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      Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics

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          Abstract

          As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

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          Fiji: an open-source platform for biological-image analysis.

          Johannes Schindelin, Ignacio Arganda-Carreras, Erwin Frise … (2019)
          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            SciPy 1.0: fundamental algorithms for scientific computing in Python

            Pauli Virtanen, Ralf Gommers, Travis E. Oliphant … (2020)
            SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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              Comprehensive Integration of Single-Cell Data

              Tim Stuart, Andrew Butler, Paul Hoffman … (2019)
              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Journal
                Title: Annual Review of Neuroscience
                Abbreviated Title: Annu. Rev. Neurosci.
                Publisher: Annual Reviews
                ISSN (Print): 0147-006X
                ISSN (Electronic): 1545-4126
                Publication date Created: July 08 2020
                Publication date (Print): July 08 2020
                Volume: 43
                Issue: 1
                Pages: 441-464
                Affiliations
                [1 ]Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
                [2 ]Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
                [3 ]Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
                [4 ]Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA
                [5 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
                [6 ]Department of Otolaryngology–Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts 02115, USA
                [7 ]Stanford University, Palo Alto, California 94305, USA
                Article
                DOI: 10.1146/annurev-neuro-100119-110036
                SO-VID: a3f3310c-9b13-4cac-a80c-008c56e454a1
                Copyright © © 2020
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