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 20 results

A Probabilistic Framework for RNA Velocity Statistical Tests Using Pyro
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This project will develop two complementary approaches that allow characterization and interpretability of RNA Velocity that are statistically grounded.

Benchmarking of scRNA-Seq to Improve Human Health
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This project will advance a coherent methodological framework to analyze the effect of single-cell variability on patient phenotypes.

Beyond Data Repositories: Decentralized Analysis of the Human Cell Atlas
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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.

Deep and Standardized Single-Cell Annotations with CITE-seq
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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.

Efficient Data Structures for Single-Cell Data Integration
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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.

Enhancing Rigor and Reliability of Single-Cell Data Science
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This project will develop software packages and graphical user interfaces to enhance the rigor and reliability of single-cell data analysis and tool benchmarking.

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

Harmony Powers Robust and Scalable Single-Cell Integration
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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.

Improving Product-space Forms for Single-Cell Data Representation and Understanding
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This project will develop new variational autoencoders for embedding heterogeneous single-cell data into product spaces of appropriate mixed curvatures and dimensions.

Integrating Multiple Datasets to Better Understand Human Reproductive Function
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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.

Mapping Enhancer-gene Regulation in Various Cell Types
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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).

MetaPlaq: Integrative Single-Cell Meta-Analysis for Atherosclerosis
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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.

Methods and Software for Decontamination of Single-Cell Data
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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).

Multi-omics Integration with Batch-adversarial Neural Networks
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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.

Multiscale Data Integration for Single-Cell Spatial Genomics
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This project will develop novel methodologies for multiscale data integration of single-cell spatial genomics.

Single-Cell T-cell Receptor and Expression Grouped Ontologies (STEGO)
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This project aims to leverage single-cell data richness to visualize key interplays between T-cell subtypes, as well as other immune cells.

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

Statistical Methods to Investigate Phenotypic Variation
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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.

Understanding the History of Mutations and Cancer Cells
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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.

Unraveling Immunogenomic Diversity in Single-Cell Data
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This project will implement a precise quantification of immune gene expression at the allele, gene, and functional level.

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