Interactive and Reproducible Workflows for Exploring and Modeling RNA‐seq Data with pcaExplorer, Ideal, and GeneTonic

Abstract

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA‐seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in‐depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill‐down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold‐standard pipelines implemented in the open‐source Bioconductor project and community, these protocols will permit complex tasks in RNA‐seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA‐seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses

Impact of plastic-related compounds on the gene expression signature of HepG2 cells transfected with CYP3A4

“…Transcript abundance estimates were computed with Salmon (version 1.5.0) (Patro et al 2017 ) with a transcriptome index generated from the GENCODE (version 38), and subsequently summarized to gene level with the tximeta R package (version 1.16.0) (Love et al 2020 ). The exploration, modelling, and interpretation of the expression data followed previously described protocols (Ludt et al 2022 ). Exploratory data analysis was executed with the pcaExplorer package (version 2.24.0) (Marini and Binder 2019 ).…”

“…Accurate estimation of the effect sizes (described as log2 fold change) was finalized using the apeglm shrinkage estimator (version 1.20.0) (Zitovsky and Love 2020 ). Subsequent analyses included Gene Ontology pathway enrichment by topGO (version 2.50.0) (Alexa et al 2006 )—using all expressed genes as background dataset and the ideal package (version 1.22.0) (Ludt et al 2022 )—and by clusterProfiler (version 4.6.0) (Wu et al 2021 ) with default settings using the log fold change as input. The enrichment results were the foundation for visualization and summarization with the GeneTonic package (version 2.2.0) (Marini et al 2021 ).…”

Endothelial Differentiation of CCM1 Knockout iPSCs Triggers the Establishment of a Specific Gene Expression Signature

“…Gene hit counts were extracted with featureCounts from the Subread package v.1.5.2. Principal component analysis of RNA sequencing data was performed with pcaExplorer [ 45 , 46 ]. The 300 genes with the highest inter-sample variance were used to compute the principal components.…”

Glucosylceramide Synthase Inhibitors Induce Ceramide Accumulation and Sensitize H3K27 Mutant Diffuse Midline Glioma to Irradiation

“…The decimal logarithmic transformed normalized count values with one pseudo count were used for the boxplots. The following R packages were used for the visualization: ComplexHeatmap (version 2.10.0) [82], ggplot2 (version 3.4.2) [83], and pcaExplorer (version 2.20.2) [84,85].…”