Biology subjects

Kwok, A. W. C.

Publications and source records attributed to Kwok, A. W. C..

3 recordsLinked to original sources

bioRxiv2025

Denoising image-based spatial transcriptomics data with DenoIST

Image-based spatial transcriptomics (IST) technologies provide unprecedented resolution of gene expression in tissue sections, but suffer from contamination of cells gene expression profiles due to imperfect cell segmentation. We present DenoIST (Denoising Image-based Spatial Transcriptomics data), a new computational tool that accurately identifies and removes contaminating transcripts from IST datasets. DenoIST models the observed transcript counts using a Poisson mixture model that explicitly accounts for local neighbourhood contamination. Applied to multiple real IST datasets of varying cell densities, DenoIST restores gene expression specificity and clarifies local biological structures by identifying and filtering transcripts spilt over from neighbouring cells. The denoised data enable more consistent and interpretable cell type annotation by minimising conflicting gene expression profiles, reducing the prevalence of hybrid or ambiguous cell types, and enhancing the contrast between distinct functional compartments. Overall, we demonstrate that DenoIST can be integrated to existing IST analysis workflows to improve biological interpretability and robustness of IST data.

bioinformatics↗

bioRxiv2024

Going beyond cell clustering and feature aggregation: Is there single cell level information in single-cell ATAC-seq data?

1Single-cell Assay for Transposase Accessible Chromatin with sequencing (scATAC-seq) has become a widely used method for investigating chromatin accessibility at single-cell resolution. However, the resulting data is highly sparse with most data entries being zeros. As such, currently available computational methods for scATAC-seq feature a range of transformation procedures to extract meaningful information from the sparse data. Most notably, these transformations can be categorized into: 1) feature aggregation with known biological associations, 2) pseudo-bulking cells of similar biology, and 3) binarisation of count data. These strategies beg the question of whether or not scATAC-seq data actually has usable single-cell and single-region information as intended from the assay. If we can go beyond aggregated features and pooled cells, it opens up the possibility of more complex statistical tasks that require that degree of granularity. To reach the finest possible resolution of single-cell, single-region information there are inevitably many computational challenges to overcome. Here, we review the major data analysis challenges lying between raw data readout and biological discovery, and discuss the limitations of current data analysis approaches. Lastly, we conclude that chromatin accessibility profiling at true single-cell resolution is not yet achieved with current technology, but that it may be achieved with promising developments in optimising the efficiency of scATAC-seq assays.

bioinformatics↗

bioRxiv2021

MQuad enables clonal substructure discovery using single cell mitochondrial variants

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

bioinformatics↗