Voyager: Exploratory spatial data analysis from geospatial to spatial -omics** Author(s): Lambda Moses,Kayla Jackson,Laura Luebbert,Pétur Helgi Einarsson,Pall Melsted,Lior Pachter Affiliation(s): California Institute of Technology Social media: https://twitter.com/LambdaMoses With the rise of spatial transcriptomics, many methods have been written for specialized tasks in spatial transcriptomics data analysis, such as finding spatially variable genes, finding spatial regions, deconvoluting Visium spots, data integration with other modalities and with multiple tissue slices, and identifying interactions between cell types.
Reproducible and programmatic analysis of flow cytometry experiments with the cytoverse** Author(s): Arpan Neupane,Mike Jiang,Malisa Smith,Greg Finak,Andrew McDavid Affiliation(s): Ozette technologies Social media: https://twitter.com/EquivMeasures Multi-dimensional flow cytometry remains the gold standard for assessing the presence and abundance of cellular subpopulations in basic science and clinical and translational studies. There is increasing recognition of the importance of replacing ad-hoc GUI tools with automated and programmable workflows. For several years, Bioconductor has hosted the cytoverse: a set of interoperable packages, including flowCore, flowWorkspace, opencyto, ggcyto, cytoqc and others, which all facilitate programmatic flow cytometry analysis in R.
Mariner: Explore the Hi-Cs** Author(s): Eric Scott Davis,Manjari Kiran,Sarah M Parker,Nicole Kramer,Douglas Phanstiel Affiliation(s): The University of North Carolina at Chapel Hill Social media: https://twitter.com/ericscottdavis1 3D chromatin structure plays an integral, yet incompletely understood role in the long-distance regulation of genes by enhancers and repressors. Disruption or aberrant formation of these long-range interactions can result in developmental abnormalities and diseases, such as cancer. Therefore, deriving biological insights from 3D chromatin structure experiments, such as Hi-C or Micro-C, is essential for understanding and correcting human disease.
High-Performance Computing in R for Genomic Research** Author(s): Jiefei Wang Affiliation(s): University at Texas Medical Branch High-performance computing(HPC) has become an essential topic for handling large high-throughput data and bringing complex algorithms to life. However, the intricate nature of parallelization structures often hinders people from implementing the correct parallel computing cluster in R. In this talk, we will introduce the modern parallel framework package BiocParallel and its utility packages SharedObject and RedisParam.
Bridges from python to Bioconductor: applications in genetics and single-cell genomics** Author(s): Vincent James Carey Affiliation(s): Channing Division of Network Medicine, Harvard Medical School Multilingual data science strategies can increase efficiency of discovery by taking advantage of diverse data management and analysis strategies. In this workshop we will examine interplay between R, Python, and Apache Spark in genetic and single-cell applications. CITE-seq studies simultaneously quantify surface protein and mRNA abundance in single cells.
A Bioconductor-style differential expression analysis powered by SPEAQeasy* Author(s): Daianna Gonzalez-Padilla,Renee Garcia-Flores,Nicholas J Eagles,Leonardo Collado Torres Affiliation(s): Lieber Institute for Brain Development Social media: https://twitter.com/daianna_glez With the increase in research projects involving data from RNA sequencing (RNA-seq), there has been an increase in the availability of software designed to perform the required preparation steps prior to statistical analyses such as differential expression. Many of these programs focus on performing only one of the mandatory steps in the pre-analysis, which makes it necessary to use multiple programs in one single pipeline.