Jingyi Jessica Li

MorphoANOVA: Dissecting Histology-Gene Expression Relationships in Spatial Transcriptomics

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

Spatial Single-Cell Mapping of Transcriptional Differences Across Genetic Backgrounds in Mouse Brains

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: assessing scRNA-seq analysis stability under bulk-derived between-sample variation

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

Evolutionary dynamics of temporal transcription factor series in the insect visual brain

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: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches

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: An end-to-end framework for designing optimized aggregated spatial transcriptomics experiments

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

Identifying Context-Specific Cell-Cell Interaction Genes Without Ligand-Receptor Databases from Spatial Transcriptomics

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

Information Geometry Reconciles Discrete and Continuous Variation in Single-Cell and Spatial Transcriptomic Analysis

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

SnakeAltPromoter Facilitates Differential Alternative Promoter Analysis

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 single-cell RNA-seq for power analysis, benchmarking, and privacy protection

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

Systematic mapping of emergent transcriptional states in interacting single-cell dyads by Cell-Cell-seq

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: Comparative Isoform Profiling Across Cell Populations from Long-Read Single-Cell Transcriptomes

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

CellScope: High-Performance Cell Atlas Workflow with Tree-Structured Representation

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

spCorr: flexible and scalable inference of spatially varying correlation in spatial transcriptomics

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

mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis

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

ClipperQTL: ultrafast and powerful eGene identification method

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

Co-fluctuations of genes in single cells predict transcriptome-wide outcomes to perturbations

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

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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.

Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models

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.

Semisynthetic Simulation for Microbiome Data Analysis

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

Systematic dissection of epigenetic age acceleration in normal breast tissue reveals its link to estrogen signaling and cancer risk

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.

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms

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

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

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

Targeting circadian transcriptional programs in triple negative breast cancer through a cis-regulatory mechanism

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: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

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

Systematic Evaluation of Cell Type Deconvolution Methods for Plasma Cell-free DNA

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.

Benchmarking computational methods to identify spatially variable genes and peaks

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: a single-cell RNA-seq and ATAC-seq read simulator

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

Decoding Heterogenous Single-cell Perturbation Responses

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

Synthetic Control Enables Reliable Cluster Validation and Marker Discovery in Omics Data: From Single-Cell and Spatial to Population-Scale

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.