Data Insights

These projects support researchers and computational experts to advance tools and resources that make it possible to gain greater insights into health and disease from existing single-cell biology datasets.

Showing 38 results

A Probabilistic Framework for RNA Velocity Statistical Tests Using Pyro

Data Insights (Cycle 1)

This project will develop two complementary approaches that allow characterization and interpretability of RNA Velocity that are statistically grounded.

Luca Pinello, PhD

Massachusetts General Hospital

Eli Bingham, PhD

Broad Institute

Gioele La Manno, MD, PhD

Swiss Federal Institute of Technology Lausanne

Advanced Search Functionality for Cell Atlases

Data Insights (Cycle 2)

This project will develop computational tools that will allow for fast and intuitive access to cell atlases through a web browser.

Martin Hemberg, PhD

Brigham and Women's Hospital

Benchmarking of scRNA-Seq to Improve Human Health

Data Insights (Cycle 1)

This project will advance a coherent methodological framework to analyze the effect of single-cell variability on patient phenotypes.

Elizabeth Purdom, PhD

University of California, Berkeley

Beyond Data Repositories: Decentralized Analysis of the Human Cell Atlas

Data Insights (Cycle 1)

This project will build a new software package, screfpy, to enable standardized access for researchers analyzing atlas datasets of single-cell genomics and for transferring knowledge from these datasets to new query datasets.

Fabian Theis, PhD

Helmholtz Munich

Nir Yosef, PhD

Weizmann Institute of Science, Rehovot

Deep and Standardized Single-Cell Annotations with CITE-seq

Data Insights (Cycle 1)

This project will develop tools specifically tailored to protein data, including normalization and annotation, and leverage public databases to create a corpus of well-annotated single-cell data with deep and standardized annotations.

Evan Newell, PhD

Fred Hutchinson Cancer Center

Raphael Gottardo, PhD

Lausanne University Hospital University of Lausanne

Detecting Signaling Among Single Cells from Spatial Transcriptomic Data

Data Insights (Cycle 2)

This project will develop CytoSignal and VeloCytoSignal, two computational tools that detect cell-cell signaling interactions and their dynamics at single-cell resolution from spatial transcriptomic data.

Joshua Welch, PhD

University of Michigan

Jialin Liu, MS

University of Michigan

Differential Expression Analysis of Single-Cell RNA-seq Data

Data Insights (Cycle 2)

This project will develop statistically robust and computationally efficient methods for performing differential expression analysis from single-cell RNA-sequencing data.

Jimmie Ye

University of California, San Francisco

Dissecting Human Tissue Pathology with Integrated sc- and Spatial RNA-seq

Data Insights (Cycle 2)

This project will develop robust computational tools for spatial transcriptomic analysis of human tissue pathology, with an initial focus in cancer biology.

Omer Bayraktar, PhD

Wellcome Sanger Institute

Oliver Stegle, PhD

European Molecular Biology Laboratory

Efficient Data Structures for Single-Cell Data Integration

Data Insights (Cycle 1)

This project democratizes atlas-scale integration of single-cell data with a new sparse matrix format that requires a fraction of the space of current standards without compromising performance.

Timothy Triche, PhD

Van Andel Research Institute

Enhancing Rigor and Reliability of Single-Cell Data Science

Data Insights (Cycle 1)

This project will develop software packages and graphical user interfaces to enhance the rigor and reliability of single-cell data analysis and tool benchmarking.

Jingyi Jessica Li, PhD

University of California, Los Angeles

Explainable AI for Single-Cell Regulatory Genomics

Data Insights (Cycle 2)

This project will develop explainable AI techniques for single-cell regulatory genomics which will enable more rigorous and interpretable data-driven discoveries in single-cell regulatory genomics.

Su-In Lee, PhD

University of Washington

Jian Ma, PhD

Carnegie Mellon University

Gene Set Enrichment Analysis for Single-Cell Data (scGSEA)

Data Insights (Cycle 1)

This project aspires to develop and distribute a new version of Gene Set Enrichment Analysis specifically tailored for use with single-cell data.

Jill Mesirov, PhD

University of California, San Diego

Harmony Powers Robust and Scalable Single-Cell Integration

Data Insights (Cycle 1)

This project outlines three independent strategies to improve Harmony, a popular and well-benchmarked method, to enable larger and more complex analyses of single-cell data.

Ilya Korsunsky, PhD

The Brigham and Women’s Hospital, Inc.

Soumya Raychaudhuri, MD, PhD

The Brigham and Women’s Hospital, Inc.

Martin Hemberg, PhD

The Brigham and Women’s Hospital, Inc.

Improved Quantitative Accuracy for Single-Cell Proteomics

Data Insights (Cycle 2)

This project will create and optimize new computational methods to significantly improve quantitative accuracy of single-cell proteomics data.

Samuel Payne, PhD

Brigham Young University

William Noble, PhD

University of Washington

Michael Shortread, PhD

University of Wisconsin

Improving Product-space Forms for Single-Cell Data Representation and Understanding

Data Insights (Cycle 1)

This project will develop new variational autoencoders for embedding heterogeneous single-cell data into product spaces of appropriate mixed curvatures and dimensions.

Olgica Milenkovic, PhD

University of Illinois at Urbana Champaign

Minji Kim, PhD

The Jackson Laboratory

Integrated Cross-Species Analysis of Muscle Tissue Biology

Data Insights (Cycle 2)

This project will create and disseminate tools to enable cross-species and cross-measurement platform analysis of cell types and tissue environment-specific gene regulation in the muscle.

Iwijn de Vlaminck

Cornell University

Bo Wang, PhD

Stanford University

Benjamin Cosgrove, PhD

Cornell University

Integrating Multiple Datasets to Better Understand Human Reproductive Function

Data Insights (Cycle 1)

This project aims to develop CellPhoneDB-v5 and use it to investigate cell-cell communication across the whole reproductive system over the lifespan by integrating multiple publically available datasets.

Roser Vento-Tormo, PhD

Genome Research Limited

Sarah Teichmann, PhD

Wellcome Sanger Institute

Luz Garcia-Alonso, PhD

Wellcome Sanger Institute

Learning Regulatory Codes from Single-Cell Multi-Omics Atlases

Data Insights (Cycle 2)

This project will predict, model, and compare gene regulatory networks and enhancer logic in brain and cancer cell types by re-using human and mouse single-cell multi-omics atlases.

Stein Aerts

VIB-KU Leuven

Light and Scalable Statistical Approximations of Cell Atlases

Data Insights (Cycle 2)

This project will develop light and scalable approximations of cell atlases to democratize online access, web visualization, and machine learning, which will provide biological insights across organs and organisms.

Fabio Zanini, PhD

University of New South Wales

Machine Learning for Spatially-Resolved Integration of Single-Cell Data

Data Insights (Cycle 2)

This project will develop a publicly-available software package to integrate diverse spatial and nonspatial single-cell data modalities, creating a comprehensive spatial representation of a tissue of interest.

Barbara E Engelhardt, PhD

Stanford University

Mapping Enhancer-gene Regulation in Various Cell Types

Data Insights (Cycle 1)

This project aims to develop and apply computational pipelines to predict enhancer-gene connections in hundreds of cell types based on single-cell measurements of chromatin accessibility (scATAC-seq).

Jesse Engreitz, PhD

Stanford University

MetaPlaq: Integrative Single-Cell Meta-Analysis for Atherosclerosis

Data Insights (Cycle 1)

This project will will establish a multi-omic single-cell atlas of adult human coronary and carotid artery atherosclerosis by meta-analyzing publicly available datasets across age, sex, and ancestries.

Clint Miller, PhD

University of Virginia

Chongzhi Zang, PhD

University of Virginia

Sander W. van der Laan, PhD

University Medical Center Utrecht

Methods and Software for Decontamination of Single-Cell Data

Data Insights (Cycle 1)

This project will develop and test various approaches for estimating contamination in other single-cell data modalities such as single-cell ATAC-seq (scATAC-seq) and data with Antibody-Derived Tags (ADTs).

Joshua Campbell, PhD

Boston University

Masanao Yajima, PhD

Boston University

Multi-omics Integration with Batch-adversarial Neural Networks

Data Insights (Cycle 1)

This project will devise effective machine learning approaches for multi-omics data integration, allowing for flexible, paired, or single-modality input data and adversarial batch effect removal.

Uwe Ohler, PhD

Max Delbruck Center for Molecular Medicine

Multi-Sample Clustering for Large Atlas-Scale Spatial Data

Data Insights (Cycle 2)

This project will develop a spatially-aware unsupervised clustering algorithm and open-source software in R/Bioconductor scalable for analysis of large atlas-scale spatial transcriptomics data with multiple samples.

Stephanie Hicks

Johns Hopkins Bloomberg School of Public Health

Shila Ghazanfar

The University of Sydney

Multiscale Data Integration for Single-Cell Spatial Genomics

Data Insights (Cycle 1)

This project will develop novel methodologies for multiscale data integration of single-cell spatial genomics.

Shila Ghazanfar, PhD

University of Sydney

Pathway Motifs and the Mosaic Structure of Cell States

Data Insights (Cycle 2)

This project will show how cell states can be understood as mosaic combinations of pathway-specific motifs and develop computational tools that make it possible to apply this framework to other cellular systems.

Michael Elowitz

Caltech

Matt Thomson

Caltech

Preview Beyond Health: Building a Blood Cell Atlas Across Health and Disease

Data Insights (Cycle 2)

This project will build a 14 million blood cell reference cell atlas that will incorporate diverse healthy donors and 27 distinct disease states.

Alexandra-Chloe Villani, PhD

Massachusetts General Hospital, Harvard Medical School

Gary Reynolds, MD, PhD

Massachusetts General Hospital, Newcastle University

Refining Cell Type Standards and Harmonizing Cell Atlases

Data Insights (Cycle 2)

This project will develop CellTypist.org 2.0, an extended cross-tissue database accompanied by a cell type harmonization pipeline, which will automatically curate, standardize and annotate cell types across cell atlases.

Sarah Teichmann, PhD

Wellcome Sanger Institute

Kerstin Meyer, PhD

Wellcome Sanger Institute

scLIB: Single-Cell Living Benchmark for Multi-Sample Studies

Data Insights (Cycle 2)

This project will develop a series of benchmarking frameworks for case-control and multi-perturbation studies that will increase the utilization of public single-cell data for precision medicine research.

Jean Yang, PhD

The University of Sydney

Sequence-to-Function AI Models for Single-Cell Genomics

Data Insights (Cycle 2)

This project will develop sequence-to-function neural network models for learning the relationship between regulatory genomic DNA sequence and dynamic cellular behavior at single cell resolution.

Sara Mostafavi, PhD

University of Washington

Su-In Lee, PhD

University of Washington

Single-Cell Exploration of Ovarian Aging Across Vertebrates

Data Insights (Cycle 2)

This project will characterize the genomic regulatory networks underlying ovarian aging in humans and vertebrate model organisms via integrative meta-analysis of ovarian single-cell RNA-seq datasets.

Bérénice Benayoun, PhD

University of Southern California

Single-Cell T-cell Receptor and Expression Grouped Ontologies (STEGO)

Data Insights (Cycle 1)

This project aims to leverage single-cell data richness to visualize key interplays between T-cell subtypes, as well as other immune cells.

Pieter Meysman, PhD

University of Antwerp

Kris Laukens, PhD

University of Antwerp

Benson Ogunjimi, PhD

University of Antwerp

SpatialData: A FAIR Framework for Multimodal Spatial Omics

Data Insights (Cycle 2)

This project will enable interoperable multimodal spatial omics data storage and analysis by building open software and standards that join the imaging (napari, OME) and single-cell (scverse) communities.

Josh Moore

German BioImaging - Society for Microscopy and Image Analysis

Oliver Stegle, PhD

European Molecular Biology Laboratory

Kevin Yamauchi, PhD

Swiss Federal Institute of Technology in Zürich

Statistic Methods to Illuminate Splicing and RNA Regulation in Single-Cell Biology

Data Insights (Cycle 1)

This project will develop and disseminate algorithms that will empower researchers to identify new functions for RNAP at a precision and scale not previously possible.

Julia Salzman, PhD

Stanford University

David Tse, PhD

Stanford University

Statistical Methods to Investigate Phenotypic Variation

Data Insights (Cycle 1)

This project will develop a measurement error model to estimate gene-gene correlations from scRNA-seq data and detect correlations that are otherwise hidden by technical limitations.

Sunduz Keles, PhD

University of Wisconsin

Emery Bresnick

University of Wisconsin

Understanding the History of Mutations and Cancer Cells

Data Insights (Cycle 1)

This project will model the relationship between cell type chromatin accessibility and somatic mutation landscapes to infer the cell-of-origin in 38 tumor types and better understand the history of cancer progression.

Alexander Tsankov, PhD

Icahn School of Medicine at Mount Sinai

Rosa Karlic, PhD

University of Zagreb

Unraveling Immunogenomic Diversity in Single-Cell Data

Data Insights (Cycle 1)

This project will implement a precise quantification of immune gene expression at the allele, gene, and functional level.

Katharina Imkeller, PhD

Johann Wolfgang Goethe University Frankfurt

Federico Marini, PhD

University Medical Center of the Johannes Gutenberg University Mainz

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