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
Luca Pinello Eli Bingham Gioele La Manno
This project will develop two complementary approaches that allow characterization and interpretability of RNA Velocity that are statistically grounded.
This project will advance a coherent methodological framework to analyze the effect of single-cell variability on patient phenotypes.
Fabian Theis Nir Yosef
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.
Evan Newell Raphael Gottardo
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.
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.
Jingyi Jessica Li
This project will develop software packages and graphical user interfaces to enhance the rigor and reliability of single-cell data analysis and tool benchmarking.
This project aspires to develop and distribute a new version of Gene Set Enrichment Analysis specifically tailored for use with single-cell data.
Ilya Korsunsky Soumya Raychaudhuri Martin Hemberg
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.
Olgica Milenkovic Minji Kim
This project will develop new variational autoencoders for embedding heterogeneous single-cell data into product spaces of appropriate mixed curvatures and dimensions.
Roser Vento-Tormo Sarah Teichmann Luz Garcia-Alonso
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.
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).
Clint Miller Chongzhi Zang Sander W. van der Laan
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.
Joshua Campbell Masanao Yajima
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).
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.
This project will develop novel methodologies for multiscale data integration of single-cell spatial genomics.
Pieter Meysman Kris Laukens Benson Ogunjimi
This project aims to leverage single-cell data richness to visualize key interplays between T-cell subtypes, as well as other immune cells.
Julia Salzman David Tse
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.
Sunduz Keles Emery Bresnick
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.
Alexander Tsankov Rosa Karlic
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.
Katharina Imkeller Federico Marini
This project will implement a precise quantification of immune gene expression at the allele, gene, and functional level.
Sorry, there are currently no results that match those criteria. Please try selecting fewer filters or clearing all search terms.