A guide to the BRAIN Initiative Cell Census Network data ecosystem

Hawrylycz, Michael; Martone, Maryann E.; Ascoli, Giorgio A.; Bjaalie, Jan G.; Dong, Hong‐Wei; Ghosh, Samik; Gillis, Jesse; Hertzano, Ronna; Haynor, David R.; Hof, Patrick R.; Kim, Yongsoo; Lein, Ed; Liu, Yufeng; Miller, Jeremy A.; Mitra, Partha P.; Mukamel, Eran A.; Ng, Lydia; Osumi-Sutherland, David; Peng, Hanchuan; Ray, Patrick L; Sánchez, Raymond E A; Regev, Aviv; Ropelewski, Alexander J.; Scheuermann, Richard H.; Tan, Shawn Zheng Kai; Thompson, Carol L.; Tickle, Timothy L.; Tilgner, Hagen U; Varghese, Merina; Wester, Brock A.; White, Owen; Zeng, Hongkui; Aevermann, Brian D.; Allemang, David; Ament, Seth A.; Athey, Thomas L.; Baker, Cody; Baker, Katherine; Baker, Pamela; Bandrowski, Anita; Banerjee, Samik; Bishwakarma, Prajal; Carr, A. J. H.; Chen, Min; Choudhury, Roni; Cool, Jonah; Creasy, Heather Huot; D’Orazi, Florence D.; Degatano, Kylee; Dichter, Benjamin; Ding, Song‐Lin; Dolbeare, Tim; Ecker, Joseph R.; Fang, Rongxin; Fillion‐Robin, Jean‐Christophe; Fliss, Timothy P; Gee, James C.; Gillespie, Tom; Gouwens, Nathan W.; Zhang, Guo Qiang; Halchenko, Yaroslav O.; Harris, Nomi L.; Herb, Brian R.; Hintiryan, Houri; Hood, Greg; Horvath, Samantha; Huo, Bing-Xing; Jarecka, Dorota; Jiang, Shengdian; Khajouei, Farzaneh; Kiernan, Elizabeth; Kır, Hüseyin; Kruse, Lauren; Lee, Changkyu; Lelieveldt, Boudewijn P. F.; Li, Yang Eric; Liu, Hanqing; Liu, Lijuan; Mahurkar, Anup; Mathews, James; Mathews, Kaylee L.; Mezias, Christopher; Miller, Michael I.; Mollenkopf, Tyler; Mufti, Shoaib; Mungall, Christopher J.; Orvis, Joshua; Puchades, Maja; Qu, Lei; Receveur, Joseph P.; Ren, Bing; Sjoquist, Nathan; Staats, Brian; Tward, Daniel J.; Velthoven, Cindy T. J. van; Wang, Quanxin; Xie, Fangming; Xu, Hua; Yao, Zizhen; Yun, Zhixi; Zhang, Yun Renee; Zheng, Wenxian; Zingg, Brian

doi:10.1371/journal.pbio.3002133

Cited by 20 publications

(14 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These challenges and requirements are not easily met using existing repositories. Several solutions arose due to these unique requirements, offering access to individual datasets and some larger specialized collections, such as Lung Gene Expression Analysis portal, 9 Allen Brain Map, 10 and the Single Cell Portal. 11 Built-for-purpose portals enable rapid publication of studies and dissemination of unique biological features in specific datasets, but lack the scalability and standardization needed for efficient meta-analysis.…”

Section: Introductionmentioning

confidence: 99%

CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Abdulla,

Aevermann,

Assis

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover(cellxgene.cziscience.com), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

show abstract

Section: Introductionmentioning

confidence: 99%

CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Abdulla,

Aevermann,

Assis

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the main applications of NS-Forest identified marker genes is contributing to the definition of ontological classes of scRNA-seq data-driven cell types that can later be adopted into the oYicial Cell Ontology 19 , as NS-Forest provides the minimum combinations of marker genes that can serve as a set of definitional characteristics of the cell types 19 . Such eYorts have already begun, and NS-Forest has contributed to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem to derive the necessary and suYicient marker gene knowledge 21 . As such, the Provisional Cell Ontology (PCL) is generated in this manner for human, mouse, and marmoset primary motor cortex 22 .…”

Section: Discussionmentioning

confidence: 99%

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

Liu,

Peng,

Pankajam

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

The use of single-cell transcriptomic technologies that quantitively describe cell transcriptional phenotypes using single cell/nucleus RNA sequencing (scRNA-seq) is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in eYiciently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an eYective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and suYicient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements, to select marker gene combinations that exhibit selective expression patterns among closely related cell types and more eYiciently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to1, with a metric of 1 given to markers that are only expressed within their target cell types and not in cells of any other cell types. We have applied NS-Forest v4.0 on scRNA-seq datasets from three human organs, including the brain, kidney, and lung. We observe that NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches on the classification performance with significantly higher F-beta scores.

show abstract

“…This allows datasets previously registered to the CCFv3 to seamlessly integrate with the DevCCF with undistorted stereotaxic spatial coordinates and developmental annotations. This atlas integration will promote large scale data analysis to accommodate rapidly growing cell census data 47 .…”

Section: Discussionmentioning

confidence: 99%

Developmental Mouse Brain Common Coordinate Framework

Kronman,

Liwang,

Betty

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

show abstract

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Cited by 20 publications

References 69 publications

CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

Developmental Mouse Brain Common Coordinate Framework

Contact Info

Product

Resources

About