Module: Bioconductor R analyses

These are a collection of Bioconductor tutorials, ranging from an introduction to R and Bioconductor, to showing how to perform more complex analyses of biological data using Bioconductor R packages.

During Smorgasbord week May 22-26 2023

Organisers: Maria Doyle, Alex Mahmoud, Bioconductor Teaching Committee

Register: See registration information here

Setup

Demo Video

Description:

This video will help you get setup on the BioConductor Galaxy to run the tutorials in this module.
Length: 5 minutes
Captions: Bioconductor
Created: 5 May 2023
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Bioconductor Carpentries lessons

Introduction to data analysis with R and Bioconductor

Bioconductor logo This is a self-study session. Please work through the materials on your own, and ask the instructors for help if you get stuck or have any questions!
Tutorial: Introduction to data analysis with R and Bioconductor
Description: The Data science lesson is based on the Carpentries Ecology Curriculum. There are no pre-requisites for this module, and the materials assume no prior knowledge about R and Bioconductor. It introduces R, RStudio, teaches data cleaning, management, analysis, and visualisation and introduces some Bioconductor concepts.
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The Bioconductor project

Bioconductor logo This is a self-study session. Please work through the materials on your own, and ask the instructors for help if you get stuck or have any questions!
Tutorial: The Bioconductor project
Description: The Bioconductor project lesson provides an introduction to the Bioconductor project such as the Bioconductor home page, packages, package landing pages, and package vignettes, where to find help, Bioconductor workflows, Bioconductor release schedule and versions, some core infrastructure,.. It is meant to be use in combination with other modules as part of a wider workshop.
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Bioconductor workshops

Tutorial Video

Description:

This workshop demonstrates the use of the iSEE package to create and configure interactive applications for the exploration of various types of genomics data sets (e.g., bulk and single-cell RNA-seq, CyTOF, gene expression microarray). This workshop is presented as a lab session that combines an instructor-led live demo, followed by hands-on experimentation guided by completely worked examples and stand-alone notes that participants may continue to use after the workshop.

The instructor-led live demo comprises three parts:

  1. Brief lecture on the package concept and functionality
  2. Overview of the graphical user interface
  3. Instructions to preconfigure iSEE apps

The hands-on lab comprises three parts:

  1. Inspection of single-cell RNA-seq data at various steps of a typical computational workflow, including quality control and dimensionality reduction
  2. Addition of custom panels to the user interface for advanced visualization.
  3. Additional questions from the participants, including individual use cases and suggestions for future developments
Length: 55 minutes
Captions: No captions available for this video. We aim to have captions for all our videos, and are working to add captions to this video as soon as possible.
Created: 28 July 2020
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Tutorial Video

Description:

This tutorial will showcase analysis of single-cell RNA sequencing data following the tidy data paradigm. The tidy data paradigm provides a standard way to organise data values within a dataset, where each variable is a column, each observation is a row, and data is manipulated using an easy-to-understand vocabulary. Most importantly, the data structure remains consistent across manipulation and analysis functions.

This can be achieved with the integration of packages present in the R CRAN and Bioconductor ecosystem, including tidySingleCellExperiment and tidyverse. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis. For more information see the tidy transcriptomics blog.
Length: 1 hour 27 minutes
Captions: No captions available for this video. We aim to have captions for all our videos, and are working to add captions to this video as soon as possible.
Created: 28 July 2022
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Tutorial Video

Description:

This workshop consists of a demonstration of using DECIPHER and SynExtend for common analyses in comparative genomics. The immediate goal of this session is to use sequence data to uncover networks of functionally associated genes. These networks consist of genetic regions under shared evolutionary pressure, which have previously been shown to imply some degree of conserved function.
Length: 1 hour 5 minutes
Captions: No captions available for this video. We aim to have captions for all our videos, and are working to add captions to this video as soon as possible.
Created: 29 July 2022
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Tutorial Video

Description:

Concepts of causal inference in epidemiology have important ramifications for studies across bioinformatics and other fields of health research. In this workshop, we introduce basic concepts of epidemiology, study design, and causal inference for bioinformaticians. Emphasis is placed on addressing bias and confounding as common threats to assessing a causal pathway in a variety of study design types and when using common forms of analyses such as GWAS and survival analysis. Workshop participants will have the opportunity to create their own structural causal models (DAGs) using dagitty and ggdag and then use this model to determine how to assess an estimated causal effect. Examples using DESeq2, edgeR, and limma will be used to show how multivariable models can be fitted depending on the hypothesized causal relationship. Presented successfully at BioC2021 to a large audience of more than 100, updates that material by revising current examples based on participant feedback as well as content updates.
Length: 83 minutes
Captions: No captions available for this video. We aim to have captions for all our videos, and are working to add captions to this video as soon as possible.
Created: 27 July 2022
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SpatialOmicsOverlay Workshop

Bioconductor logo This is a self-study session. Please work through the materials on your own, and ask the instructors for help if you get stuck or have any questions!
Tutorial: SpatialOmicsOverlay Workshop
Description: This workshop will introduce users to the NanoString R package SpatialOmicsOverlay. This package is designed for use in interpretation and presentation of the multi-form data generated by NanoString's GeoMx ® Digital Spatial Profiler spatial biology platform. The GeoMx DSP produces both rich imaging and genomics data. Integrating these data types facilitates a deep understanding of the profiled tissue. The SpatialOmicsOverlay package integrates both of these data types, thereby by maintaining the relationship between underlying tissue morphology and resultant gene expression. Specifically, SpatialOmicsOverlay was developed to visualize and analyze the free-handed nature of Region of Interest (ROI) selection in a GeoMx experiment, as well as the immunofluorescence-guided segmentation process. The overlay from the instrument is recreated in the R environment, which allows for plotting overlays with data like ROI type or gene expression. The package provides a convenient workflow for users to generate customized, sharable visualizations. The Introduction to SpatialOmicsOverlay vignette demonstrates how to use OME-TIFF files, which are exported from the GeoMx platform. Participants will learn how to interact with this file type and generate informative plots over images. This vignette utilizes data from our Spatial Organ Atlas. The Spatial Organ Atlas is a freely-accesible resource of whole transcriptome spatial profiles of functional components of tissues from human and mouse generated using our Whole Transcriptome Atlas RNA assay. In particular, vignette users will be analyzing data from the mouse brain. This content is similar to the vignette available with the package upon installation.
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Instructors

Bioconductor workflows

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

Bioconductor logo This is a self-study session. Please work through the materials on your own, and ask the instructors for help if you get stuck or have any questions!
Tutorial: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
Description: The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.
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From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

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Tutorial: From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline
Description: RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.
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Using singscore to predict mutations in AML from transcriptomic signatures

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Tutorial: Using singscore to predict mutations in AML from transcriptomic signatures
Description: Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single-sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through The Cancer Genome Atlas we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within specific subsets of cancers, and as demonstrated, singscore has the ability to identify samples where alternative mutations/genetic lesions appear to drive transcriptional programs.
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fluentGenomics: A plyranges and tximeta workflow

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Tutorial: fluentGenomics: A plyranges and tximeta workflow
Description: An extended workflow using the plyranges and tximeta packages for fluent genomic data analysis. Use tximeta to correctly import RNA-seq transcript quantifications and summarize them to gene counts for downstream analysis. Use plyranges for clearly expressing operations over genomic coordinates and to combine results from differential expression and differential accessibility analyses.
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MungeSumstats: Standardise the format of GWAS summary statistics

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Tutorial: MungeSumstats: Standardise the format of GWAS summary statistics
Description: The package is designed to handle the lack of standardisation of output files by the GWAS community. The MRC IEU Open GWAS team have provided full summary statistics for >10k GWAS, which are API-accessible via the ieugwasr and gwasvcf packages. But these GWAS are only standardised in the sense that they are VCF format, and can be fully standardised with MungeSumstats. MungeSumstats provides a framework to standardise the format for any GWAS summary statistics, including those in VCF format, enabling downstream integration and analysis. It addresses the most common discrepancies across summary statistic files, and offers a range of adjustable Quality Control (QC) steps.
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R for Mass Spectrometry

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Tutorial: R for Mass Spectrometry
Description: This material introduces participants to the analysis and exploration of mass spectrometry (MS) based proteomics data using R and Bioconductor. The course will cover all levels of MS data, from raw data to identification and quantitation data, up to the statistical interpretation of a typical shotgun MS experiment and will focus on hands-on tutorials. At the end of this course, the participants will be able to manipulate MS data in R and use existing packages for their exploratory and statistical proteomics data analysis.
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