Package demo

Using Tercen To Run Bioconductor Pipelines: Save Time And Share Data Easy With Collaborators.***

Using Tercen To Run Bioconductor Pipelines: Save Time And Share Data Easy With Collaborators.*** Author(s): Wes Wilson, Alex Gouy Affiliation(s): University of Pennsylvania Twitter: @WesleyWilson Are you one of few people in your lab writing pipelines and analyzing data? Do you have a backlog of raw data you haven't even started the QC on yet? do you want to easily share data with collaborators and team members but dont want to make 100 custom graphs with ggplot and then re-export your R markdown every 2 hours you get an email asking to change an axis?

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Using And Extending Biomedical Ontologies With Ontoproc**

Using And Extending Biomedical Ontologies With Ontoproc** Author(s): Vincent James Carey, Sara Stankiewicz Affiliation(s): Harvard Medical School An ontology, as studied in information science, is a formalization of a conceptual domain. A typical ontology consists of a controlled vocabulary including term definitions, and a system of relationships among terms and concepts to which these terms are associated. The ontoProc package addresses the ingestion, visualization, and manipulation of basic biomedical ontologies.

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SpatialOmicsOverlay***

SpatialOmicsOverlay: Overlay of Structurally Profiled Regions of Interest on GeoMx DSP Tissue Images*** Author(s): Maddy Griswold, David Allen Henderson, Nicole Ortogero Affiliation(s): NanoString Technologies Spatial biology is changing the way we view biology. We can now look at a tissue in terms of compartments such as cells, tissue structure, or disease state. Compartments are selected on a tissue image by a scientist or pathologist but then that image is not typically used after compartment selection.

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SpatialFeatureExperiment: An S4 Class Bringing Geospatial Tools To Spatial Omics***

SpatialFeatureExperiment: An S4 Class Bringing Geospatial Tools To Spatial Omics*** Author(s): Lambda Moses, Lior Pachter Affiliation(s): California Institute of Technology Twitter: @LambdaMoses Spatial localization of gene expression and histological images in spatial transcriptomics present many opportunities unavailable to non-spatial single cell RNA-seq (scRNA-seq). Some existing data analysis packages for spatial transcriptomics have been inspired by spatial statistics originally developed for geospatial data, such as Moran's I, Ripley's K, and Gaussian process regression.

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Spatial Analysis Of High Dimensional In Situ Cytometry Data***

Spatial Analysis Of High Dimensional In Situ Cytometry Data*** Author(s): Ellis Patrick, Nick Canete, Elijah Willie, Alexander Nicholls Affiliation(s): The University of Sydney Twitter: @TheEllisPatrick Understanding the interplay between different types of cells and their immediate environment is critical for understanding the mechanisms of cells themselves and their function in the context of human diseases. Recent advances in high dimensional in situ cytometry technologies have fundamentally revolutionized our ability to observe these complex cellular relationships providing an unprecedented characterisation of cellular heterogeneity in a tissue environment.

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SEESAW: Statistical Estimation Of Allelic Expression Using Salmon And Swish**

SEESAW: Statistical Estimation Of Allelic Expression Using Salmon And Swish** Author(s): Euphy Wu, Noor Pratap Singh, Mohsen Zakeri, Rob Patro, Michael I Love Affiliation(s): UNC-Chapel Hill Twitter: @mikelove There are a number of existing bioinformatic pipelines for assessing allelic imbalance of expression. These often consist of genomic alignment of RNA-seq reads, removal of multi-mapping bias, counting of reference and alternate alleles, statistical inference on the allelic ratio: null hypothesis testing of the ratio representing balanced expression of the two alleles.

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Rcwl/RcwlPipelines: Use R To Build, Read, Write, And execute CWL Workflows***

Rcwl/RcwlPipelines: Use R To Build, Read, Write, And execute CWL Workflows*** Author(s): Qian Liu, Qiang Hu Affiliation(s): Roswell Park Comprehensive Cancer Center The Common Workflow Language (CWL) is used to provide portable and reproducible data analysis workflows across different tools and computing environments. We have developed Rcwl, an R interface to CWL, to provide easier development, use and maintenance of CWL pipelines from within R. We have also collected nearly 200 pre-built tools and pipelines in RcwlPipelines, ready to be queried and used by researchers in their own analysis.

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Nullranges: Modular Workflow For Overlap Enrichment**

Nullranges: Modular Workflow For Overlap Enrichment** Author(s): Wancen Mu, Eric Scott Davis, Mikhail Dozmorov, Stuart Lee, Michael I Love, Douglas Phanstiel Affiliation(s): University of North Carolina at Chapel Hill There are many well-established packages for overlap enrichment in R/Bioconductor. These can be used to establish if two sets of genomic ranges are distributed closer to each other than expected under a particular null hypothesis. In this software demo we will focus on two branches of specification of null hypothesis for distribution of genomic ranges, where we find it is beneficial to separate generation of null ranges from the enrichment analysis steps.

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Introduction To Using Bioconductor’s Carpentries-Style Training Materials*

Introduction To Using Bioconductor’s Carpentries-Style Training Materials* Author(s): Jenny Drnevich, Charlotte Soneson, Laurent Gatto, Kevin Christophe Rue-Albrecht, Robert Castelo, Toby Hodges Affiliation(s): University of Illinois, Urbana-Champaign The Bioconductor teaching committee has produced three sets of training materials using the Carpentries pedagogical methods: 1) Introduction to data analysis with R and Bioconductor, 2) The Bioconductor project and 3) RNA-seq analysis with Bioconductor. These are freely available for anyone who wants to use all or part of them in their own teaching.

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Identifying Differentially Methylated Genomic Regions With Comethdmr**

Identifying Differentially Methylated Genomic Regions With Comethdmr** Author(s): Gabriel J. Odom, Lissette Gomez, Tiago Chedraoui Silva, Ingrid Gonzalez, Suky Martinez, Jermaine Jones, Lily Wang Affiliation(s): Florida International University, The University of Miami Twitter: @RevDocGabriel Because of the complex interdependencies inherent in epigenomics data, identifying differentially methylated regions (DMRs) remains a challenging task. However, many current methods of DMR detection focus on finding regions with a few highly significant differentially methylated CpGs, thereby failing to detect regions with small but pervasive differential methylation patterns.

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Genomicsupersignature: Interpretation Of RNA-Seq Experiments Through Robust, Efficient Comparison To Public Databases**

Genomicsupersignature: Interpretation Of RNA-Seq Experiments Through Robust, Efficient Comparison To Public Databases** Author(s): Sehyun Oh, Levi Waldron, Sean Davis Affiliation(s): City University of New York Twitter: @drsehyun Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We develop a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their datasets such as literatures, gene sets, and MeSH terms.

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Genomicdistributions: Fast, Easy, And Flexible Summary And Visualization Of Genomic Regions*

Genomicdistributions: Fast, Easy, And Flexible Summary And Visualization Of Genomic Regions* Author(s): Kristyna Kupkova, Jose Verdezoto Mosquera, Jason P. Smith, Michal Stolarczyk, Tessa L. Danehy, John T. Lawson, Bingjie Xue, John T. Stubbs, Nathan LeRoy, Nathan C. Sheffield Affiliation(s): University of Virginia Twitter: @KupkovaKristyna The output of epigenetic studies are genomic region sets represented by genomic coordinates with a shared property, e.g. open chromatin regions identified by ATAC-seq in a given cell type.

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Deploy Custom Bioconductor Data Science VM On Azure With Linux Extension***

Deploy Custom Bioconductor Data Science VM On Azure With Linux Extension*** Author(s): Erdal Cosgun,Nitesh Turaga Affiliation(s): Roswell Park Comprehensive Cancer Center Azure virtual machine (VM) extensions are small applications that provide post-deployment configuration and automation tasks on Azure VMs. For example, if a virtual machine requires software installation, antivirus protection, or the ability to run a script inside it, you can use a VM extension. [1] Users can run Azure VM extensions by using the Azure CLI, PowerShell, Azure Resource Manager templates (ARM templates), and the Azure portal.

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Cola: A General Framework For Consensus Partitioning**

Cola: A General Framework For Consensus Partitioning** Author(s): Zuguang Gu Affiliation(s): German Cancer Research Center Consensus partitioning is the most widely applied approach to reveal subgroups by summarizing a consensus classification from a list of individual classifications generated by repeatedly executing clustering on random subsets of the data. We implemented an R/Bioconductor package, cola, that provides a general framework for consensus partitioning. With cola, various parameters and methods can be user-defined and easily integrated into different steps of an analysis, e.

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CITE-Viz: Reproducing The Flow Cytometry Workflow Using CITE-Seq Data***

CITE-Viz: Reproducing The Flow Cytometry Workflow Using CITE-Seq Data*** Author(s): Garth Kong, Thai T Nguyen, Wesley K Rosales, Anjali D Panikar, John H W Cheney, Brittany M Curtiss, Sarah A Carratt, Theodore P Braun, Julia E Maxson Affiliation(s): Oregon Health and Science Twitter: @KongGarth One of the most essential steps in single-cell sequencing analysis is the classification of cell clusters. The identification of cluster identities is essential to set up downstream analyses (e.

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Bioconductor Experimenthub And Annotationhub -- How To Use And How To Contribute**

Bioconductor Experimenthub And Annotationhub -- How To Use And How To Contribute** Author(s): Lori Ann Shepherd, Kayla Interdonato Affiliation(s): Roswell Park Comprehensive Cancer Center AnnotationHub and ExperimentHub are vital Bioconductor infrastructure. They provide access to user contributed and core provided data resources available for public use. This examines the Hubs from two scenarios: a user querying and accessing data and a package maintainer wanting to contribute and distribute data using the Hubs.

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Analyzing Cellular Heterogeneity Across Time And Across Biological Interventions***

Analyzing Cellular Heterogeneity Across Time And Across Biological Interventions*** Author(s): Xinge Wang, Shang Gao Affiliation(s): University of Illinois at Chicago Single-cell RNA sequencing (scRNA-seq) is a powerful experimental approach to study cellular heterogeneity. Single cell transcriptome analyses typically involve analyzing differences between biological conditions (disease vs. normal or distinct biological interventions) and across time points (longitudinal transcriptome assessments). Workflows and computational toolkits are needed to address the following essential biological questions.

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Accessing Human Cell Atlas Data Locally And On The Anvil Cloud**

Accessing Human Cell Atlas Data Locally And On The Anvil Cloud** Author(s): Martin Morgan, Kayla Interdonato, Nitesh Turaga, Marcel Ramos, Vincent James Carey Affiliation(s): Roswell Park Comprehensive Cancer Center This short worksop demonstrates how three R/Bioconductor packages provide access to the increasing number of single cell gene expression data sets produced as part of the Human Cell Atlas. The cellxgenedp package (https://bioconductor.org/packages/cellxgenedp) allows discovery, download for import into R / Bioconductor as SingleCellExperiment objects, and visual exploration through the cellxgene data portal of more than 300 consistently-processed datasets.

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A Framework For Multiplexed Image Processing And Spatial Analysis***

A Framework For Multiplexed Image Processing And Spatial Analysis*** Author(s): Nils Eling, Jonas Windhager Affiliation(s): University of Zurich, ETH Zurich Twitter: @NilsEling Simultaneous profiling of the spatial distributions of multiple biological molecules at single-cell resolution has recently been enabled by the development of highly multiplexed imaging technologies. Extracting and analyzing biologically relevant information contained in complex imaging data requires the use of a diverse set of computational tools and algorithms.

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