Sijie Chen

The paper introduces UniCoord, a joint-VAE model that learns a universal coordinate system for single-cell transcriptomic data to capture heterogeneity and enable data generation and analysis.

This paper introduces HapHiC, a reference-independent allele-aware scaffolding tool that improves chromosome assignment and contig ordering, and applies it to construct a haplotype-resolved allotriploid genome.

scDirect identifies key transcription factors driving cell state transitions using single-cell multi-omics data, showing robust performance in benchmarking and real-world applications.

The paper constructs a generic chromatin accessibility reference (cPeaks) from bulk ATAC-seq data to improve scATAC-seq analysis and cell type identification.

STEM maps single-cell and spatial transcriptomics data using transfer learning to create spatially aware embeddings and predict pseudo-spatial adjacencies.

The paper introduces hECA, a cell-centric human Ensemble Cell Atlas, which integrates scRNA-seq data from multiple studies to enable targeted data retrieval, multi-view presentations, and customizable reference creation for cell type annotations.

The paper introduces a new statistic for efficiently detecting repetitive sequences in genomic data and demonstrates its effectiveness across multiple scenarios.