Jingyi Jessica Li

scDeepSim generates synthetic single-cell data with controlled biological and technical factors to benchmark single-cell analysis methods.

DE-Rigor-Agent automates differential expression analysis for bulk RNA-seq by evaluating method suitability, reducing false discovery rates and improving statistical power across diverse datasets.

MorphoANOVA systematically analyzes spatial gene-expression patterns in relation to tissue morphology, identifying genes with location-specific associations and spatial expression beyond morphology.

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.

scStable evaluates scRNA-seq analysis stability by generating synthetic samples with between-sample variation, showing better performance than existing methods in gastric cancer data.

This study reconstructs the evolutionary history of temporal transcription factor series in insect visual brains, showing conserved patterns and species-specific variations that influence neuronal diversity.

IntegrateRigor optimizes batch integration for cell identity recovery in single-cell and spatial transcriptomics data, revealing cancer-immune interface niches previously obscured by integration biases.

CIPHER optimizes aggregate spatial transcriptomics experiments by jointly designing gene signatures and cell embeddings, improving decoding accuracy and experimental reliability.

CellNeighborEX v2 identifies context-specific cell-cell interaction genes from spatial transcriptomics data without relying on ligand-receptor databases.

GAIA provides a framework for analyzing single-cell and spatial transcriptomic data by reconciling discrete and continuous gene expression variation through information geometry.

SnakeAltPromoter provides a reproducible framework for differential alternative promoter analysis using RNA-seq, benchmarked against CAGE data and scalable for large datasets.

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

Cell-Cell-seq maps transcriptional states in interacting cell pairs by profiling defined dyads, revealing emergent gene programs and cross-cell coordination in tumor-T cell interactions.

Hypatia compares isoform complexities across cell populations using long-read single-cell transcriptomes, improving detection of isoform usage shifts and diversity.

CellScope improves cell atlas construction by integrating UMAP with hierarchical clustering, enhancing clustering performance, visualization, and interpretability across single-cell datasets.

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

mcRigor improves metacell partitioning reliability by detecting heterogeneous metacells and optimizing hyperparameters for single-cell RNA-seq and multiome data.

ClipperQTL identifies eGenes efficiently by reducing permutations needed for large datasets while maintaining performance comparable to FastQTL.

CIPHER uses gene co-fluctuations in unperturbed cells to predict transcriptome-wide responses to perturbations, outperforming existing methods and revealing key regulatory structures.

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.

This study uses LLMs and semantic similarity to analyze children's scientific drawings, finding consistent representations and identifying factors influencing their drawing accuracy and focus.

This paper reviews simulation methods for microbiome data analysis, demonstrating their use in benchmarking, power analysis, and evaluating key properties of simulated data.

The paper evaluates epigenetic clocks in normal breast tissue, identifies age acceleration linked to estrogen signaling and cancer risk, and highlights cellular heterogeneity's role in age estimation.

The paper identifies extensive isoform diversity in the developing human neocortex and links it to neuropsychiatric disorder risk mechanisms.

scCDC detects and corrects contamination in scRNA-seq and snRNA-seq data by identifying contamination-causing genes, achieving better accuracy than existing methods.

This paper identifies a cis-regulatory mechanism in triple-negative breast cancer where targeting circadian transcription factors with small molecules represses a circadian transcriptional program, offering a new therapeutic strategy.

APIR aggregates database search algorithms for proteomics to increase peptide identification while controlling FDR, outperforming individual methods on standard and real data.

This study benchmarks five methylation-based methods for cell type deconvolution of plasma cell-free DNA using deep whole-genome bisulfite sequencing data from 35 human cell types.

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.