An interpretable deep learning method for predicting cellular potency categories and absolute developmental potential from single-cell expression data (bioRxiv 2024).
Robust and rapid alignment of single-cell and spatial transcriptomes, enabling single-cell expression analysis in spatial dimensions (Nature Biotechnology 2023).
A method to identify cell states and multicellular ecosystems from bulk, single-cell, and spatially-resolved gene expression data (Cell 2021).
A method to predict the differentiation states of cells from single-cell RNA-sequencing data, without prior knowledge of developmental orderings (Science 2020).
A method to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data (Nature Biotechnology 2019).
Computational “background polishing” to model and eliminate stereotypical sequencing artifacts in next-generation sequencing data (Nature Biotechnology 2016).
A method for accurately quantifying relative proportions of distinct cell types within complex tissue expression profiles (Nature Methods 2015).
PRECOG is a system for querying associations between genomic profiles and cancer outcomes. It enables researchers to query whether, for example, high expression of a gene is prognostic for shorter or longer patient survival (Nature Medicine 2015).
A practical tool for de novo enumeration of genomic fusions and breakpoints from paired-end targeted (or genome-wide) sequencing data (Bioinformatics 2014).
A computational method for identifying clusters of diverse shapes and sizes from large data sets without prior knowledge of cluster number (BMC Bioinformatics 2010).
A rapid and powerful algorithm for identifying perfect and degenerate tandem repeat motifs in protein (and nucleotide) sequence data (BMC Bioinformatics 2007).