Dongyuan Song

The paper introduces ATLAS, a method to map transcriptional differences across genetic backgrounds in mouse brains, revealing significant cell type distribution changes between sexes and genetic models.

This paper introduces spEnhance, a computational framework that improves spatial resolution and reliability of spot-level spatial omics data across multiple modalities.

spAlignDE aligns spatial omics data across samples and modalities, improving differential expression analysis by accounting for alignment errors and cell-type composition.

scDesignPop generates realistic population-scale scRNA-seq data for power analysis, benchmarking, and privacy protection by simulating genetic effects and cell-type-specific eQTLs.

spCorr identifies spatially varying gene correlations in spatial transcriptomics data, revealing biologically meaningful patterns and improving detection power while controlling FDR.

APALORD is an R-based tool for analyzing differential alternative polyadenylation in long-read RNA-seq data, identifying polyadenylation sites and quantifying their usage across biological conditions.

The paper introduces Worm Perturb-Seq (WPS) to perform high-resolution RNA-seq on perturbed C. elegans, identifying gene regulatory networks and showing pairwise modularity in nuclear hormone receptor regulation.

The paper introduces CORAL, a deep generative model for spatial multiomics data that deconvolves low-resolution data into single-cell profiles and identifies spatial domains and cell-cell interactions.

The paper analyzes DNA binding effects of 92 human homeodomain mutants, identifying 14 novel specificity-determining residues and highlighting limitations of variant effect prediction tools.

This study benchmarks 14 computational methods for identifying spatially variable genes and peaks using simulated and real-world spatial transcriptomics and ATAC-seq data, finding spatialDE2 performs best and highlighting the need for specialized algorithms for peaks.

scReadSim generates synthetic scRNA-seq and scATAC-seq reads with user-specified ground truths, enabling benchmarking of computational tools.

The paper introduces a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.

ClusterDE identifies reliable markers and validates clusters in omics data by controlling false discovery rates through synthetic control, improving accuracy in single-cell, spatial, and bulk transcriptomics.

The paper introduces scDesign3, a statistical simulator that generates realistic single-cell and spatial omics data and infers biologically meaningful parameters for benchmarking computational tools.

The paper introduces scGTM, a model for capturing interpretable gene expression trends along pseudotime, showing it can reveal molecular insights in single-cell data.

scSampler is a fast diversity-preserving subsampling method for single-cell transcriptomic data that outperforms existing methods in speed and accuracy.

The paper discusses the controversy around zero-inflation in scRNA-seq data, evaluates impacts of non-biological zeros, and benchmarks different data types.

Clipper provides a p-value-free FDR control method for high-throughput data from two conditions, outperforming existing approaches without relying on p-values or distributional assumptions.

scDesign2 generates high-fidelity synthetic single-cell gene expression data with gene correlations, enabling benchmarking of experimental protocols and computational methods.

PseudotimeDE identifies differentially expressed genes along cell pseudotime using single-cell RNA-seq data with well-calibrated p-values, outperforming existing methods in false discovery rate control and power.

scPNMF selects informative genes from scRNA-seq data to improve cell type distinction and enable alignment with targeted gene profiling data.