Provide Proactive Reproducible Analysis Transparency with Every Publication

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation

Provide proactive reproducible analysis transparency with every publication

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large datasets, a granular understanding of the analysis methodology is an essential component of reproducibility. This article discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation

Abstract 1071: Online resources from the Allen Institute for Immunology: longitudinal profiling of multiple myeloma treatment and the Human Immune System Explorer platform

The Allen Institute for Immunology was founded in 2018 to deeply profile the human immune system in health and disease. As part of our mission to understand how the immune system changes over time in the setting of oncology, we recruited a cohort of patients at diagnosis for multiple myeloma (MM), and followed these subjects throughout induction therapy, autologous stem cell transplant, and after transplant recovery. We sampled the same subjects longitudinally, then performed immune profiling using scRNA-seq on peripheral blood cells, CITE-seq on bone marrow cells, multiple high-dimensional flow cytometry panels, plasma and bone marrow interstitial fluid (BMIF) proteomics, and clinical lab tests. To process, analyze, and distribute this data, we developed the Human Immune System Explorer (HISE) platform, a flexible, scalable, cloud-based framework to enable storage, interactive analysis, visualization, and generation of Certificates of Reproducibility that enable inspection and replay of any step of an analysis workflow. We joined our robust, large-scale analytical platform to public-facing tools, scientific context, and data releases, which now include interactive visualization tools for exploring the effects of MM treatment in detail. We invite oncologists, immunologists, and clinicians to explore this resource and our expanding library of immunology data, insights, and tools at https://explore.allenimmunology.org/