (!requireNamespace("BiocManager", quietly = TRUE)) {
      install.packages("BiocManager")
    }
    BiocManager::install("ideal")

The GitHub repository for ideal is https://github.com/federicomarini/ideal. This is the place to file an issue, report a bug, or provide a pull request.

Once ideal is installed, it can be loaded by the following command.

    library("ideal")

# Introduction

ideal is a Bioconductor package containing a Shiny application for analyzing RNA-Seq data in the context of differential expression. This enables an interactive and at the same time analysis, keeping the functionality accessible, and yet providing a comprehensive selection of graphs and tables to mine the dataset at hand.

ideal is an R package which fully leverages the infrastructure of the Bioconductor project in order to deliver an interactive yet reproducible analysis for the detection of differentially expressed genes in RNA-Seq datasets. Graphs, tables, and interactive HTML reports can be readily exported and shared across collaborators. The dynamic user interface displays a broad level of content and information, subdivided by thematic tasks. All in all, it aims to enforce a proper analysis, by reaching out both life scientists and experienced bioinformaticians, and also fosters the communication between the two sides, offering robust statistical methods and high standard of accessible documentation.

It is structured in a similar way to the pcaExplorer, also designed as an interactive companion tool for RNA-seq analysis focused rather on the exploratory data analysis e.g. using principal components analysis as a main tool.

The interactive/reactive design of the app, with a dynamically generated user interface makes it easy and immediate to apply the gold standard methods (in the current implementation, based on DESeq2) in a way that is information-rich and accessible also to the bench biologist, while also providing additional insight also for the experienced data analyst. Reproducibility is supported via state saving and automated report generation.

## Citation info

If you use ideal for your analysis, please cite it as here below:

    citation("ideal")

    #> Please cite the articles below for the 'ideal' software itself, or its
    #> usage in combined workflows with the 'pcaExplorer' or 'GeneTonic'
    #> software packages:
    #>
    #>   Federico Marini, Jan Linke, Harald Binder (2020). ideal: an
    #>   R/Bioconductor package for Interactive Differential Expression
    #>   Analysis. BMC Bioinformatics, 21, 565,
    #>   <doi:10.1186/s12859-020-03819-5> <doi:10.18129/B9.bioc.ideal>.
    #>
    #>   Annekathrin Ludt, Arsenij Ustjanzew, Harald Binder, Konstantin
    #>   Strauch, Federico Marini (2022). Interactive and Reproducible
    #>   Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer,
    #>   ideal, and GeneTonic. Current Protocols, 2 (4), e411,
    #>   <doi:10.1002/cpz1.411>.
    #>
    #> To see these entries in BibTeX format, use 'print(<citation>,
    #> bibtex=TRUE)', 'toBibtex(.)', or set
    #> 'options(citation.bibtex.max=999)'.

# Using the application

There are different ways to use ideal for interactive differential expression analysis.

## Launching ideal locally

First load the library:

    library("ideal")

and then launch the app with the `ideal` function. This takes the following essential parameters as input:

- `dds_obj` - a `DESeqDataSet` object. If not provided, then a `countmatrix` and a `expdesign` need to be provided. If none of the above is provided, it is possible to upload the data during the execution of the Shiny App
- `res_obj` - a `DESeqResults` object. If not provided, it can be computed during the execution of the application
- `annotation_obj` - a `data.frame` object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, `gene_name`, containing e.g. HGNC-based gene symbols. If not provided, it can be constructed during the execution via the `org.eg.XX.db` packages
- `countmatrix` - a count matrix, with genes as rows and samples as columns. If not provided, it is possible to upload the data during the execution of the Shiny App
- `expdesign` -a `data.frame` containing the info on the experimental covariates of each sample. If not provided, it is possible to upload the data during the execution of the Shiny App

Different modalities are supported to launch the application:

- `ideal(dds_obj = dds, res_obj = res, annotation_obj = anno)`, where the objects are precomputed in the current session and provided as parameters
- `ideal(dds_obj = dds)`, as in the command above, but where the result object is assembled at runtime
- `ideal(countmatrix = countmatrix, expdesign = expdesign)`, where instead of passing the defined `DESeqDataSet` object, its components are given, namely the count matrix (e.g. generated after a run of featureCounts or HTSeq-count) and a data frame with the experimental covariates. The design formula can be constructed interactively at runtime
- `ideal()`, where the count matrix and experimental design can simply be uploaded at runtime, where all the derived objects can be extracted and computed live. These files have to be formatted as tabular text files, and a function in the package tries to guess the separator, based on heuristics of occurrencies per line of commonly used characters

You can obtain more information on the formats that are expected and required to work with ideal by pressing the small round button in Step 1 of the Data Setup panel - these are common to the formats used by pcaExplorer. For the sake of completeness, you can see an overview reported in the image below.

The value to specify for the experimental design is best selected live in Step 2 of the Data Setup panel (see more in Section @ref(datasetup)) - or if passing directly the `dds` object in the call to `ideal`, is already contained in the `dds` itself.

## Accessing the public instance of ideal

To use ideal without installing any additional software, you can access the public instance of the Shiny Server made available at the Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI) in Mainz.

This resource is accessible at this address:

http://shiny.imbei.uni-mainz.de:3838/ideal

## Deploying to a Shiny Server

A deployment-oriented version of the package is available at https://github.com/federicomarini/ideal_serveredition. This repository contains also detailed instruction to setup the running instance of a Shiny Server, where ideal can be run without further installation for the end-users.

Please note that you still need ideal to be installed there once during the setup phase - for this operation, you might require root administrator permissions.

# Getting to know the user interface and the functionality

The user interface is dynamically displayed according to the provided and computed objects, with tabs that are actively usable only once the required input is effectively available.

Moreover, for some relevant UI widgets, the user can receive additional information by hovering over with the mouse, with the functionality powered by the shinyBS package.

For the user which is either new with the app UI/functionality, or not extensively familiar with the topic of differential expression, it is possible to obtain a small guided tour of the App by clicking on the respective help buttons, marked in the app like this - please note that this button is clickable but does not start any tour.

These trigger the start of a step-by-step guide and feature introduction, powered by the rintrojs package.

## The controls sidebar

Some of the input controls which affect different tabs are located in the sidebar, while others are as well in the individual tabs of the app. By changing one or more of the input parameters, the user can get a fine control on what is computed and displayed.

### App settings

- Group/color by - Select the group of samples to stratify the analysis for plotting. Can also assume multiple values.
- Select the gene(s) of interest - ids - Select a subset of genes for deeper analysis. If an annotation object is provided, the user can handily select the genes e.g. based on their HGNC symbol
- False Discovery Rate - Set as default to 0.05, it is the FDR value for the Benjamini-Hochberg procedure for adjusting p-values in the multiple testing comparison scenario

### Plot export settings

Width and Height for the figures to export are input here in cm.

### Quick viewer

This displays a list of the underlying objects with which basically all of the analysis can be performed. A green tick icon appears close to each when the respective component is either provided or calculated. For obtaining the best analysis experience in ideal, it is recommended to provide all of them.

### First steps help

Clicking on this button activated the `intro.js` based tour for getting to know the components and the structure of the app. Dedicated step-by-step procedures are also available in each individual tab.

## The task menu

The task menu, accessible by clicking on the cog icon in the upper right part of the application, provides two functionalities:

- `Exit ideal & save` will close the application and store the content of the `input` and `values` reactive objects in a list of two elements in the `ideal_env` environment, respectively called `ideal_inputs_YYYYMMDD_HHMMSS` and `ideal_values_YYYYMMDD_HHMMSS`
- `Save State as .RData` will similarly store `LiveInputs` and `r_data` in a binary file named `idealState_YYYYMMDD_HHMMSS.Rdata`, without closing the application

# The main app panels

The ideal app is a one-paged dashboard, structured in different panels, where each of them is focused on a different aspect of the data exploration.

On top of the panels, three `valueBox` objects serve as guiding elements for having an overview of the data at hand: how many genes and samples are in the data, how many entries are in the annotation object, and how many genes were found to be differentially expressed in the results. Whenever each of the underlying objects is available, the background color turns from red to green.

For the main analysis, the available panels are described in the following subsections.

## Welcome!

The landing page for the app is also where you might likely be reading this text (otherwise in the package vignette).

## Data Setup

The Data Setup panel is where you can upload or inspect the required inputs for running the app. This builds on the primary idea used by pcaExplorer and extends it with the following aspects:

- the panel structure appears dynamically in three consecutive mandatory steps, marked with color from red to yellow to green, with optional steps in light blue.
- the optional step of retrieving the annotation on the fly relieves the user from the task of composing the `data.frame` in advance, and is based on the widely adopted `org.XX.eg.db` Bioconductor packages.
- when the objects are already passed as parameters, or computed, a brief overview/summary for them is displayed
- to tighten the concert operations between similar tools with different scope (as pcaExplorer and ideal are), the information flow can move from the data exploration to decisions taken at the moment of testing

A diagnostic mean-dispersion plot is also provided in a collapsible element at the bottom of the panel, shown when the `DESeqDataSet` is generated and the `DESeq` command from the `DESeq2` package has been applied.

## Counts Overview

As in pcaExplorer, interactive tables for the raw, normalized, or variance stabilized transformed (preferred over regularized logarithm transformation, because of its speed during the dispersion estimation - especially with larger number of samples) counts are shown in this tab. The user can also generate a sample-to-sample correlation scatter plot with the selected data.

Additionally, ideal has an option to include a filter step at the gene level by removing genes with low absolute or averages low values. After this, it might be possible to have to re-run the analysis in step 3 from the Data Setup panel.

## Extract Results

This tab is an interface for generating the summary tables after testing for DE. It is usually based on the Wald test, as implemented in DESeq2, but when the factor of interest is assuming more than two levels, the user can also perform an ANOVA-like test across the groups with the likelihood ratio test. Options for enabling/disabling automated independent filtering, adding the additional column of unshrunken log2 fold change values (instead of the moderated estimates used by default), as well as using the Independent Hypothesis Weighting (IHW) framework, are provided.

The False Discovery Rate (FDR) can be set from the sidebar panel, and a couple of diagnostic plots, such as the histogram of raw p-values and the distribution of log2fc, are shown below the interactive enhanced version of the table - with clickable elements to link to ENSEMBL database and NCBI website.

## Summary Plots

In this tab an interactive MA plot for the contrast selected in the Extract Results tab is displayed. Clicking on a single gene in the zoomed plot (enabled by brushing in the main plot), it is possible to obtain a boxplot for its expression values, flanked by an overview of information accessed live from the Entrez database. Alternatively, a volcano plot of -log10(p-value) versus log fold change can provide a slightly different perspective. The subset of selected genes are also here presented in static and interactive heatmaps, with the underlying data accessible from the collapsible box element.

## Gene Finder

The functionality in the Gene Finder builds upon the one provided by pcaExplorer, and allows to query up to four genes in the same view, which can here be selected from a dropdown input list which supports autocompletion.

A combined summary table (with both normalized counts and results statistics) is located below an MA plot where the selected genes are marked and annotated on the plot. To avoid repeating this manually, the user can also quickly upload a list of genes as text file (one gene identifier per line), such as members of gene families (e.g. all cytokines, all immunoglobulines, …) or defined by common function (e.g. all housekeeping genes, or others based on any annotation).

## Functional Analysis

The Functional Analysis tab takes the user from the simple lists of DE genes to insight on the affected biological pathways, with three approaches based on the Gene Ontology (GO) databases. This panel of ideal has a slim interface to the following methods for performing functional analysis:

- `limma::goana` for the quick yet standard implementation
- `topGO`, particularly valuable for pruning terms which are topologically less meaningful than their specific nodes
- `goseq`, which accounts for the specific length bias intrinsic in RNA-Seq assays (longer genes have higher chances of being called DE).

ideal allows the user to work simultaneously with more gene lists, two of which can be uploaded in a custom way (e.g. list of gene families, or extracted from other existing publications).

The interaction among these lists can be visually represented in Venn diagrams, as well as with the appealing alternative from the UpSetR package, where all combination of sets are explicitly shown.

Each of the methods for GO enrichment delivers its own interactive `DT`-based table, which can then be explored interactively with the display of a heatmap for all the (DE) genes annotated to a particular term, picking the normalized transformed values for comparing robustly the expression values. This is simply triggered by clicking any of the rows for the results tables. Another useful feature is provided by the clickable link to the AmiGO database on each of the GO term identifiers.

## Signatures Explorer

The Signatures Explorer tab allows the user to check the behavior of a number of provided gene signatures in the data at hand, displaying this as a heatmap.

This panel is composed by different well panels:

- in the Setup Options, you can select and upload a gene signature file, in `gmt` format (e.g. like the ones provided in the MSigDB database, or from WikiPathways), and quickly compute the variance stabilized transformed version of your data, which is more amenable for visualization than raw or normalized counts

- in the Conversion options tab, you can create an annotation vector, used to bring the ids from your data and the ids the `gmt` used for encoding the signature elements. This works based on the `org.XX.eg.db` packages.

- the lower well panels control the appearance of the heatmap, also with an option to display all genes annotated in that pathway, or only the ones detected as differentially expressed (for this you need to provide or compute the result object)

## Report Editor

The Report Editor tab works in the same way of pcaExplorer, with the scope of providing an interface to full computational reproducibility of the analyses.

General `Markdown options` and `Editor options` are available, and the text editor, based on the `shinyAce` package, contains a comprehensive template report, that can be edited to the best convenience of the user.

The code contained in the template report fetches the latest state of the reactive values in the ongoing session, and its output is a comprehensive HTML file that can be expanded, edited, previewed in the tab itself, downloaded, and shared with a few mouse clicks.

As for pcaExplorer, the functionality to display the report preview is based on `knit2html`, and some elements such as `DataTable` objects might not render correctly. To render them correctly, please install the PhantomJS executable before launching the app. This can be done by using the webshot package and calling `webshot::install_phantomjs()` - HTML widgets will be rendered automatically as screenshots. Alternatively, the more recent webshot2 package uses the headless Chrome browser (via the chromote package, requiring Google Chrome or other Chromium-based browser). Keep in mind that the fully rendered report (the one you can obtain with the “Generate & Save” button) is not affected by this, since it uses `rmarkdown::render()`.

## About

The About tab contains the output of `sessionInfo`, plus general information on ideal, including the link to the Github development version. If requested, the modular structure of the app can be easily expanded, and many new operations on the same set of input data and derived results can be embedded in the same framework.

# Running ideal on an exemplary data set

We can run ideal for demonstration purpose on published datasets that are available as SummarizedExperiment in an experiment Bioconductor packages.

We will use the airway dataset, which can be installed with this command:

    if (!requireNamespace("BiocManager", quietly = TRUE)) {
      install.packages("BiocManager")
    }
    BiocManager::install("airway")

This package provides a `RangedSummarizedExperiment` object of read counts in genes for an RNA-Seq experiment on four human airway smooth muscle cell lines treated with dexamethasone. More details such as gene models and count quantifications can be found in the airway package vignette.

To run ideal on this dataset, the following commands are required. First, prepare the objects to be passed as parameters of ideal.

    library("airway")
    library("DESeq2")

    data("airway", package = "airway")

    dds_airway <- DESeqDataSet(airway, design = ~ cell + dex)
    dds_airway

    #> class: DESeqDataSet
    #> dim: 63677 8
    #> metadata(2): '' version
    #> assays(1): counts
    #> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
    #>   ENSG00000273493
    #> rowData names(10): gene_id gene_name ... seq_coord_system symbol
    #> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
    #> colData names(9): SampleName cell ... Sample BioSample

    # run deseq on it
    dds_airway <- DESeq(dds_airway)
    # extract the results
    res_airway <- results(dds_airway, contrast = c("dex", "trt", "untrt"), alpha = 0.05)

Then launch the app itself.

    ideal(dds_obj = dds_airway)
    # or also providing the results object
    ideal(dds_obj = dds_airway, res_obj = res_airway)

The `annotation` for this dataset can be built manually by exploiting the org.Hs.eg.db package.

    library(org.Hs.eg.db)
    genenames_airway <- mapIds(org.Hs.eg.db, keys = rownames(dds_airway), column = "SYMBOL", keytype = "ENSEMBL")
    annotation_airway <- data.frame(
      gene_id = rownames(dds_airway),
      gene_name = genenames_airway,
      row.names = rownames(dds_airway),
      stringsAsFactors = FALSE
    )
    head(annotation_airway)

    #>                         gene_id gene_name
    #> ENSG00000000003 ENSG00000000003    TSPAN6
    #> ENSG00000000005 ENSG00000000005      TNMD
    #> ENSG00000000419 ENSG00000000419      DPM1
    #> ENSG00000000457 ENSG00000000457     SCYL3
    #> ENSG00000000460 ENSG00000000460     FIRRM
    #> ENSG00000000938 ENSG00000000938       FGR

or alternatively, can be handily created at runtime in the optional step.

Then again, the app can be launched with:

    ideal(
      dds_obj = dds_airway,
      annotation_obj = annotation_airway
    )

If desired, alternatives can be used. See the well written annotation workflow available at the Bioconductor site (https://bioconductor.org/help/workflows/annotation/annotation/).

## Coming from `edgeR`/`limma-voom`

Let’s suppose you performed part of your analysis with edgeR or limma/`voom` and you want to continue inspecting your data interactively using ideal. You can use the functionality provided by the DEFormats package to convert the object with:

    library(DEFormats)
    library(edgeR)

    #> Loading required package: limma

    #>
    #> Attaching package: 'limma'

    #> The following object is masked from 'package:DESeq2':
    #>
    #>     plotMA

    #> The following object is masked from 'package:BiocGenerics':
    #>
    #>     plotMA

    #>
    #> Attaching package: 'edgeR'

    #> The following object is masked from 'package:DEFormats':
    #>
    #>     DGEList

    library(limma)
    dge_airway <- as.DGEList(dds_airway) # this is your initial object
    # your factors for the design:
    dex <- colData(dds_airway)$dex
    cell <- colData(dds_airway)$cell

    redo_dds_airway <- as.DESeqDataSet(dge_airway)
    # force the design to ~cell + dex
    design(redo_dds_airway) <- ~ cell + group # TODO: this is due to the not 100% re-conversion via DEFormats

    ### with edgeR
    y <- calcNormFactors(dge_airway)

    #> calcNormFactors has been renamed to normLibSizes

    design <- model.matrix(~ cell + dex)
    y <- estimateDisp(y, design)
    # If you performed quasi-likelihood F-tests
    fit <- glmQLFit(y, design)
    qlf <- glmQLFTest(fit) # contrast for dexamethasone treatment
    topTags(qlf)

    #> Coefficient:  dexuntrt
    #>                                                                                                              seqnames
    #> ENSG00000109906             11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11
    #> ENSG00000165995 10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10
    #> ENSG00000250978                                                                                         5,5,5,5,5,5,5
    #> ENSG00000168309                                                                   3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3
    #> ENSG00000152583                         4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
    #> ENSG00000120129                                                                                               5,5,5,5
    #> ENSG00000189221                                             X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X
    #> ENSG00000146250                                                                                             6,6,6,6,6
    #> ENSG00000171819                                                                                       1,1,1,1,1,1,1,1
    #> ENSG00000157214                                                 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7
    #>                                                                                                                                                                                                                                                                                                                                                                          start
    #> ENSG00000109906                                                    113930315,113930447,113930459,113930979,113931229,113932093,113933933,113933933,113933933,113933933,113935134,113992540,114027059,114027059,114051488,114057674,114057674,114057674,114107929,114112889,114112889,114117512,114117920,114117920,114117920,114117920,114121048,114121048,114121048,114121048
    #> ENSG00000165995                                              18429606,18429666,18429676,18429721,18429887,18430109,18439812,18439812,18439812,18470736,18549698,18550190,18629646,18629666,18689742,18690853,18690853,18787284,18787284,18789741,18795400,18803165,18803440,18803909,18807265,18807839,18816517,18823005,18825030,18827109,18828159,18828159,18828159,18828159
    #> ENSG00000250978                                                                                                                                                                                                                                                                                                 66759637,66760863,66761082,66762701,66763734,66771243,66771243
    #> ENSG00000168309                                                                                                                                                                                              58549844,58551188,58551188,58551579,58552935,58552946,58555418,58555418,58556053,58563036,58563036,58572585,58574933,58574933,58594601,58612698,58612698,58613143
    #> ENSG00000152583 88394487,88394500,88394570,88400582,88401524,88403576,88411425,88411912,88412770,88414734,88414734,88415521,88415521,88415528,88415569,88415592,88415685,88416133,88416133,88416133,88416165,88418283,88420673,88420673,88425974,88449278,88449707,88449707,88450197,88450197,88450197,88450197,88450197,88450197,88450197,88450197,88451697,88452130,88452130
    #> ENSG00000120129                                                                                                                                                                                                                                                                                                                        172195093,172196578,172197164,172197589
    #> ENSG00000189221                                                                                           43515467,43515548,43515550,43517110,43542761,43542761,43552538,43552538,43571119,43571119,43571119,43571952,43571952,43587420,43587420,43590488,43590488,43590941,43591946,43595474,43599928,43601197,43602951,43603041,43603356,43603356,43603614,43603614,43603614
    #> ENSG00000146250                                                                                                                                                                                                                                                                                                                   84222194,84222257,84231254,84233141,84233141
    #> ENSG00000171819                                                                                                                                                                                                                                                                                        11249398,11252327,11253637,11253684,11254518,11254583,11254911,11254911
    #> ENSG00000157214                                                                                                             89796904,89841000,89841058,89841174,89841187,89841200,89841245,89841590,89843134,89844588,89845805,89845809,89854364,89854364,89854364,89856285,89856285,89856285,89859186,89861651,89861651,89861651,89861651,89866205,89866205,89866205,89867413
    #>                                                                                                                                                                                                                                                                                                                                                                            end
    #> ENSG00000109906                                                    113930604,113930604,113930604,113931016,113931306,113932283,113934368,113934466,113935290,113935290,113935290,113992636,114027156,114027156,114051664,114057760,114057760,114060486,114108062,114113059,114113059,114118087,114118018,114118066,114118087,114118087,114121198,114121279,114121374,114121398
    #> ENSG00000165995                                              18429785,18429785,18429785,18429785,18430144,18430144,18439904,18439904,18440198,18471036,18550246,18550246,18629906,18629906,18690029,18690972,18690972,18787406,18787722,18789877,18795476,18803298,18803459,18803970,18807345,18807897,18816626,18823156,18825125,18827294,18828653,18830038,18830056,18830798
    #> ENSG00000250978                                                                                                                                                                                                                                                                                                 66759788,66761155,66761155,66762766,66763881,66771305,66771420
    #> ENSG00000168309                                                                                                                                                                                              58552422,58552422,58553091,58552422,58553091,58553091,58555592,58555592,58556150,58563115,58563491,58572840,58574996,58574996,58594984,58613147,58613337,58613337
    #> ENSG00000152583 88394955,88394955,88394955,88400730,88401672,88403712,88411545,88412030,88412842,88415750,88415750,88415750,88415750,88415750,88415750,88415750,88415750,88416279,88416279,88416295,88416279,88418391,88420737,88420737,88426075,88449385,88449766,88449771,88450244,88450253,88450393,88450524,88450556,88450609,88450655,88450656,88451783,88452201,88452213
    #> ENSG00000120129                                                                                                                                                                                                                                                                                                                        172196135,172196797,172197309,172198198
    #> ENSG00000189221                                                                                           43515662,43515662,43515662,43517217,43542855,43542855,43552675,43552675,43571223,43571223,43571223,43572043,43572043,43587561,43587561,43590629,43590637,43591100,43592042,43595527,43599985,43601294,43603152,43603152,43603418,43603418,43603905,43604409,43606068
    #> ENSG00000146250                                                                                                                                                                                                                                                                                                                   84222413,84222413,84231358,84235419,84235423
    #> ENSG00000171819                                                                                                                                                                                                                                                                                        11250012,11252427,11253831,11253831,11254716,11254716,11255235,11256038
    #> ENSG00000157214                                                                                                             89797227,89841359,89841359,89841248,89841359,89841359,89841359,89841650,89843174,89845917,89845917,89845917,89854660,89854888,89854888,89856337,89856347,89856812,89859350,89861798,89861938,89866908,89866945,89866922,89866945,89866992,89867451
    #>                                                                                                                                                              width
    #> ENSG00000109906                                 290,158,146,38,78,191,436,534,1358,1358,157,97,98,98,177,87,87,2813,134,171,171,576,99,147,168,168,151,232,327,351
    #> ENSG00000165995                    180,120,110,65,258,36,93,93,387,301,549,57,261,241,288,120,120,123,439,137,77,134,20,62,81,59,110,152,96,186,495,1880,1898,2640
    #> ENSG00000250978                                                                                                                           152,293,74,66,148,63,178
    #> ENSG00000168309                                                                             2579,1235,1904,844,157,146,175,175,98,80,456,256,64,64,384,450,640,195
    #> ENSG00000152583 469,456,386,149,149,137,121,119,73,1017,1017,230,230,223,182,159,66,147,147,163,115,109,65,65,102,108,60,65,48,57,197,328,360,413,459,460,87,72,84
    #> ENSG00000120129                                                                                                                                   1043,220,146,610
    #> ENSG00000189221                                         196,115,113,108,95,95,138,138,105,105,105,92,92,142,142,142,150,160,97,54,58,98,202,112,63,63,292,796,2455
    #> ENSG00000146250                                                                                                                              220,157,105,2279,2283
    #> ENSG00000171819                                                                                                                   615,101,195,148,199,134,325,1128
    #> ENSG00000157214                                           324,360,302,75,173,160,115,61,41,1330,113,109,297,525,525,53,63,528,165,148,288,5258,5295,718,741,788,39
    #>                                                                                        strand
    #> ENSG00000109906                   +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000165995           +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000250978                                                                 -,-,-,-,-,-,-
    #> ENSG00000168309                                           -,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-
    #> ENSG00000152583 -,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-
    #> ENSG00000120129                                                                       -,-,-,-
    #> ENSG00000189221                     +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000146250                                                                     +,+,+,+,+
    #> ENSG00000171819                                                               +,+,+,+,+,+,+,+
    #> ENSG00000157214                         +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #>                                                                                                                                                                                                                                                                                          exon_id
    #> ENSG00000109906                                                                360958,360959,360960,360961,360962,360963,360964,360965,360967,360966,360968,360969,360971,360970,360972,360974,360973,360975,360976,360977,360978,360979,360980,360981,360982,360983,360984,360985,360986,360987
    #> ENSG00000165995                                    321791,321792,321793,321794,321795,321796,321797,321798,321799,321800,321801,321802,321803,321804,321805,321806,321807,321808,321809,321810,321811,321812,321813,321814,321815,321816,321817,321818,321819,321820,321821,321822,321823,321824
    #> ENSG00000250978                                                                                                                                                                                                                                 199128,199130,199131,199132,199133,199134,199135
    #> ENSG00000168309                                                                                                                                                    140728,140729,140730,140731,140732,140733,140734,140735,140736,140737,140738,140739,140741,140740,140742,140743,140744,140745
    #> ENSG00000152583 172689,172690,172691,172692,172693,172694,172695,172696,172697,172699,172698,172700,172701,172702,172703,172704,172705,172707,172706,172708,172709,172710,172711,172712,172713,172714,172715,172716,172717,172718,172719,172720,172721,172722,172723,172724,172725,172726,172727
    #> ENSG00000120129                                                                                                                                                                                                                                                      207637,207638,207639,207640
    #> ENSG00000189221                                                                       653982,653983,653984,653985,653986,653987,653989,653988,653990,653991,653992,653993,653994,653995,653996,653997,653998,653999,654000,654001,654002,654003,654004,654005,654006,654007,654008,654009,654010
    #> ENSG00000146250                                                                                                                                                                                                                                               218407,218408,218409,218410,218411
    #> ENSG00000171819                                                                                                                                                                                                                                          2221,2222,2223,2224,2225,2226,2227,2228
    #> ENSG00000157214                                                                                     246728,246730,246731,246732,246733,246734,246735,246736,246737,246738,246739,246740,246741,246743,246742,246744,246745,246746,246747,246748,246749,246750,246751,246752,246753,246754,246755
    #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       exon_name
    #> ENSG00000109906                                                                                                                                                 ENSE00001344383,ENSE00002217296,ENSE00002239785,ENSE00002278969,ENSE00001777728,ENSE00002277816,ENSE00002264739,ENSE00002267450,ENSE00003532715,ENSE00003522546,ENSE00002223922,ENSE00002239280,ENSE00003606532,ENSE00003503866,ENSE00002304588,ENSE00003667676,ENSE00003652618,ENSE00002237247,ENSE00002308595,ENSE00003574983,ENSE00003648066,ENSE00002216844,ENSE00002254046,ENSE00002202712,ENSE00003533854,ENSE00003675344,ENSE00002251269,ENSE00002289800,ENSE00002238115,ENSE00001513888
    #> ENSG00000165995                                                                                 ENSE00001552836,ENSE00001323316,ENSE00001912501,ENSE00001877551,ENSE00001563168,ENSE00001473561,ENSE00003566244,ENSE00003685211,ENSE00001824310,ENSE00001935997,ENSE00001473551,ENSE00001696833,ENSE00001402115,ENSE00001811759,ENSE00001473523,ENSE00003471380,ENSE00003510985,ENSE00001005446,ENSE00001886774,ENSE00001005448,ENSE00001005464,ENSE00001005450,ENSE00001367687,ENSE00001385143,ENSE00001098801,ENSE00001098797,ENSE00001098795,ENSE00001098805,ENSE00001737387,ENSE00001098799,ENSE00001266433,ENSE00001846919,ENSE00001548772,ENSE00001552865
    #> ENSG00000250978                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00002085457,ENSE00002055881,ENSE00002051029,ENSE00002080371,ENSE00002028106,ENSE00002048405,ENSE00002074015
    #> ENSG00000168309                                                                                                                                                                                                                                                                                                                                                 ENSE00001419495,ENSE00001913711,ENSE00001833888,ENSE00001891124,ENSE00001228325,ENSE00001929708,ENSE00003652265,ENSE00003666445,ENSE00001518622,ENSE00001876866,ENSE00001518624,ENSE00001736715,ENSE00003684332,ENSE00003505632,ENSE00001872799,ENSE00001849960,ENSE00001901828,ENSE00001854286
    #> ENSG00000152583 ENSE00001914146,ENSE00001135423,ENSE00002056886,ENSE00001175672,ENSE00001175678,ENSE00001175684,ENSE00001175691,ENSE00001006057,ENSE00001006053,ENSE00003651115,ENSE00003561541,ENSE00003487906,ENSE00003598972,ENSE00001786211,ENSE00002316459,ENSE00002028083,ENSE00001789990,ENSE00003602905,ENSE00003459209,ENSE00002234840,ENSE00001678790,ENSE00002045268,ENSE00003592507,ENSE00003613792,ENSE00001605148,ENSE00002310772,ENSE00002225747,ENSE00002211631,ENSE00002063937,ENSE00002061881,ENSE00002270604,ENSE00002252787,ENSE00002269371,ENSE00001175712,ENSE00001603831,ENSE00001942085,ENSE00002020252,ENSE00002083738,ENSE00002034086
    #> ENSG00000120129                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00001876542,ENSE00000769464,ENSE00000769465,ENSE00000812856
    #> ENSG00000189221                                                                                                                                                                 ENSE00001415055,ENSE00002218265,ENSE00001834008,ENSE00001836223,ENSE00003501661,ENSE00003656032,ENSE00003691177,ENSE00003550793,ENSE00003487993,ENSE00003504764,ENSE00003590154,ENSE00003495252,ENSE00003524833,ENSE00003526715,ENSE00003672501,ENSE00001811266,ENSE00001383783,ENSE00001382812,ENSE00001377890,ENSE00001373498,ENSE00001369566,ENSE00001677475,ENSE00001816354,ENSE00001614970,ENSE00003583432,ENSE00003609031,ENSE00001875207,ENSE00002311052,ENSE00001389556
    #> ENSG00000146250                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00002225101,ENSE00001450675,ENSE00002300058,ENSE00002228069,ENSE00001450674
    #> ENSG00000171819                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00001471825,ENSE00001168153,ENSE00001168146,ENSE00001948794,ENSE00001168140,ENSE00001823259,ENSE00001841505,ENSE00001471787
    #> ENSG00000157214                                                                                                                                                                                                 ENSE00001600835,ENSE00001206871,ENSE00001519027,ENSE00001519061,ENSE00001622820,ENSE00002226827,ENSE00001547971,ENSE00001519060,ENSE00001519048,ENSE00001897780,ENSE00001519046,ENSE00001620946,ENSE00001752071,ENSE00003616543,ENSE00003474395,ENSE00001638606,ENSE00001938466,ENSE00001031278,ENSE00001275182,ENSE00001519045,ENSE00001519014,ENSE00001918233,ENSE00001845894,ENSE00003246458,ENSE00001779097,ENSE00001519026,ENSE00001553338
    #>                     logFC   logCPM         F       PValue          FDR
    #> ENSG00000109906 -7.133909 4.143740 1520.8725 2.256923e-13 1.437141e-08
    #> ENSG00000165995 -3.275295 4.504348 1242.4595 1.003982e-12 3.196527e-08
    #> ENSG00000250978 -6.158313 1.348282  397.6951 3.951524e-12 8.387374e-08
    #> ENSG00000168309 -4.694869 2.775385  730.2343 6.005459e-12 8.675542e-08
    #> ENSG00000152583 -4.547624 5.528678  876.1696 6.812147e-12 8.675542e-08
    #> ENSG00000120129 -2.930539 7.308257  757.3110 1.510161e-11 1.602709e-07
    #> ENSG00000189221 -3.324127 6.766776  710.0722 2.146692e-11 1.821785e-07
    #> ENSG00000146250  2.764773 3.909086  701.9883 2.288783e-11 1.821785e-07
    #> ENSG00000171819 -5.699126 3.500283  500.6621 3.209452e-11 2.009954e-07
    #> ENSG00000157214 -1.961771 7.129056  645.2310 3.618657e-11 2.009954e-07

    # If you performed likelihood ratio tests
    fit <- glmFit(y, design)
    lrt <- glmLRT(fit)
    topTags(lrt)

    #> Coefficient:  dexuntrt
    #>                                                                                                              seqnames
    #> ENSG00000109906             11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11
    #> ENSG00000165995 10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10
    #> ENSG00000152583                         4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
    #> ENSG00000171819                                                                                       1,1,1,1,1,1,1,1
    #> ENSG00000120129                                                                                               5,5,5,5
    #> ENSG00000189221                                             X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X
    #> ENSG00000162692                                                                       1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
    #> ENSG00000168309                                                                   3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3
    #> ENSG00000101347                      20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20
    #> ENSG00000127954                                                                                   7,7,7,7,7,7,7,7,7,7
    #>                                                                                                                                                                                                                                                                                                                                                                          start
    #> ENSG00000109906                                                    113930315,113930447,113930459,113930979,113931229,113932093,113933933,113933933,113933933,113933933,113935134,113992540,114027059,114027059,114051488,114057674,114057674,114057674,114107929,114112889,114112889,114117512,114117920,114117920,114117920,114117920,114121048,114121048,114121048,114121048
    #> ENSG00000165995                                              18429606,18429666,18429676,18429721,18429887,18430109,18439812,18439812,18439812,18470736,18549698,18550190,18629646,18629666,18689742,18690853,18690853,18787284,18787284,18789741,18795400,18803165,18803440,18803909,18807265,18807839,18816517,18823005,18825030,18827109,18828159,18828159,18828159,18828159
    #> ENSG00000152583 88394487,88394500,88394570,88400582,88401524,88403576,88411425,88411912,88412770,88414734,88414734,88415521,88415521,88415528,88415569,88415592,88415685,88416133,88416133,88416133,88416165,88418283,88420673,88420673,88425974,88449278,88449707,88449707,88450197,88450197,88450197,88450197,88450197,88450197,88450197,88450197,88451697,88452130,88452130
    #> ENSG00000171819                                                                                                                                                                                                                                                                                        11249398,11252327,11253637,11253684,11254518,11254583,11254911,11254911
    #> ENSG00000120129                                                                                                                                                                                                                                                                                                                        172195093,172196578,172197164,172197589
    #> ENSG00000189221                                                                                           43515467,43515548,43515550,43517110,43542761,43542761,43552538,43552538,43571119,43571119,43571119,43571952,43571952,43587420,43587420,43590488,43590488,43590941,43591946,43595474,43599928,43601197,43602951,43603041,43603356,43603356,43603614,43603614,43603614
    #> ENSG00000162692                                                                                                                                                                                                101185298,101185311,101185316,101185320,101186032,101186032,101188576,101190180,101194663,101196754,101197974,101200058,101200731,101203679,101203679,101203679
    #> ENSG00000168309                                                                                                                                                                                              58549844,58551188,58551188,58551579,58552935,58552946,58555418,58555418,58556053,58563036,58563036,58572585,58574933,58574933,58594601,58612698,58612698,58613143
    #> ENSG00000101347                                                                                                             35518632,35525971,35526225,35526843,35532560,35532560,35533767,35533767,35539621,35540751,35540864,35545125,35545352,35547767,35555585,35555585,35559163,35559163,35563432,35563432,35563744,35569442,35569442,35575141,35575141,35579839,35579839
    #> ENSG00000127954                                                                                                                                                                                                                                                                      87905744,87907343,87910230,87910798,87911956,87913129,87920231,87936107,87936107,87936107
    #>                                                                                                                                                                                                                                                                                                                                                                            end
    #> ENSG00000109906                                                    113930604,113930604,113930604,113931016,113931306,113932283,113934368,113934466,113935290,113935290,113935290,113992636,114027156,114027156,114051664,114057760,114057760,114060486,114108062,114113059,114113059,114118087,114118018,114118066,114118087,114118087,114121198,114121279,114121374,114121398
    #> ENSG00000165995                                              18429785,18429785,18429785,18429785,18430144,18430144,18439904,18439904,18440198,18471036,18550246,18550246,18629906,18629906,18690029,18690972,18690972,18787406,18787722,18789877,18795476,18803298,18803459,18803970,18807345,18807897,18816626,18823156,18825125,18827294,18828653,18830038,18830056,18830798
    #> ENSG00000152583 88394955,88394955,88394955,88400730,88401672,88403712,88411545,88412030,88412842,88415750,88415750,88415750,88415750,88415750,88415750,88415750,88415750,88416279,88416279,88416295,88416279,88418391,88420737,88420737,88426075,88449385,88449766,88449771,88450244,88450253,88450393,88450524,88450556,88450609,88450655,88450656,88451783,88452201,88452213
    #> ENSG00000171819                                                                                                                                                                                                                                                                                        11250012,11252427,11253831,11253831,11254716,11254716,11255235,11256038
    #> ENSG00000120129                                                                                                                                                                                                                                                                                                                        172196135,172196797,172197309,172198198
    #> ENSG00000189221                                                                                           43515662,43515662,43515662,43517217,43542855,43542855,43552675,43552675,43571223,43571223,43571223,43572043,43572043,43587561,43587561,43590629,43590637,43591100,43592042,43595527,43599985,43601294,43603152,43603152,43603418,43603418,43603905,43604409,43606068
    #> ENSG00000162692                                                                                                                                                                                                101185480,101185480,101185480,101185480,101186121,101186307,101188896,101190446,101194938,101197074,101198240,101200324,101204595,101204169,101204594,101204601
    #> ENSG00000168309                                                                                                                                                                                              58552422,58552422,58553091,58552422,58553091,58553091,58555592,58555592,58556150,58563115,58563491,58572840,58574996,58574996,58594984,58613147,58613337,58613337
    #> ENSG00000101347                                                                                                             35521469,35526362,35526362,35526947,35532652,35532652,35533797,35533906,35539736,35540955,35540955,35545233,35545452,35547922,35555655,35555655,35559278,35559278,35563592,35563592,35563806,35569514,35569514,35575207,35575207,35580111,35580246
    #> ENSG00000127954                                                                                                                                                                                                                                                                      87908943,87908943,87910394,87912483,87912483,87913586,87920329,87936195,87936203,87936206
    #>                                                                                                                                                              width
    #> ENSG00000109906                                 290,158,146,38,78,191,436,534,1358,1358,157,97,98,98,177,87,87,2813,134,171,171,576,99,147,168,168,151,232,327,351
    #> ENSG00000165995                    180,120,110,65,258,36,93,93,387,301,549,57,261,241,288,120,120,123,439,137,77,134,20,62,81,59,110,152,96,186,495,1880,1898,2640
    #> ENSG00000152583 469,456,386,149,149,137,121,119,73,1017,1017,230,230,223,182,159,66,147,147,163,115,109,65,65,102,108,60,65,48,57,197,328,360,413,459,460,87,72,84
    #> ENSG00000171819                                                                                                                   615,101,195,148,199,134,325,1128
    #> ENSG00000120129                                                                                                                                   1043,220,146,610
    #> ENSG00000189221                                         196,115,113,108,95,95,138,138,105,105,105,92,92,142,142,142,150,160,97,54,58,98,202,112,63,63,292,796,2455
    #> ENSG00000162692                                                                                    183,170,165,161,90,276,321,267,276,321,267,267,3865,491,916,923
    #> ENSG00000168309                                                                             2579,1235,1904,844,157,146,175,175,98,80,456,256,64,64,384,450,640,195
    #> ENSG00000101347                                                  2838,392,138,105,93,93,31,140,116,205,92,109,101,156,71,71,116,116,161,161,63,73,73,67,67,273,408
    #> ENSG00000127954                                                                                                            3200,1601,165,1686,528,458,99,89,97,100
    #>                                                                                        strand
    #> ENSG00000109906                   +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000165995           +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000152583 -,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-
    #> ENSG00000171819                                                               +,+,+,+,+,+,+,+
    #> ENSG00000120129                                                                       -,-,-,-
    #> ENSG00000189221                     +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000162692                                               +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
    #> ENSG00000168309                                           -,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-
    #> ENSG00000101347                         -,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-
    #> ENSG00000127954                                                           -,-,-,-,-,-,-,-,-,-
    #>                                                                                                                                                                                                                                                                                          exon_id
    #> ENSG00000109906                                                                360958,360959,360960,360961,360962,360963,360964,360965,360967,360966,360968,360969,360971,360970,360972,360974,360973,360975,360976,360977,360978,360979,360980,360981,360982,360983,360984,360985,360986,360987
    #> ENSG00000165995                                    321791,321792,321793,321794,321795,321796,321797,321798,321799,321800,321801,321802,321803,321804,321805,321806,321807,321808,321809,321810,321811,321812,321813,321814,321815,321816,321817,321818,321819,321820,321821,321822,321823,321824
    #> ENSG00000152583 172689,172690,172691,172692,172693,172694,172695,172696,172697,172699,172698,172700,172701,172702,172703,172704,172705,172707,172706,172708,172709,172710,172711,172712,172713,172714,172715,172716,172717,172718,172719,172720,172721,172722,172723,172724,172725,172726,172727
    #> ENSG00000171819                                                                                                                                                                                                                                          2221,2222,2223,2224,2225,2226,2227,2228
    #> ENSG00000120129                                                                                                                                                                                                                                                      207637,207638,207639,207640
    #> ENSG00000189221                                                                       653982,653983,653984,653985,653986,653987,653989,653988,653990,653991,653992,653993,653994,653995,653996,653997,653998,653999,654000,654001,654002,654003,654004,654005,654006,654007,654008,654009,654010
    #> ENSG00000162692                                                                                                                                                                                  14792,14793,14794,14795,14796,14797,14798,14799,14800,14801,14802,14803,14804,14805,14806,14807
    #> ENSG00000168309                                                                                                                                                    140728,140729,140730,140731,140732,140733,140734,140735,140736,140737,140738,140739,140741,140740,140742,140743,140744,140745
    #> ENSG00000101347                                                                                     626560,626561,626562,626563,626565,626564,626566,626567,626568,626569,626570,626571,626572,626573,626575,626574,626576,626577,626578,626579,626580,626581,626582,626584,626583,626585,626586
    #> ENSG00000127954                                                                                                                                                                                                            263537,263538,263539,263540,263541,263542,263543,263544,263545,263546
    #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       exon_name
    #> ENSG00000109906                                                                                                                                                 ENSE00001344383,ENSE00002217296,ENSE00002239785,ENSE00002278969,ENSE00001777728,ENSE00002277816,ENSE00002264739,ENSE00002267450,ENSE00003532715,ENSE00003522546,ENSE00002223922,ENSE00002239280,ENSE00003606532,ENSE00003503866,ENSE00002304588,ENSE00003667676,ENSE00003652618,ENSE00002237247,ENSE00002308595,ENSE00003574983,ENSE00003648066,ENSE00002216844,ENSE00002254046,ENSE00002202712,ENSE00003533854,ENSE00003675344,ENSE00002251269,ENSE00002289800,ENSE00002238115,ENSE00001513888
    #> ENSG00000165995                                                                                 ENSE00001552836,ENSE00001323316,ENSE00001912501,ENSE00001877551,ENSE00001563168,ENSE00001473561,ENSE00003566244,ENSE00003685211,ENSE00001824310,ENSE00001935997,ENSE00001473551,ENSE00001696833,ENSE00001402115,ENSE00001811759,ENSE00001473523,ENSE00003471380,ENSE00003510985,ENSE00001005446,ENSE00001886774,ENSE00001005448,ENSE00001005464,ENSE00001005450,ENSE00001367687,ENSE00001385143,ENSE00001098801,ENSE00001098797,ENSE00001098795,ENSE00001098805,ENSE00001737387,ENSE00001098799,ENSE00001266433,ENSE00001846919,ENSE00001548772,ENSE00001552865
    #> ENSG00000152583 ENSE00001914146,ENSE00001135423,ENSE00002056886,ENSE00001175672,ENSE00001175678,ENSE00001175684,ENSE00001175691,ENSE00001006057,ENSE00001006053,ENSE00003651115,ENSE00003561541,ENSE00003487906,ENSE00003598972,ENSE00001786211,ENSE00002316459,ENSE00002028083,ENSE00001789990,ENSE00003602905,ENSE00003459209,ENSE00002234840,ENSE00001678790,ENSE00002045268,ENSE00003592507,ENSE00003613792,ENSE00001605148,ENSE00002310772,ENSE00002225747,ENSE00002211631,ENSE00002063937,ENSE00002061881,ENSE00002270604,ENSE00002252787,ENSE00002269371,ENSE00001175712,ENSE00001603831,ENSE00001942085,ENSE00002020252,ENSE00002083738,ENSE00002034086
    #> ENSG00000171819                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00001471825,ENSE00001168153,ENSE00001168146,ENSE00001948794,ENSE00001168140,ENSE00001823259,ENSE00001841505,ENSE00001471787
    #> ENSG00000120129                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00001876542,ENSE00000769464,ENSE00000769465,ENSE00000812856
    #> ENSG00000189221                                                                                                                                                                 ENSE00001415055,ENSE00002218265,ENSE00001834008,ENSE00001836223,ENSE00003501661,ENSE00003656032,ENSE00003691177,ENSE00003550793,ENSE00003487993,ENSE00003504764,ENSE00003590154,ENSE00003495252,ENSE00003524833,ENSE00003526715,ENSE00003672501,ENSE00001811266,ENSE00001383783,ENSE00001382812,ENSE00001377890,ENSE00001373498,ENSE00001369566,ENSE00001677475,ENSE00001816354,ENSE00001614970,ENSE00003583432,ENSE00003609031,ENSE00001875207,ENSE00002311052,ENSE00001389556
    #> ENSG00000162692                                                                                                                                                                                                                                                                                                                                                                                 ENSE00001167786,ENSE00001811760,ENSE00001825682,ENSE00001451872,ENSE00001451878,ENSE00001067757,ENSE00001067755,ENSE00001067758,ENSE00001067756,ENSE00001067753,ENSE00001067759,ENSE00001067751,ENSE00003565397,ENSE00001892498,ENSE00001451871,ENSE00001167778
    #> ENSG00000168309                                                                                                                                                                                                                                                                                                                                                 ENSE00001419495,ENSE00001913711,ENSE00001833888,ENSE00001891124,ENSE00001228325,ENSE00001929708,ENSE00003652265,ENSE00003666445,ENSE00001518622,ENSE00001876866,ENSE00001518624,ENSE00001736715,ENSE00003684332,ENSE00003505632,ENSE00001872799,ENSE00001849960,ENSE00001901828,ENSE00001854286
    #> ENSG00000101347                                                                                                                                                                                                 ENSE00000800455,ENSE00001930417,ENSE00000661812,ENSE00000661813,ENSE00003692177,ENSE00003551249,ENSE00001842453,ENSE00000661815,ENSE00001622813,ENSE00001604475,ENSE00001711142,ENSE00001793017,ENSE00001667406,ENSE00001752622,ENSE00003598833,ENSE00003555165,ENSE00003535434,ENSE00003608826,ENSE00003546823,ENSE00003559540,ENSE00002218059,ENSE00003462531,ENSE00003673643,ENSE00003619308,ENSE00003541183,ENSE00001957470,ENSE00001898623
    #> ENSG00000127954                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSE00001917465,ENSE00001277946,ENSE00001163797,ENSE00001753614,ENSE00001483677,ENSE00001120706,ENSE00001621319,ENSE00001673636,ENSE00001905312,ENSE00001871078
    #>                     logFC   logCPM        LR        PValue           FDR
    #> ENSG00000109906 -7.142568 4.143740 1261.9807 2.067517e-276 1.316533e-271
    #> ENSG00000165995 -3.274748 4.504348  812.8324 8.751783e-179 2.786436e-174
    #> ENSG00000152583 -4.555162 5.528678  681.3354 3.423895e-150 7.267445e-146
    #> ENSG00000171819 -5.667623 3.500283  610.6332 8.147746e-135 1.297060e-130
    #> ENSG00000120129 -2.932426 7.308257  554.0511 1.654719e-122 2.107351e-118
    #> ENSG00000189221 -3.336874 6.766776  539.1684 2.859902e-119 3.035166e-115
    #> ENSG00000162692  3.687487 4.599336  520.7024 2.976645e-115 2.707769e-111
    #> ENSG00000168309 -4.721213 2.775385  519.6618 5.013376e-115 3.990460e-111
    #> ENSG00000101347 -3.751833 9.206504  499.8604 1.019390e-110 7.212412e-107
    #> ENSG00000127954 -5.175431 3.652849  491.9900 5.258022e-109 3.348151e-105

    # lrt to DESeqResults

    tbledger <- lrt$table
    colnames(tbledger)[colnames(tbledger) == "PValue"] <- "pvalue"
    colnames(tbledger)[colnames(tbledger) == "logFC"] <- "log2FoldChange"
    colnames(tbledger)[colnames(tbledger) == "logCPM"] <- "baseMean"
    # get from the logcpm to something more the baseMean for better
    tbledger$baseMean <- (2^tbledger$baseMean) * mean(dge_airway$samples$lib.size) / 1e6
    # use the constructor for DESeqResults
    edger_resu <- DESeqResults(DataFrame(tbledger))
    edger_resu <- DESeq2:::pvalueAdjustment(edger_resu,
      independentFiltering = TRUE,
      alpha = 0.05, pAdjustMethod = "BH"
    )

    # cfr with res_airway
    # plot(res_airway$pvalue,edger_resu$pvalue)

    ### with limma-voom
    x <- calcNormFactors(dge_airway)

    #> calcNormFactors has been renamed to normLibSizes

    design <- model.matrix(~ 0 + dex + cell)
    contr.matrix <- makeContrasts(dextrt - dexuntrt, levels = colnames(design))
    v <- voom(x, design)
    vfit <- lmFit(v, design)
    vfit <- contrasts.fit(vfit, contrasts = contr.matrix)
    efit <- eBayes(vfit)

    tbllimma <- topTable(efit, number = Inf, sort.by = "none")
    colnames(tbllimma)[colnames(tbllimma) == "P.Value"] <- "pvalue"
    colnames(tbllimma)[colnames(tbllimma) == "logFC"] <- "log2FoldChange"
    colnames(tbllimma)[colnames(tbllimma) == "AveExpr"] <- "baseMean"
    colnames(tbllimma)[colnames(tbllimma) == "adj.P.Val"] <- "padj"
    # get from the logcpm to something more the baseMean for better
    tbllimma$baseMean <- (2^tbllimma$baseMean) * mean(dge_airway$samples$lib.size) / 1e6
    # use the constructor for DESeqResults
    limma_resu <- DESeqResults(DataFrame(tbllimma))

    # cfr with res_airway
    # plot(res_airway$pvalue,limma_resu$pvalue)

After preparing the objects, you can launch the app with the following command:

    ideal(redo_dds_airway, edger_resu)
    # or ...
    ideal(redo_dds_airway, limma_resu)

Basically, you need a `data.frame` (e.g. `myresults_df`) with 3 columns to create the `DESeqResults` object to be passed to the app, with this names:

- `baseMean` as an average expression value
- `log2FoldChange` as an indicator for the effect size
- `pvalue` as a measure of significance

Then, you just need to call `DESeqResults(DataFrame(myresults_df))`. `padj` can be computed either by `p.adjust` or using `DESeq2:::pvalueAdjustment` as in the example above.

# Functions exported by the package for standalone usage

The functions exported by the ideal package can be also used in a standalone scenario, provided the required objects are in the working environment. They are listed here for an overview, but please refer to the documentation for additional details. Where possible, for each function a code snippet will be provided for its typical usage.

## `deseqresult2DEgenes` and `deseqresult2tbl`

`deseqresult2DEgenes` and `deseqresult2tbl` generate a tidy table with the results of DESeq2, sorted by the values in the `padj` column.

From version 1.30.0 onwards, the `deseqresult2tbl()` and `deseqresult2DEgenes()` functions have been deprecated, in favor of the equivalent `mosdef::deresult_to_df()` function, which is more robust and flexible in its usage.

    summary(res_airway)

    #>
    #> out of 33469 with nonzero total read count
    #> adjusted p-value < 0.05
    #> LFC > 0 (up)       : 2211, 6.6%
    #> LFC < 0 (down)     : 1817, 5.4%
    #> outliers [1]       : 0, 0%
    #> low counts [2]     : 16687, 50%
    #> (mean count < 7)
    #> [1] see 'cooksCutoff' argument of ?results
    #> [2] see 'independentFiltering' argument of ?results

    res_airway

    #> log2 fold change (MLE): dex trt vs untrt
    #> Wald test p-value: dex trt vs untrt
    #> DataFrame with 63677 rows and 6 columns
    #>                  baseMean log2FoldChange     lfcSE      stat      pvalue
    #>                 <numeric>      <numeric> <numeric> <numeric>   <numeric>
    #> ENSG00000000003  708.6022     -0.3812540  0.100654 -3.787752 0.000152016
    #> ENSG00000000005    0.0000             NA        NA        NA          NA
    #> ENSG00000000419  520.2979      0.2068126  0.112219  1.842943 0.065337292
    #> ENSG00000000457  237.1630      0.0379204  0.143445  0.264356 0.791505742
    #> ENSG00000000460   57.9326     -0.0881682  0.287142 -0.307054 0.758801924
    #> ...                   ...            ...       ...       ...         ...
    #> ENSG00000273489  0.275899       1.483738   3.51395  0.422243    0.672848
    #> ENSG00000273490  0.000000             NA        NA        NA          NA
    #> ENSG00000273491  0.000000             NA        NA        NA          NA
    #> ENSG00000273492  0.105978      -0.463679   3.52308 -0.131612    0.895291
    #> ENSG00000273493  0.106142      -0.521355   3.53139 -0.147634    0.882631
    #>                       padj
    #>                  <numeric>
    #> ENSG00000000003 0.00119659
    #> ENSG00000000005         NA
    #> ENSG00000000419 0.18625623
    #> ENSG00000000457 0.90370473
    #> ENSG00000000460 0.88616659
    #> ...                    ...
    #> ENSG00000273489         NA
    #> ENSG00000273490         NA
    #> ENSG00000273491         NA
    #> ENSG00000273492         NA
    #> ENSG00000273493         NA

    head(mosdef::deresult_to_df(res_airway))

    #>                              id   baseMean log2FoldChange     lfcSE     stat
    #> ENSG00000152583 ENSG00000152583   997.4398       4.574919 0.1840561 24.85611
    #> ENSG00000165995 ENSG00000165995   495.0929       3.291062 0.1331737 24.71255
    #> ENSG00000120129 ENSG00000120129  3409.0294       2.947810 0.1214377 24.27426
    #> ENSG00000101347 ENSG00000101347 12703.3871       3.766995 0.1554380 24.23472
    #> ENSG00000189221 ENSG00000189221  2341.7673       3.353580 0.1417823 23.65301
    #> ENSG00000211445 ENSG00000211445 12285.6151       3.730403 0.1658306 22.49526
    #>                        pvalue          padj
    #> ENSG00000152583 2.220933e-136 3.727170e-132
    #> ENSG00000165995 7.839410e-135 6.578049e-131
    #> ENSG00000120129 3.666925e-130 2.051278e-126
    #> ENSG00000101347 9.583815e-130 4.020890e-126
    #> ENSG00000189221 1.098955e-123 3.688532e-120
    #> ENSG00000211445 4.618318e-112 1.291744e-108

In particular, `deseqresult2DEgenes` only includes genes detected as DE - this is also covered by `mosdef::deresult_to_df()` in the newer versions.

    head(mosdef::deresult_to_df(res_airway, FDR = 0.05))

    #>                              id   baseMean log2FoldChange     lfcSE     stat
    #> ENSG00000152583 ENSG00000152583   997.4398       4.574919 0.1840561 24.85611
    #> ENSG00000165995 ENSG00000165995   495.0929       3.291062 0.1331737 24.71255
    #> ENSG00000120129 ENSG00000120129  3409.0294       2.947810 0.1214377 24.27426
    #> ENSG00000101347 ENSG00000101347 12703.3871       3.766995 0.1554380 24.23472
    #> ENSG00000189221 ENSG00000189221  2341.7673       3.353580 0.1417823 23.65301
    #> ENSG00000211445 ENSG00000211445 12285.6151       3.730403 0.1658306 22.49526
    #>                        pvalue          padj
    #> ENSG00000152583 2.220933e-136 3.727170e-132
    #> ENSG00000165995 7.839410e-135 6.578049e-131
    #> ENSG00000120129 3.666925e-130 2.051278e-126
    #> ENSG00000101347 9.583815e-130 4.020890e-126
    #> ENSG00000189221 1.098955e-123 3.688532e-120
    #> ENSG00000211445 4.618318e-112 1.291744e-108

    # the output in the first lines is the same, but
    dim(res_airway)

    #> [1] 63677     6

    dim(mosdef::deresult_to_df(res_airway))

    #> [1] 63677     7

This tables can be enhanced with clickable links to the ENSEMBL and NCBI gene databases by the following commands:

    myde <- mosdef::deresult_to_df(res_airway, FDR = 0.05)
    myde$symbol <- mapIds(org.Hs.eg.db, keys = as.character(myde$id), column = "SYMBOL", keytype = "ENSEMBL")

    #> 'select()' returned 1:many mapping between keys and columns

    myde_enhanced <- myde
    myde_enhanced$id <- mosdef::create_link_ENSEMBL(myde_enhanced$id, species = "Homo_sapiens")
    myde_enhanced$symbol <- mosdef::create_link_NCBI(myde_enhanced$symbol)

    DT::datatable(myde_enhanced[1:100, ], escape = FALSE)

## `ggplotCounts`

`ggplotCounts` extends the functionality of the `plotCounts` function of DESeq2, and plots the normalized counts of a single gene as a boxplot, with jittered points superimposed.

    ggplotCounts(
      dds = dds_airway,
      gene = "ENSG00000103196", # CRISPLD2 in the original publication
      intgroup = "dex"
    )

If an `annotation_obj` is provided, their gene name can also be included in the title.

    ggplotCounts(
      dds = dds_airway,
      gene = "ENSG00000103196", # CRISPLD2 in the original publication
      intgroup = "dex",
      annotation_obj = annotation_airway
    )

When used in the context of the app, it is possible to seamless search for genes also by their gene name, making exploration even more immediate.

From version 1.30.0 onwards, the `ggplotCounts()` function has been deprecated, in favor of the equivalent `mosdef::gene_plot()` function, which is more robust and flexible in its usage.

## `goseqTable`

`goseqTable` is a wrapper to extract the functional GO terms enriched in in a list of (DE) genes, based on the algorithm and the implementation in the goseq package.

Its counterpart, based on the topGO package, can be found in the pcaExplorer package.

Please note that the following code chunks are not run only for reducing compilation time. The reader is invited to try out the functionality by simply executing these commands.

    res_subset <- mosdef::deresult_to_df(res_airway)[1:100, ] # taking only a subset of the DE genes
    myde <- res_subset$id
    myassayed <- rownames(res_airway)
    mygo <- goseqTable(
      de.genes = myde,
      assayed.genes = myassayed,
      genome = "hg38",
      id = "ensGene",
      testCats = "GO:BP",
      addGeneToTerms = FALSE
    )
    head(mygo)

As for the results, this table can be enhanced by adding the links for each category to the AmiGO database.

    mygo_enhanced <- mygo
    # using the unexported function to link the GO term to the entry in the AmiGO db
    mygo_enhanced$category <- mosdef::create_link_GO(mygo_enhanced$category)
    DT::datatable(mygo_enhanced, escape = FALSE)

From version 1.30.0 onwards, the `goseqTable()` function has been deprecated, in favor of the equivalent `mosdef::run_goseq()` function, which is faster, more robust and flexible in its usage.

## `plot_ma`

The MA plot provided by ideal displays the gene-wise log2-fold changes (logFCs) versus the average expression value. As a main input parameter, a `DESeqResults` object is required. Control on the appearance of the plot can be applied by selecting the False Discovery Rate (`FDR`), the point transparency (`point_alpha`), adding horizontal lines at particular logFC values (`hlines`). The user can also decide to add rug plots in the margins (setting `add_rug` to `TRUE`).

To facilitate the inspection of a particular gene or gene set, `intgenes` can assume the value of a vector of genes (either the IDs or the gene symbols if `symbol` column is provided in `res_obj`. Labels can be added via `labels_intgenes`, while classical labels/title can be also edited as preferred (see `plot_ma` for all details).

    plot_ma(res_airway,
      FDR = 0.05, hlines = 1,
      title = "Adding horizontal lines", add_rug = FALSE
    )
    plot_ma(res_airway,
      FDR = 0.1,
      intgenes = c(
        "ENSG00000103196", # CRISPLD2
        "ENSG00000120129", # DUSP1
        "ENSG00000163884", # KLF15
        "ENSG00000179094"
      ), # PER1
      title = "Providing a shortlist of genes",
      add_rug = FALSE
    )

    res_airway2 <- res_airway
    res_airway2$symbol <- mapIds(org.Hs.eg.db, keys = rownames(res_airway2), column = "SYMBOL", keytype = "ENSEMBL")

    #> 'select()' returned 1:many mapping between keys and columns

    plot_ma(res_airway2,
      FDR = 0.05,
      intgenes = c(
        "CRISPLD2", # CRISPLD2
        "DUSP1", # DUSP1
        "KLF15", # KLF15
        "PER1"
      ), # PER1
      annotation_obj = annotation_airway,
      hlines = 2,
      add_rug = FALSE,
      title = "Putting gene names..."
    )

## `plot_volcano`

The volcano plot gives a different flavor for the gene overview, displaying log2-fold changes and log p-values.

In a way similar to `plot_ma`, genes can be annotated with `intgenes`, and vertical lines can be added via `vlines`. `ylim_up` controls the y axis upper limit to visualize better the bulk of genes - keep in mind that very small p-values due to robust differences/large effect sizes can be “cut out”.

    plot_volcano(res_airway)

    plot_volcano(res_airway2,
      FDR = 0.05,
      intgenes = c(
        "CRISPLD2", # CRISPLD2
        "DUSP1", # DUSP1
        "KLF15", # KLF15
        "PER1"
      ), # PER1
      title = "Selecting the handful of genes - and displaying the full range for the -log10(pvalue)",
      ylim_up = -log10(min(res_airway2$pvalue, na.rm = TRUE))
    )

## `sepguesser`

`sepguesser` makes an educated guess on the separator character for the input text file (`file`). The separator list can be provided as a vector in `sep_list` (defaults to comma, tab, semicolon, and whitespace - which ideally could cover most of the cases). The heuristics is based on the number of occurrencies of each separator in each line.

    sepguesser(system.file("extdata/design_commas.txt", package = "ideal"))

    #> [1] ","

    sepguesser(system.file("extdata/design_semicolons.txt", package = "ideal"))

    #> [1] ";"

    sepguesser(system.file("extdata/design_spaces.txt", package = "ideal"))

    #> [1] " "

    anyfile <- system.file("extdata/design_tabs.txt", package = "ideal") # we know it is going to be TAB
    guessed_sep <- sepguesser(anyfile)
    guessed_sep

    #> [1] "\t"

    # to be used for reading in the same file, without having to specify the sep
    read.delim(anyfile,
      header = TRUE, as.is = TRUE,
      sep = guessed_sep,
      quote = "", row.names = 1, check.names = FALSE
    )

    #>            SampleName    cell   dex albut        Run avgLength Experiment
    #> SRR1039508 GSM1275862  N61311 untrt untrt SRR1039508       126  SRX384345
    #> SRR1039509 GSM1275863  N61311   trt untrt SRR1039509       126  SRX384346
    #> SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512       126  SRX384349
    #> SRR1039513 GSM1275867 N052611   trt untrt SRR1039513        87  SRX384350
    #> SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516       120  SRX384353
    #> SRR1039517 GSM1275871 N080611   trt untrt SRR1039517       126  SRX384354
    #> SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520       101  SRX384357
    #> SRR1039521 GSM1275875 N061011   trt untrt SRR1039521        98  SRX384358
    #>               Sample    BioSample sizeFactor
    #> SRR1039508 SRS508568 SAMN02422669  1.0236476
    #> SRR1039509 SRS508567 SAMN02422675  0.8961667
    #> SRR1039512 SRS508571 SAMN02422678  1.1794861
    #> SRR1039513 SRS508572 SAMN02422670  0.6700538
    #> SRR1039516 SRS508575 SAMN02422682  1.1776714
    #> SRR1039517 SRS508576 SAMN02422673  1.3990365
    #> SRR1039520 SRS508579 SAMN02422683  0.9207787
    #> SRR1039521 SRS508580 SAMN02422677  0.9445141

# Creating and sharing output objects

While running the app, the user can

- generate and save graphics
- create and export tables
- generate, preview, download/export an HTML report
- save the values of the `reactiveValues` in an environment, or in binary format

This functionality to retrieve and share the output is provided by action buttons that are placed close to each element of interest.

## Expanding the analysis after `ideal`

After exporting the objects to the R environment, via the button “Exit ideal and save”, it is possible to continue the analyses e.g. using alternative methods for functional enrichment analysis, with Functional Class Scoring methods (such as GSEA, as implemented in fgsea), or additional methods for Overrepresentation Analysis (e.g. gprofiler2).
In the example below, we show an example of the code required to perform `fgsea` on the set of objects exported from `ideal()`.

    library("fgsea")
    # selecting the result object from the exported environment
    exported_res <- ideal_env$ideal_values_20200926_135052$res_obj
    summary(exported_res)

    # for processing of intermediate steps
    library("dplyr")
    library("tibble")

    # extracting the rankes for pre-ranked gsea
    de_ranks <- exported_res %>%
      as.data.frame() %>%
      dplyr::arrange(padj) %>%
      dplyr::select(symbol, stat) %>%
      deframe()
    head(de_ranks, 20)

    # loading the signatures from the gmt file - to be downloaded locally
    pathways_gmtfile <- gmtPathways("path_to/msigdb_v7.0_GMTs/h.all.v6.2.symbols.gmt")

    # running fgsea
    fgsea_result <- fgsea(
      pathways = pathways_gmtfile,
      stats = de_ranks,
      nperm = 100000
    )
    fgsea_result <- fgsea_result %>%
      arrange(desc(NES))

    # displaying the results
    DT::datatable(fgsea_result)

The following chunk shows an example of how to perform the functional enrichment analysis with `g:Profiler`’s tool `g:GOST`, using the interface in gprofiler2

    library("gprofiler2")

    # selecting the result object from the exported environment
    exported_res <- ideal_env$ideal_values_20200926_135052$res_obj
    summary(exported_res)

    # extracting the DE genes vector
    degenes <- deseqresult2DEgenes(exported_res, FDR = 0.01)$symbol

    # running the test
    gost_res <- gost(
      query = degenes[1:1000],
      organism = "hsapiens",
      ordered_query = FALSE,
      multi_query = FALSE,
      significant = FALSE,
      exclude_iea = TRUE,
      measure_underrepresentation = FALSE,
      evcodes = TRUE,
      user_threshold = 0.05,
      correction_method = "g_SCS",
      domain_scope = "annotated",
      numeric_ns = "",
      sources = "GO:BP",
      as_short_link = FALSE
    )

    # displaying the results
    DT::datatable(gost_res$result)

# Enhancing `ideal`

The `annotation_obj` is a quick helper to make your data and results easier to read. For creating it, you can exploit the corresponding `org.XX.eg.db` packages, available in Bioconductor.

Currently available are the following (in alphabetical order):

- org.Ag.eg.db
- org.At.tair.db
- org.Bt.eg.db
- org.Ce.eg.db
- org.Cf.eg.db
- org.Dm.eg.db
- org.Dr.eg.db
- org.EcK12.eg.db
- org.EcSakai.eg.db
- org.Gg.eg.db
- org.Hs.eg.db
- org.Mm.eg.db
- org.Mmu.eg.db
- org.Pf.plasmo.db
- org.Pt.eg.db
- org.Rn.eg.db
- org.Sc.sgd.db
- org.Sco.eg.db
- org.Ss.eg.db
- org.Tgondii.eg.db
- org.Xl.eg.db

They can all be easily installed with `BiocManager::install("orgdb_packagename")`.

In a similar way, for using at best the `goseq` package, the gene length information can also be computed if the corresponding `TxDb` packages are installed. Currently, following `TxDb` packages are available:

- TxDb.Athaliana.BioMart.plantsmart22
- TxDb.Athaliana.BioMart.plantsmart25
- TxDb.Athaliana.BioMart.plantsmart28
- TxDb.Btaurus.UCSC.bosTau8.refGene
- TxDb.Celegans.UCSC.ce11.refGene
- TxDb.Celegans.UCSC.ce6.ensGene
- TxDb.Cfamiliaris.UCSC.canFam3.refGene
- TxDb.Dmelanogaster.UCSC.dm3.ensGene
- TxDb.Dmelanogaster.UCSC.dm6.ensGene
- TxDb.Drerio.UCSC.danRer10.refGene
- TxDb.Ggallus.UCSC.galGal4.refGene
- TxDb.Hsapiens.UCSC.hg18.knownGene
- TxDb.Hsapiens.UCSC.hg19.knownGene
- TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts
- TxDb.Hsapiens.UCSC.hg38.knownGene
- TxDb.Mmulatta.UCSC.rheMac3.refGene
- TxDb.Mmulatta.UCSC.rheMac8.refGene
- TxDb.Mmusculus.UCSC.mm10.ensGene
- TxDb.Mmusculus.UCSC.mm10.knownGene
- TxDb.Mmusculus.UCSC.mm9.knownGene
- TxDb.Ptroglodytes.UCSC.panTro4.refGene
- TxDb.Rnorvegicus.UCSC.rn4.ensGene
- TxDb.Rnorvegicus.UCSC.rn5.refGene
- TxDb.Rnorvegicus.UCSC.rn6.refGene
- TxDb.Scerevisiae.UCSC.sacCer2.sgdGene
- TxDb.Scerevisiae.UCSC.sacCer3.sgdGene
- TxDb.Sscrofa.UCSC.susScr3.refGene

As for the `org.XX.eg.db` above, these can all be quickly installed with `BiocManager::install("txdb_packagename")`.

# Further development

Additional functionality for the ideal will be added in the future, as it is tightly related to a topic under current development research.

Improvements, suggestions, bugs, issues and feedback of any type can be sent to marinif@uni-mainz.de.

# Session Info

    sessionInfo()

    #> R version 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Etc/UTC
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] edgeR_4.10.0                limma_3.68.0
    #>  [3] DEFormats_1.40.0            org.Hs.eg.db_3.22.0
    #>  [5] DESeq2_1.52.0               airway_1.30.0
    #>  [7] SummarizedExperiment_1.42.0 GenomicRanges_1.64.0
    #>  [9] Seqinfo_1.2.0               MatrixGenerics_1.24.0
    #> [11] matrixStats_1.5.0           ideal_2.6.0
    #> [13] topGO_2.64.0                SparseM_1.84-2
    #> [15] GO.db_3.22.0                AnnotationDbi_1.74.0
    #> [17] IRanges_2.46.0              S4Vectors_0.50.0
    #> [19] Biobase_2.72.0              graph_1.90.0
    #> [21] BiocGenerics_0.58.0         generics_0.1.4
    #> [23] knitr_1.51                  BiocStyle_2.40.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] fs_2.1.0                 bitops_1.0-9             enrichplot_1.32.0
    #>   [4] fontawesome_0.5.3        httr_1.4.8               webshot_0.5.5
    #>   [7] RColorBrewer_1.1-3       Rgraphviz_2.56.0         backports_1.5.1
    #>  [10] tools_4.6.0              R6_2.6.1                 DT_0.34.0
    #>  [13] lazyeval_0.2.3           mgcv_1.9-4               withr_3.0.2
    #>  [16] prettyunits_1.2.0        gridExtra_2.3            fdrtool_1.2.18
    #>  [19] cli_3.6.6                TSP_1.2.7                scatterpie_0.2.6
    #>  [22] labeling_0.4.3           slam_0.1-55              sass_0.4.10
    #>  [25] S7_0.2.2                 genefilter_1.94.0        goseq_1.64.0
    #>  [28] systemfonts_1.3.2        Rsamtools_2.28.0         yulab.utils_0.2.4
    #>  [31] gson_0.1.0               txdbmaker_1.8.0          DOSE_4.6.0
    #>  [34] rentrez_1.2.4            AnnotationForge_1.54.0   RSQLite_2.4.6
    #>  [37] GOstats_2.78.0           gridGraphics_0.5-1       BiocIO_1.22.0
    #>  [40] crosstalk_1.2.2          gtools_3.9.5             dplyr_1.2.1
    #>  [43] dendextend_1.19.1        Matrix_1.7-5             abind_1.4-8
    #>  [46] lifecycle_1.0.5          yaml_2.3.12              qvalue_2.44.0
    #>  [49] gplots_3.3.0             SparseArray_1.12.0       BiocFileCache_3.2.0
    #>  [52] grid_4.6.0               blob_1.3.0               promises_1.5.0
    #>  [55] crayon_1.5.3             shinydashboard_0.7.3     ggtangle_0.1.2
    #>  [58] lattice_0.22-9           annotate_1.90.0          GenomicFeatures_1.64.0
    #>  [61] cigarillo_1.2.0          KEGGREST_1.52.0          sys_3.4.3
    #>  [64] maketools_1.3.2          pillar_1.11.1            rjson_0.2.23
    #>  [67] codetools_0.2-20         glue_1.8.1               ggiraph_0.9.6
    #>  [70] ggfun_0.2.0              fontLiberation_0.1.0     data.table_1.18.2.1
    #>  [73] treeio_1.36.0            vctrs_0.7.3              png_0.1-9
    #>  [76] gtable_0.3.6             assertthat_0.2.1         cachem_1.1.0
    #>  [79] xfun_0.57                S4Arrays_1.12.0          mime_0.13
    #>  [82] survival_3.8-6           aisdk_1.1.0              pheatmap_1.0.13
    #>  [85] seriation_1.5.8          iterators_1.0.14         statmod_1.5.1
    #>  [88] Category_2.78.0          nlme_3.1-169             ggtree_4.2.0
    #>  [91] bit64_4.8.0              fontquiver_0.2.1         progress_1.2.3
    #>  [94] filelock_1.0.3           UpSetR_1.4.0             GenomeInfoDb_1.48.0
    #>  [97] bslib_0.10.0             KernSmooth_2.23-26       otel_0.2.0
    #> [100] DBI_1.3.0                tidyselect_1.2.1         processx_3.9.0
    #> [103] bit_4.6.0                compiler_4.6.0           curl_7.1.0
    #> [106] httr2_1.2.2              BiasedUrn_2.0.12         fontBitstreamVera_0.1.1
    #> [109] DelayedArray_0.38.0      plotly_4.12.0            rtracklayer_1.72.0
    #> [112] checkmate_2.3.4          scales_1.4.0             caTools_1.18.3
    #> [115] mosdef_1.8.0             RBGL_1.88.0              callr_3.7.6
    #> [118] rappdirs_0.3.4           stringr_1.6.0            digest_0.6.39
    #> [121] shinyBS_0.65.0           rmarkdown_2.31           ca_0.71.1
    #> [124] XVector_0.52.0           htmltools_0.5.9          pkgconfig_2.0.3
    #> [127] base64enc_0.1-6          lpsymphony_1.40.0        dbplyr_2.5.2
    #> [130] fastmap_1.2.0            rlang_1.2.0              htmlwidgets_1.6.4
    #> [133] UCSC.utils_1.8.0         shiny_1.13.0             farver_2.1.2
    #> [136] jquerylib_0.1.4          IHW_1.40.0               jsonlite_2.0.0
    #> [139] BiocParallel_1.46.0      GOSemSim_2.38.0          RCurl_1.98-1.18
    #> [142] magrittr_2.0.5           ggplotify_0.1.3          patchwork_1.3.2
    #> [145] Rcpp_1.1.1-1.1           ape_5.8-1                ggnewscale_0.5.2
    #> [148] viridis_0.6.5            gdtools_0.5.0            rintrojs_0.3.4
    #> [151] stringi_1.8.7            MASS_7.3-65              plyr_1.8.9
    #> [154] parallel_4.6.0           ggrepel_0.9.8            Biostrings_2.80.0
    #> [157] splines_4.6.0            hms_1.1.4                geneLenDataBase_1.46.0
    #> [160] locfit_1.5-9.12          ps_1.9.3                 igraph_2.3.0
    #> [163] enrichit_0.1.4           buildtools_1.0.0         reshape2_1.4.5
    #> [166] biomaRt_2.68.0           XML_3.99-0.23            evaluate_1.0.5
    #> [169] BiocManager_1.30.27      tweenr_2.0.3             foreach_1.5.2
    #> [172] httpuv_1.6.17            polyclip_1.10-7          tidyr_1.3.2
    #> [175] purrr_1.2.2              heatmaply_1.6.0          ggplot2_4.0.3
    #> [178] ggforce_0.5.0            xtable_1.8-8             restfulr_0.0.16
    #> [181] tidytree_0.4.7           tidydr_0.0.6             later_1.4.8
    #> [184] viridisLite_0.4.3        tibble_3.3.1             clusterProfiler_4.20.0
    #> [187] aplot_0.2.9              memoise_2.0.1            registry_0.5-1
    #> [190] GenomicAlignments_1.48.0 cluster_2.1.8.2          GSEABase_1.74.0
    #> [193] shinyAce_0.4.4