Biology subjects

Chau, T. N.

Publications and source records attributed to Chau, T. N..

3 recordsLinked to original sources

bioRxiv2025

PLM-OMG: Protein Language Model-Based Ortholog Detection for Cross-Species Cell Type Mapping

Understanding conserved and divergent cell types across plant species is essential for ad- vancing comparative genomics and improving crop traits. Accurate and scalable ortholog detection is central to this goal, particularly in cross-species single-cell analysis. However, conventional methods are time-consuming and perform poorly with distantly related species, limiting their effectiveness. To address these limitations, we introduce PLM-OMG, a protein language model-based framework for orthogroup classification and cross-species cell type mapping. We benchmark five deep learning models including ESM2, ProGen2, ProteinBERT, ProtGPT2, and LSTM, using a curated 15-species dataset and large-scale monocot and dicot datasets from PLAZA. Transformer-based models, particularly ProtGPT2 and ESM2, achieve superior accuracy and generalization across evolutionary distances. Our results show that PLM-OMG enables scalable and reusable orthogroup detection without recomputing existing groups, significantly reducing computational overhead and highlighting its potential to transform cross-species transcriptomic analysis in plant genomics.

bioinformatics↗

bioRxiv2025

scCoBench: Benchmarking single cell RNA-seq co-expression using promoter-reporter lines

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for uncovering transcriptomic heterogeneity and reconstructing gene regulatory networks in complex tissues. However, the sparsity, high noise levels, and dropout events inherent to scRNA-seq data pose challenges for accurate inference of gene-gene relationships. In this study scCoBench, we systematically benchmark correlation metrics, pseudo bulk analysis, and imputation methods using promoter-reporter and native gene pairs as internal controls to evaluate the performance of ten widely used gene-gene co-expression measurements. Interestingly, we found that commonly used data scaling and normalization approaches lead to lower correlation between promoter reporter and native gene pairs in most of the co-expression methods. Moreover, we assess the impact of five popular imputation techniques, including scImpute, SAVER, Autoencoder (AE), Variational Autoencoder (VAE), and Generative Adversarial Network (GAN) on recovering biologically relevant co-expression patterns. Our results demonstrate that imputation models not only markedly enhance correlation between each promoter-reporter and native gene pair but also increase the number of cells co-expressing both genes. Imputation also improved transcription factor target gene correlations and revealed stronger associations among genes within the same protein complex. This work highlights the utility of promoter-reporter systems for benchmarking computational methods and underscores the potential of deep learning-based imputation to improve the biologically relevant signals of scRNA-seq data.

bioinformatics↗

bioRxiv2025

Cell and nuclear size are associated with chromosomal instability and tumorigenicity in cancer cells that undergo whole genome doubling

Whole genome doubling (WGD) is a frequent event in cancer evolution associated with chromosomal instability, metastasis, and poor prognosis. While the genomic consequences of WGD are well documented, the effects of non-genetic alterations that accompany WGD, such as changes to cell and nuclear size, on tetraploid (4N) cancer cell physiology are less understood. Here, we show that cell and nuclear volume do not always scale with DNA content after WGD in cancer cells, resulting in 4N cells that differ in size. We find that small size is associated with enhanced cell fitness, mitotic fidelity, and tumorigenicity in 4N cancer cells and with poor patient survival in WGD-positive human cancers. Overall, these results suggest that cell and nuclear size contribute to the tumorigenic potential of 4N cancer cells and could be an important prognostic marker in human tumors that undergo WGD. Statement of SignificanceWe report that WGD generates tetraploid cancer cells that vary in size, with larger cells displaying high chromosomal instability and smaller cells exhibiting high fitness and tumorigenicity. Furthermore, WGD status and cancer cell nuclear size in human tumors correlated with patient survival, demonstrating the clinical relevance of this association.

cancer biology↗