Viewing Several Interactive Plots with Plotly and Trelliscopejs: A Lipidomics Data Example Jeremy John Selva National University of Singapore Abstract In a typical lipidomics workflow, many quality checks are done to evaluate samples measured for a particular transition. This results in several quality control plots for each transition which are converted as pages in a pdf file. However, as technologies improved, many transitions can be measured in one sample at a fast rate.
The current state of single-cell proteomics data analysis Christophe Vanderaa,Laurent Gatto UCLouvain Abstract Mass spectrometry-based single-cell proteomics is a fast growing field that enables quantitative proteomics at single-cell resolution. This is achieved thanks to technical improvements that dramatically increase the throughput, sensitivity and accuracy of the technology. However, little efforts have been invested in understanding single-cell proteomics data analysis. We have addressed this need with scp, an R/Bioconductor package that standardizes single-cell proteomics data analysis.
Poster: SpaceLINCS: an R package to visualise correlations between experimental gene expression and L1000 perturbagens profiles
SpaceLINCS: an R package to visualise correlations between experimental gene expression and L1000 perturbagens profiles Ivo Kwee, Axel Martinelli, Layal Abo Khayal and Murodhzon Akhmedov. BigOmics Analytics, Switzerland Abstract Accessing the collection of perturbed gene expression profiles, such as the LINCS L1000 connectivity map, is usually performed at the individual dataset level, followed by a summary performed by counting individual hits for each perturbagen. With the SpaceLINCS R package we present an alternative approach that combines rank correlation and gene set enrichment analysis to identify meta-level enrichment at the perturbagen level and, in the case of drugs, at the mechanism of action (MoA) level.
MetaboAnnotation: an R package simplifying metabolite annotation Andrea Vicini,Carolin Huber,Michael Witting,Johannes Rainer Institute for Biomedicine, Eurac Research, 39100 Bolzano, Italy. Abstract We present the R package MetaboAnnotation designed to assist the end-user in metabolite annotation. Specifically, MetaboAnnotation provides the high level functions matchValues() and matchSpectra() to perform MS1- and MS2-based annotation. The former involves matching of measured m/z values and/or retention times of LC–MS features against reference values; the latter a comparison of potentially generated experimental MS2 spectra against reference spectra, either from in-house or external libraries.
LMWiRe: an R package for Linear Modeling of Wide Responses based on ASCA family of methods Michel Thiel,Nadia Benaiche,Manon Martin,Sébastien Franceschini,Robin Van Oirbeek,Bernadette Govaerts UCLouvain Abstract Many modern analytical methods are used to analyze samples issued from an experimental design; for example in medical, biological, chemical or agronomic fields. Those methods generate most of the time highly multivariate data like spectra or images, where the number of variables (descriptors) tends to be much larger than the number of experimental units.
Introducing the hermes package for analyzing and reporting RNA-seq data Daniel Sabanes Bove Roche Abstract The hermes package published on Bioconductor provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed RNA-seq data. Data can be imported from SummarizedExperiment as well as matrix objects and can be annotated from BioMart. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported.
hCoCena: Horizontal integration and analysis of transcriptomics datasets Marie Oestreich,Lisa Holsten,Shobhit Agrawal,Kilian Dahm,Philipp Koch,Han Jin,Matthias Becker,Thomas Ulas Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Abstract Transcriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding and comparison of different conditions and phenotypes. Since large study designs with a broad variety of conditions are costly and laborious, extensive comparisons are hindered when utilizing only a single data set. Thus, there is an increased need for tools that allow the integration of multiple transcriptomic data sets with subsequent joint analysis, which can provide a more systematic understanding of gene co-expression and co-functionality within and across conditions.
Differential transcript usage analysis for dynamic biological processes Jeroen Gilis,Koen Van den Berge,Lieven Clement Ghent University Abstract Trajectory inference has been instrumental for modelling dynamic changes in biological systems during processes like cell differentiation, tissue development and response to external stimuli. Downstream of trajectory inference, one key challenge is the identification of marker genes that are associated with the dynamic process at hand. In this context, our Bioconductor package tradeSeq is a highly flexible and scalable tool that allows for testing both within-lineage and between-lineage changes in gene expression.
Differential analysis of labelled MS-based proteomics data with msqrob2 Stijn Vandenbulcke Universiteit Gent Abstract Mass spectrometry (MS) enables direct and comprehensive quantification of the proteome in biological samples, gaining a better understanding of biological systems. The quantification of the protein abundances can be done either with label-free workflows or by labelling with TMT or iTRAQ, which allows users to effectively multiplex multiple samples in the same MS-run. We have adapted msqrob2, an R/Bioconductor tool for differential analysis of label-free MS-based proteomics data, towards the analysis of labelled MS-based proteomics data.
decoupleR: ensemble of computational methods to infer biological activities from omics data Pau Badia i Mompel Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg 69120, Germany Abstract Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework.
Poster: Cancer signatures for reproducible gene expression analysis data: the computational way to achieve precision medicine
Cancer signatures for reproducible gene expression analysis data: the computational way to achieve precision medicine Stefania Pirrotta,Laura Masatti,Fabiola Pedrini,Chiara Romualdi,Enrica Calura University of Padova Abstract Cancer is a complex disease, characterized by extensive genomic aberrations with an evident impact on gene expression regulation and cell biological processes. Many studies and some clinical trials proposed gene expression signatures as a valuable tool for understanding cancer mechanisms and defining subtypes. Moreover, transcriptional signatures have the potential to show cancer activities while they are happening.
A Bioconductor workflow for dynamic spatial proteomics Lisa M Breckels,Oliver Crook,Laurent Gatto,Kathryn S Lilley University of Cambridge Abstract Spatial proteomics is the systematic study of proteins and their assignment to subcellular niches including organelles. The field has continued to grow in importance as many diseases result from protein mislocalisation. The knowledge of subcellular localisation of proteins is extremely desirable to biologists, as it can assist elucidation of a protein’s role within the cell, as proteins are spatially organised according to their function and specificity of their molecular interactions.