Biology subjects

DAI, Q.

Publications and source records attributed to DAI, Q..

3 recordsLinked to original sources

bioRxiv2023

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

Numerous tools have been developed to infer active cell-cell communication (CCC) events, which are essential for understanding biological processes and diseases. However, existing downstream methods for assessing the relationships between CCC events and biological conditions lack clear interpretation, fail to adjust for confounders, and ignore dependencies among CCC events. To address these limitations, we introduce STACCato, a Supervised Tensor Analysis tool designed to identify Condition-related Cell-cell communication events. STACCato employs a tensor-based regression model to enable statistical inference related to the relationships between biological conditions (e.g., disease status, tissue types) and specific CCC events, while adjusting for confounders and CCC dependencies. Through extensive simulations and real-world applications on scRNA-seq datasets of lupus and autism, we demonstrate that STACCato consistently provides improved inference of condition-related CCC events compared to alternative methods. The computational tool implementing the STACCato framework is available on GitHub.

bioinformatics↗

bioRxiv2020

Augmenting Vascular Disease Diagnosis by Vasculature-awareUnsupervised Learning

Vascular diseases are among the leading causes of death and threaten human health worldwide. Imaging examination of vascular pathology with reduced invasiveness is challenging due to the intrinsic vasculature complexity and the non-uniform scattering from bio-tissues. Here, we report VasNet, a vasculature-aware unsupervised learning algorithm that augments pathovascular recognition from small sets of unlabeled fluorescence and digital subtraction angiography (DSA) images. The VasNet adopts the multi-scale fusion strategy with a domain adversarial neural network (DANN) loss function that induces biased pattern reconstruction, by strengthening the features relevant to the retinal vasculature reference while weakening the irrelevant features. VasNet delivers outputs of "Structure + X", where X refers to multi-dimensional features such as blood flows, the distinguishment of blood dilation and its suspicious counterparts, and the dependence of new pattern emergence on a disease progression, which may assist the discovery of novel diagnostics. Therefore, explainable imaging output from VasNet and other algorithm extensions hold the promise to revolutionize the practice of medical diagnosis, as it improves performance while reduces the cost on human expertise, equipment exquisite and time consumption.

bioengineering↗

bioRxiv2019

Overcoming tissue scattering in wide-field deep imaging by extended detection and computational reconstruction

Compared to the golden technique of point-scanning multiphoton microscopy, line-scanning temporal focusing microscopy (LTFM) is competitive in high imaging speed while maintaining tight axial confinement. However, considering its wide-field detection mode, LTFM suffers from shallow penetration depth as a result of crosstalk induced by tissue scattering. In contrast to the spatial filtering based on confocal slit detection, we propose the extended detection LTFM (ED-LTFM), the first technique to extract signals from scattered photons and thus effectively extend the imaging depth. By recording a succession of line-shape excited signals in 2D and reconstructing signals under Hessian regularization, we can push the depth limitation in scattering tissue imaging. We valid the concept with numerical simulations, and demonstrate the performance of enhanced imaging depth in in vivo imaging of mouse brains.

neuroscience↗