Xi Xian Chen

The paper uses snRNA-seq in Drosophila to study how AD-associated proteins affect peripheral tissues, finding distinct impacts of Aβ42 and Tau on sensory neurons and non-neuronal tissues.

The paper maps synaptic input architecture of LC-NE neurons in larval zebrafish, revealing broad-yet-sparse input organization across brain regions.

This paper shows that selective GSK3 inhibition with BRD0705 supports self-renewal of mouse stem cells and enables co-culture of naive and primed stem cells while preserving their identities.

This study identifies distinct cellular states and molecular mechanisms in the developing ventricular zone, revealing key regulators of neural stem cell and ependymal cell differentiation.

The paper introduces seqspec, a machine-readable specification for genomics assays to standardize preprocessing and enable tracking and comparison of assays.

The paper profiles chromatin accessibility dynamics in zebrafish embryos using single-cell ATAC-seq to identify enhancers and transcription factor motifs involved in early cell fate decisions.

scTIE integrates temporal multimodal data and infers gene regulatory relationships predictive of cellular state changes, outperforming existing methods in data integration and biological signal preservation.

The paper introduces CREMA, a framework for mapping cis-regulatory circuits using single-cell multiome data without peak-calling, and validates it using mouse pituitary data.

scMultiSim simulates multi-modal single-cell data by integrating cell-cell interactions and gene regulatory networks, enabling broader benchmarking of computational methods.

ISSAAC-seq jointly profiles chromatin accessibility and gene expression in single nuclei, revealing cell types, regulatory elements, and chromatin dynamics in mouse cerebral cortex.

scMoMaT integrates single-cell multi-omics data under mosaic integration using matrix tri-factorization, uncovering modality-specific biomarkers for improved cell type annotation.

This study compares bulk and single-cell RNAseq for identifying differentially expressed genes in adult hippocampal NSCs, finding single-cell methods miss high-fold-change genes but can be optimized for in vivo use.

The paper re-analyzes T cell subsets using scRNA-seq, identifies new subpopulations, and shows that gene expression patterns define more homogeneous cell populations than surface markers.

MultiMAP integrates multimodal data by reducing dimensionality and preserving structure, outperforming existing methods in various tasks.

This study investigates how dynamic histone regulations control noise in gene expression by showing that a positive feedback loop between transcriptional activators and histone acetyltransferases generates epigenetic bistability, leading to large noise, which can be reduced by modulating activator binding.

ChIP-BIT2 detects weak transcription factor binding events by integrating ChIP-seq sample and input data using a Bayesian approach.

The paper develops ChIP-GSM, a Bayesian method to infer cis-regulatory modules from ChIP-seq data, showing improved performance in predicting regulatory element activity.

BICORN integrates transcription factor binding and gene expression data to infer sparse cis-regulatory modules and their regulatory networks.

Upfront bulk Tn5 tagging then single-nucleus sorting into plates gives scATAC-seq libraries with higher complexity than Fluidigm C1, resolving 12 clusters among 3,385 mouse splenocytes.

The paper uses single-cell RNA-seq and ATAC-seq to study cardiac progenitor cell fate transitions, identifying roles for Isl1 and Nkx2-5 in lineage commitment and gene regulatory networks.

The paper introduces coupled nonnegative matrix factorizations to integrate single cell RNA-seq and ATAC-seq data by coupling clustering across samples.

This paper introduces scABC, an R package for unsupervised clustering of scATAC-seq data to classify cell types based on open chromatin regions.

Mocap predicts transcription factor binding sites using chromatin accessibility and genomic features, outperforming existing methods for new TF-cell-type combinations.