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In this workshop, we will give an overview of how to perform low-level analyses of genomic data using the grammar of genomic data transformation defined in the plyranges package. We will cover:
The workshop will be a computer lab, in which the participants will be able to ask questions and interact with the instructors.”
There are various annotation packages provided by the Bioconductor project that can be used to incorporate additional information to results from high-throughput experiments. This can be as simple as mapping Ensembl IDs to corresponding HUGO gene symbols, to much more complex queries involving multiple data sources. In this workshop we will cover the various classes of annotation packages, what they contain, and how to use them efficiently.
The goal of this workshop is to introduce Bioconductor packages for finding, accessing, and using large-scale public data resources including the Gene Expression Omnibus GEO, Sequence Read Archive SRA, the Genomic Data Commons GDC, and Bioconductor-hosted curated data resources for metagenomics, pharmacogenomics, and The Cancer Genome Atlas.
In this workshop, we will give a quick overview of the most useful functions in the DESeq2 package, and a basic RNA-seq analysis. We will cover: how to quantify transcript expression from FASTQ files using Salmon, import quantification from Salmon with tximport and [tximeta], generate plots for quality control and exploratory data analysis EDA (also using MultiQC), perform differential expression (DE) (also using apeglm), overlap with other experimental data (using AnnotationHub), and build reports (using ReportingTools and Glimma). We will give a short example of integration of DESeq2 with the zinbwave package for single-cell RNA-seq differential expression. The workshop is designed to be a lab with plenty of time for questions throughout the lab.
In this workshop, 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. 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.
We will introduce the fundamental concepts underlying the
GenomicRanges package and related infrastructure. After a
structured introduction, we will follow a realistic workflow, along
the way exploring the central data structures, including
SummarizedExperiment, and useful operations in the ranges
algebra. Topics will include data import/export, computing and
summarizing data on genomic features, overlap detection, integration
with reference annotations, scaling strategies, and
visualization. Students can follow along, and there will be plenty
of time for students to ask questions about how to apply the
infrastructure to their particular use case.
Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains. Biomarker discovery from large pharmacogenomics datasets. Syllabus & Materials.
This workshop will focus on the challenges encountered when applying machine learning techniques in complex, high dimensional biological data. In particular, we will focus on biomarker discovery from pharmacogenomic data, which consists of developing predictors of response of cancer cell lines to chemical compounds based on their genomic features. From a methodological viewpoint, biomarker discovery is strongly linked to variable selection, through methods such as Supervised Learning with sparsity inducing norms (e.g., ElasticNet) or techniques accounting for the complex correlation structure of biological features (e.g., mRMR). Yet, the main focus of this talk will be on sound use of such methods in a pharmacogenomics context, their validation and correct interpretation of the produced results. We will discuss how to assess the quality of both the input and output data. We will illustrate the importance of unified analytical platforms, data and code sharing in bioinformatics and biomedical research, as the data generation process becomes increasingly complex and requires high level of replication to achieve robust results. This is particularly relevant as our portfolio of machine learning techniques is ever enlarging, with its set of hyperparameters that can be tuning in a multitude of ways, increasing the risk of overfitting when developing multivariate predictors of drug response.
Ludwig Geistlinger and Levi Waldron. Functional enrichment analysis of high-throughput omics data. Syllabus & Materials.
This workshop gives an in-depth overview of existing methods for enrichment analysis of gene expression data with regard to functional gene sets, pathways, and networks.
The workshop will help participants understand the distinctions between assumptions and hypotheses of existing methods as well as the differences in objectives and interpretation of results. It will provide code and hands-on practice of all necessary steps for differential expression analysis, gene set- and network-based enrichment analysis, and identification of enriched genomic regions and regulatory elements, along with visualization and exploration of results.
This workshop demonstrates data management and analyses of multiple
assays associated with a single set of biological specimens, using
MultiAssayExperiment data class and methods. It introduces the
RaggedExperiment data class, which provides efficient and powerful
operations for representation of copy number and mutation and
variant data that are represented by different genomic ranges for
each specimen. “
Das D, Street K, Risso D. Analysis of single-cell RNA-seq data: Dimensionality reduction, clustering, and lineage inference. Workshop Syllabus & Materials.
This workshop will be presented as a lab session (brief introduction followed by hands-on coding) that instructs participants in a Bioconductor workflow for the analysis of single-cell RNA-sequencing data, in three parts:
We will provide worked examples for lab sessions, and a set of stand-alone notes in this repository.
Cytoscape is one of the most popular applications for network analysis and visualization. In this workshop, we will demonstrate new capabilities to integrate Cytoscape into programmatic workflows and pipelines using R. We will begin with an overview of network biology themes and concepts, and then we will translate these into Cytoscape terms for practical applications. The bulk of the workshop will be a hands-on demonstration of accessing and controlling Cytoscape from R to perform a network analysis of tumor expression data.
Cooley N and Wright E. Working with Genomic Data in R with the DECIPHER package. Syllabus & Materials.
DECIPHER is a multifaceted package that includes many tools for working with genome-scale sequence data. Genomic sequences undergo a variety of large-scale mutational processes, including rearrangements, inversions, duplications, insertions, and deletions. Since genomes are often not collinear, it is often useful to map syntenic regions between genomes to facilitate analyses. DECIPHER contains a synteny mapping function that locates syntenic regions among genomes and can be used to identify orthologous genes. Additional functions allow for alignment and downstream analyses of these syntenic regions. This workshop will walk through a complete workflow for analyzing a set of genomes using DECIPHER, starting with importing genomes from local files or external repositories. Synteny will be mapped among multiple genomes and used as the basis for ortholog prediction. We will show how to use these sets of orthologous genes to construct phylogenetic trees representing the evolutionary history of the core-genome and pan-genome.
This workshop will teach the fundamental concepts underlying the DelayedArray framework and related infrastructure. It is intended for package developers who want to learn how to use the DelayedArray framework to support the analysis of large datasets, particularly through the use of on-disk data storage. The first part of the workshop will provide an overview of the DelayedArray infrastructure and introduce computing on DelayedArray objects using delayed operations and block-processing. The second part of the workshop will present strategies for adding support for DelayedArray to an existing package and extending the DelayedArray framework. Students can expect a mixture of lecture and question-and-answer session to teach the fundamental concepts. There will be plenty of examples to illustrate common design patterns for writing performant code, although we will not be writing much code during the workshop.
Once an R package is accepted into Bioconductor, maintaining it is an active responsibility undertaken by the package developers and maintainers. In this short workshop, we will cover how to maintain a Bioconductor package using existing infrastructure. Bioconductor hosts a range of tools which maintainers can use such as daily build reports, landing page badges, RSS feeds, download stats, support site questions, and the bioc-devel mailing list. Some packages have their own continuous integration hook setup on their github pages which would be an additional tool maintainers can use to monitor their package. We will also highlight one particular git practice which need to be done while updating and maintaining your package on out git system.