Yingying Wei

The paper identifies a widespread transcript leakage issue in spatial transcriptomics and introduces DeLeakage, a Bayesian method that improves data cleaning and cell-type annotation.

This study uses single-cell RNA sequencing to analyze tissue regeneration in two jellyfish species, identifying conserved and lineage-specific genes involved in regeneration processes.

This paper presents a chromosome-scale genome assembly of the rare Camellia granthamiana using PacBio and Omni-C data, providing a resource for conservation and phylogenetic studies.

This paper presents a high-quality chromosome-level genome assembly of Liolophura japonica, a common chiton, using PacBio HiFi and Omni-C data.

This study presents the first chromosomal-level genome assembly and SNP analysis of the black-faced spoonbill, providing genomic resources for conservation and biological research.

This paper presents a chromosomal-level genome assembly of the long-spined sea urchin D. setosum using PacBio and Omni-C technologies, providing a resource for ecological and evolutionary studies.

This study presents the genome assembly of the edible jelly fungus Dacryopinax spathularia using PacBio HiFi and Omni-C data, revealing a high-quality genome with 11,510 protein-coding genes.

This paper presents the first chromosomal-level genome assembly of the golden birdwing butterfly, providing a high-quality resource for studying its biology and conservation.

This study presents a high-quality chromosomal-level genome assembly of the milky mangrove Excoecaria agallocha using PacBio and Omni-C data, revealing 73,740 protein-coding genes.

TAPE deconvolves bulk RNA-seq data into cell-type fractions and gene expression using a deep autoencoder, showing better performance and robustness compared to existing methods.

The paper introduces a Bayesian framework called Prism Vote to improve risk prediction in multi-population genome-wide sequencing data by integrating subpopulation-specific predictors.

This paper introduces BUSseq, a Bayesian model that corrects batch effects in scRNA-seq data while clustering cell types and imputing missing values.

This paper introduces a statistical method to detect cell-type-specific risk-CpG sites in epigenome-wide association studies, improving detection power and enabling cell-type resolution.