ideal User’s Guide
24 April 2017
Abstract
In the scope of differential expression analysis, we provide the R/Bioconductor package ideal, which serves as a web application to allow simultaneously for interactive and reproducible analysis. ideal guides the user throughout the steps of Differential Expression analysis and produces a wealth of effective visualizations to facilitate data interpretation, in a comprehensive and accessible way for a wide range of scientists.
Package
ideal 1.0.0
Compiled date: 2017-04-24
Last edited: 2017-04-04
License: MIT + file LICENSE
1 Getting started
ideal is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("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")2 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.
2.1 Citation info
If you use ideal for your analysis, please cite it as here below:
citation("ideal")##
## To cite package 'ideal' in publications use:
##
## Federico Marini (2017). ideal: Interactive Differential Expression
## AnaLysis. R package version 1.0.0.
## https://github.com/federicomarini/ideal
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {ideal: Interactive Differential Expression AnaLysis},
## author = {Federico Marini},
## year = {2017},
## note = {R package version 1.0.0},
## url = {https://github.com/federicomarini/ideal},
## }3 Using the application
There are different ways to use ideal for interactive differential expression analysis.
3.1 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- aDESeqDataSetobject. If not provided, then acountmatrixand aexpdesignneed to be provided. If none of the above is provided, it is possible to upload the data during the execution of the Shiny Appres_obj- aDESeqResultsobject. If not provided, it can be computed during the execution of the applicationannotation_obj- adata.frameobject, 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 theorg.eg.XX.dbpackagescountmatrix- 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 Appexpdesign-adata.framecontaining 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 parametersideal(dds_obj = dds), as in the command above, but where the result object is assembled at runtimeideal(countmatrix = countmatrix, expdesign = expdesign), where instead of passing the definedDESeqDataSetobject, 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 runtimeideal(), 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
3.2 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:
3.3 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.
4 Getting to know the user interace 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.
5 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.
5.1 Welcome!
The landing page for the app is also where you might likely be reading this text (otherwise in the package vignette).
5.2 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.framein advance, and is based on the widely adoptedorg.XX.eg.dbBioconductor 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.
5.3 Counts Overview
As in pcaExplorer, interactive tables for the raw, normalized or (r)log-transformed 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.
5.4 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.
5.5 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.
5.6 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).
5.7 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
limma::goanafor the quick yet standard implementationtopGO, particularly valuable for pruning terms which are topologically less meaningful than their specific nodesgoseq, 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.
5.8 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.
5.9 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.
6 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
source("https://bioconductor.org/biocLite.R")
biocLite("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)
dds_airway <- DESeqDataSet(airway,design= ~ cell + dex)
dds_airway## class: DESeqDataSet
## dim: 64102 8
## metadata(2): '' version
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## 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 C1orf112
## ENSG00000000938 ENSG00000000938 FGRor 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/).
6.1 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':
##
## DGEListlibrary(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)
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
## logFC logCPM F PValue FDR
## ENSG00000109906 -7.141494 4.143740 1719.009 0.000000e+00 0.000000e+00
## ENSG00000152583 -4.549790 5.528678 1693.943 0.000000e+00 0.000000e+00
## ENSG00000096060 -3.992959 6.895112 1560.869 0.000000e+00 0.000000e+00
## ENSG00000101347 -3.752212 9.206504 1575.112 0.000000e+00 0.000000e+00
## ENSG00000211445 -3.713684 9.158160 1542.044 0.000000e+00 0.000000e+00
## ENSG00000163884 -4.472519 4.685058 1348.292 3.819016e-294 4.080109e-290
## ENSG00000189221 -3.332051 6.766776 1184.595 8.385137e-259 7.678629e-255
## ENSG00000157514 -3.180372 6.675013 1062.798 1.743036e-232 1.396651e-228
## ENSG00000162692 3.665549 4.599336 1034.572 2.202493e-226 1.568713e-222
## ENSG00000112936 -3.182516 6.515306 1033.852 3.152321e-226 2.020701e-222# If you performed likelihood ratio tests
fit <- glmFit(y,design)
lrt <- glmLRT(fit)
topTags(lrt)## Coefficient: dexuntrt
## logFC logCPM LR PValue FDR
## ENSG00000109906 -7.141823 4.143740 1293.2297 3.345918e-283 2.144800e-278
## ENSG00000152583 -4.552568 5.528678 901.7878 4.010221e-198 1.285316e-193
## ENSG00000165995 -3.274517 4.504348 749.6857 4.696169e-165 1.003446e-160
## ENSG00000163884 -4.458891 4.685058 703.8821 4.280694e-155 6.860026e-151
## ENSG00000189221 -3.335100 6.766776 647.7867 6.767998e-143 8.676844e-139
## ENSG00000127954 -5.197020 3.652849 638.6801 6.471522e-141 6.913959e-137
## ENSG00000162692 3.677380 4.599336 634.3726 5.595308e-140 5.123863e-136
## ENSG00000101347 -3.751932 9.206504 631.1278 2.841479e-139 2.276806e-135
## ENSG00000171819 -5.668260 3.500283 615.0520 8.911131e-136 6.346904e-132
## ENSG00000120129 -2.932273 7.308257 603.5237 2.866583e-133 1.837537e-129# 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$pvalue)
### with limma-voom
x <- calcNormFactors(dge_airway)
design <- model.matrix(~0+dex+cell)
contr.matrix <- makeContrasts(dextrt - dexuntrt,levels=colnames(design))
v <- voom(x, design)
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
tbllimma <- topTable(efit,number= Inf, sort.by = "none")
colnames(tbllimma)[colnames(tbllimma) == 'P.Value'] <- 'pvalue'
colnames(tbllimma)[colnames(tbllimma) == 'logFC'] <- 'log2FoldChange'
colnames(tbllimma)[colnames(tbllimma) == 'AveExpr'] <- 'baseMean'
colnames(tbllimma)[colnames(tbllimma) == 'adj.P.Val'] <- 'padj'
# get from the logcpm to something more the baseMean for better
tbllimma$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:
baseMeanas an average expression valuelog2FoldChangeas an indicator for the effect sizepvalueas 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.
7 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.
7.1 deseqresult2DEgenes and deseqresult2tbl
deseqresult2DEgenes and deseqresult2tbl generate a tidy table with the results of DESeq2, sorted by the values in the padj column.
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] : 16676, 50%
## (mean count < 7)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsres_airway## log2 fold change (MLE): dex trt vs untrt
## Wald test p-value: dex trt vs untrt
## DataFrame with 64102 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSG00000000003 708.60217 -0.38125397 0.1006560 -3.7876937 0.0001520521
## ENSG00000000005 0.00000 NA NA NA NA
## ENSG00000000419 520.29790 0.20681259 0.1122218 1.8428915 0.0653448549
## ENSG00000000457 237.16304 0.03792034 0.1434532 0.2643394 0.7915183888
## ENSG00000000460 57.93263 -0.08816367 0.2871677 -0.3070111 0.7588349227
## ... ... ... ... ... ...
## LRG_94 0 NA NA NA NA
## LRG_96 0 NA NA NA NA
## LRG_97 0 NA NA NA NA
## LRG_98 0 NA NA NA NA
## LRG_99 0 NA NA NA NA
## padj
## <numeric>
## ENSG00000000003 0.00119766
## ENSG00000000005 NA
## ENSG00000000419 0.18633658
## ENSG00000000457 0.90376886
## ENSG00000000460 0.88623095
## ... ...
## LRG_94 NA
## LRG_96 NA
## LRG_97 NA
## LRG_98 NA
## LRG_99 NAhead(deseqresult2tbl(res_airway))## id baseMean log2FoldChange lfcSE stat pvalue
## 1 ENSG00000152583 997.4398 4.574919 0.1840562 24.85610 2.221711e-136
## 2 ENSG00000165995 495.0929 3.291062 0.1331768 24.71197 7.952309e-135
## 3 ENSG00000120129 3409.0294 2.947810 0.1214350 24.27480 3.618631e-130
## 4 ENSG00000101347 12703.3871 3.766995 0.1554342 24.23531 9.445863e-130
## 5 ENSG00000189221 2341.7673 3.353580 0.1417801 23.65338 1.089352e-123
## 6 ENSG00000211445 12285.6151 3.730403 0.1658267 22.49579 4.563616e-112
## padj
## 1 3.730919e-132
## 2 6.677156e-131
## 3 2.025589e-126
## 4 3.965609e-126
## 5 3.658698e-120
## 6 1.277280e-108In particular, deseqresult2DEgenes only includes genes detected as DE
head(deseqresult2DEgenes(res_airway,FDR = 0.05))## id baseMean log2FoldChange lfcSE stat pvalue
## 1 ENSG00000152583 997.4398 4.574919 0.1840562 24.85610 2.221711e-136
## 2 ENSG00000165995 495.0929 3.291062 0.1331768 24.71197 7.952309e-135
## 3 ENSG00000120129 3409.0294 2.947810 0.1214350 24.27480 3.618631e-130
## 4 ENSG00000101347 12703.3871 3.766995 0.1554342 24.23531 9.445863e-130
## 5 ENSG00000189221 2341.7673 3.353580 0.1417801 23.65338 1.089352e-123
## 6 ENSG00000211445 12285.6151 3.730403 0.1658267 22.49579 4.563616e-112
## padj
## 1 3.730919e-132
## 2 6.677156e-131
## 3 2.025589e-126
## 4 3.965609e-126
## 5 3.658698e-120
## 6 1.277280e-108# the output in the first lines is the same, but
dim(res_airway)## [1] 64102 6dim(deseqresult2DEgenes(res_airway))## [1] 4028 7This tables can be enhanced with clickable links to the ENSEMBL and NCBI gene databases by the following commands
myde <- deseqresult2DEgenes(res_airway,FDR = 0.05)
myde$symbol <- mapIds(org.Hs.eg.db,keys = as.character(myde$id),column = "SYMBOL",keytype="ENSEMBL")## 'select()' returned 1:many mapping between keys and columnsmyde_enhanced <- myde
myde_enhanced$id <- ideal:::createLinkENS(myde_enhanced$id,species = "Homo_sapiens")
myde_enhanced$symbol <- ideal:::createLinkGeneSymbol(myde_enhanced$symbol)
DT::datatable(myde_enhanced[1:100,], escape = FALSE)7.2 ggplotCounts
ggplotCounts extends the functionality of the plotCounts function of DESeq2, and plots the normalized counts of a single gene as a boxplot, with jittered points superimposed.
ggplotCounts(dds = dds_airway,
gene = "ENSG00000103196", # CRISPLD2 in the original publication
intgroup = "dex")
If an annotation_obj is provided, their gene name can also be included in the title.
ggplotCounts(dds = dds_airway,
gene = "ENSG00000103196", # CRISPLD2 in the original publication
intgroup = "dex",
annotation_obj = annotation_airway)
When used in the context of the app, it is possible to seamless search for genes also by their gene name, making exploration even more immediate.
7.3 goseqTable
goseqTable is a wrapper to extract the functional GO terms enriched in in a list of (DE) genes, based on the algorithm and the implementation in the goseq package.
Its counterpart, based on the topGO package, can be found in the pcaExplorer package.
Please note that the following code chunks are not run only for reducing compilation time. The reader is invited to try out the functionality by simply executing these commands.
res_subset <- deseqresult2DEgenes(res_airway)[1:100,] # taking only a subset of the DE genes
myde <- res_subset$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$category <- ideal:::createLinkGO(mygo_enhanced$category)
DT::datatable(mygo_enhanced,escape=FALSE)7.4 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$symbol <- mapIds(org.Hs.eg.db,keys = rownames(res_airway2),column = "SYMBOL",keytype="ENSEMBL")## 'select()' returned 1:many mapping between keys and columnsplot_ma(res_airway2, FDR = 0.05,
intgenes = c("CRISPLD2", # CRISPLD2
"DUSP1", # DUSP1
"KLF15", # KLF15
"PER1"), # PER1
annotation_obj = annotation_airway,
hlines = 2,
add_rug = FALSE,
title = "Putting gene names..."
)
7.5 plot_volcano
The volcano plot gives a different flavor for the gene overview, displaying log2-fold changes and log p-values
In a way similar to plot_ma, genes can be annotated with intgenes, and vertical lines can be added via vlines. ylim_up controls the y axis upper limit to visualize better the bulk of genes - keep in mind that very small p-values due to robust differences/large effect sizes can be “cut out”.
plot_volcano(res_airway)plot_volcano(res_airway2, FDR = 0.05,
intgenes = c("CRISPLD2", # CRISPLD2
"DUSP1", # DUSP1
"KLF15", # KLF15
"PER1"), # PER1
title = "Selecting the handful of genes - and displaying the full range for the -log10(pvalue)",
ylim_up = -log10(min(res_airway2$pvalue, na.rm =TRUE)))
7.6 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 0.94451418 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
reactiveValuesin 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.
9 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 biocLite("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 biocLite("txdb_packagename").
10 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 3.4.0 (2017-04-21)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] edgeR_3.18.0 limma_3.32.0
## [3] DEFormats_1.4.0 org.Hs.eg.db_3.4.1
## [5] DESeq2_1.16.0 airway_0.109.0
## [7] SummarizedExperiment_1.6.0 DelayedArray_0.2.0
## [9] matrixStats_0.52.2 GenomicRanges_1.28.0
## [11] GenomeInfoDb_1.12.0 ideal_1.0.0
## [13] bigmemory_4.5.19 bigmemory.sri_0.1.3
## [15] topGO_2.28.0 SparseM_1.77
## [17] GO.db_3.4.1 AnnotationDbi_1.38.0
## [19] IRanges_2.10.0 S4Vectors_0.14.0
## [21] Biobase_2.36.0 graph_1.54.0
## [23] BiocGenerics_0.22.0 BiocStyle_2.4.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.0.5 GOstats_2.42.0
## [3] Hmisc_4.0-2 NMF_0.20.6
## [5] plyr_1.8.4 lazyeval_0.2.0
## [7] GSEABase_1.38.0 shinydashboard_0.5.3
## [9] splines_3.4.0 BiocParallel_1.10.0
## [11] ggplot2_2.2.1 gridBase_0.4-7
## [13] lpsymphony_1.4.0 digest_0.6.12
## [15] foreach_1.4.3 htmltools_0.3.5
## [17] magrittr_1.5 checkmate_1.8.2
## [19] memoise_1.1.0 cluster_2.0.6
## [21] doParallel_1.0.10 Biostrings_2.44.0
## [23] annotate_1.54.0 colorspace_1.3-2
## [25] ggrepel_0.6.5 BiasedUrn_1.07
## [27] dplyr_0.5.0 RCurl_1.95-4.8
## [29] jsonlite_1.4 genefilter_1.58.0
## [31] survival_2.41-3 iterators_1.0.8
## [33] registry_0.3 gtable_0.2.0
## [35] zlibbioc_1.22.0 XVector_0.16.0
## [37] UpSetR_1.3.3 rentrez_1.0.4
## [39] scales_0.4.1 pheatmap_1.0.8
## [41] DBI_0.6-1 IHW_1.4.0
## [43] rngtools_1.2.4 Rcpp_0.12.10
## [45] xtable_1.8-2 htmlTable_1.9
## [47] foreign_0.8-67 Formula_1.2-1
## [49] DT_0.2 AnnotationForge_1.18.0
## [51] htmlwidgets_0.8 threejs_0.2.2
## [53] RColorBrewer_1.1-2 shinyAce_0.2.1
## [55] acepack_1.4.1 XML_3.98-1.6
## [57] nnet_7.3-12 locfit_1.5-9.1
## [59] labeling_0.3 reshape2_1.4.2
## [61] munsell_0.4.3 tools_3.4.0
## [63] RSQLite_1.1-2 rintrojs_0.1.2
## [65] fdrtool_1.2.15 evaluate_0.10
## [67] shinyBS_0.61 stringr_1.2.0
## [69] yaml_2.1.14 knitr_1.15.1
## [71] geneLenDataBase_1.11.0 RBGL_1.52.0
## [73] nlme_3.1-131 mime_0.5
## [75] slam_0.1-40 biomaRt_2.32.0
## [77] compiler_3.4.0 png_0.1-7
## [79] tibble_1.3.0 geneplotter_1.54.0
## [81] stringi_1.1.5 GenomicFeatures_1.28.0
## [83] lattice_0.20-35 pcaExplorer_2.2.0
## [85] Matrix_1.2-9 goseq_1.28.0
## [87] d3heatmap_0.6.1.1 data.table_1.10.4
## [89] bitops_1.0-6 httpuv_1.3.3
## [91] rtracklayer_1.36.0 R6_2.2.0
## [93] latticeExtra_0.6-28 bookdown_0.3
## [95] gridExtra_2.2.1 codetools_0.2-15
## [97] assertthat_0.2.0 pkgmaker_0.22
## [99] Category_2.42.0 rprojroot_1.2
## [101] GenomicAlignments_1.12.0 Rsamtools_1.28.0
## [103] GenomeInfoDbData_0.99.0 mgcv_1.8-17
## [105] grid_3.4.0 rpart_4.1-11
## [107] tidyr_0.6.1 rmarkdown_1.4
## [109] shiny_1.0.2 base64enc_0.1-3# devtools::session_info()