Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty DOI Creative Commons
Wei Zhang, Yaxin Xu, Xiaoying Zheng

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(6)

Опубликована: Сен. 23, 2024

Abstract Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity diversity. Precise identification cell types essential establishing a robust foundation downstream analyses prerequisite understanding heterogeneous mechanisms. However, accuracy existing warrants improvement, highly accurate often impose stringent equipment requirements. Moreover, unsupervised learning-based approaches are constrained by need to input number prior, which limits their widespread application. In this paper, we propose novel algorithm framework named WLGG. Initially, capture underlying nonlinear information, introduce weighted distance penalty term utilizing Gaussian kernel function, maps data from low-dimensional space high-dimensional linear space. We subsequently Lasso constraint on regularized graphical model enhance its ability characteristics. Additionally, utilize Eigengap strategy predict obtain predicted labels via spectral clustering. The experimental results 14 test datasets demonstrate superior clustering WLGG over 16 alternative methods. Furthermore, analysis, including marker gene identification, pseudotime inference, functional enrichment analysis based similarity matrix algorithm, substantiates reliability offers valuable insights into biological dynamic processes regulatory

Язык: Английский

Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions DOI Creative Commons
Deepti Bhusal, Shakya Wije Munige,

Zongkai Peng

и другие.

Analytical Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 25, 2025

The Single-probe single-cell mass spectrometry (SCMS) is an innovative analytical technique designed for metabolomic profiling, offering a miniaturized, multifunctional device capable of direct coupling to spectrometers. It ambient leveraging microscale sampling and nanoelectrospray ionization (nanoESI), enabling the analysis cells in their native environments without need extensive sample preparation. Due its miniaturized design versatility, this allows applications diverse research areas, including metabolomics, quantification target molecules single cell, MS imaging (MSI) tissue sections, investigation extracellular live spheroids. This review explores recent advancements Single-probe-based techniques applications, emphasizing potential utility advancing methodologies bioanalysis.

Язык: Английский

Процитировано

0
ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction DOI
Xiaoshu Zhu, Liquan Zhao, Fei Teng

и другие.

Interdisciplinary Sciences Computational Life Sciences, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

0
scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data DOI Creative Commons

Yunpei Xu,

Shaokai Wang,

Qilong Feng

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Авг. 30, 2024

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods often rely on one-time clustering using partial or global gene expression. However, these may be overlooked during phase, posing challenges their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based most differential signals in each cluster effectively separate achieve We benchmark scCAD 25 real-world scRNA-seq datasets, demonstrating its superior performance compared 10 state-of-the-art methods. In-depth case studies across diverse including mouse airway, brain, intestine, human pancreas, immunology data, clear renal carcinoma, showcase scCAD's efficiency scenarios. Furthermore, can correct annotation identify immune subtypes associated with disease, thereby offering valuable insights into disease progression.

Язык: Английский

Процитировано

3
Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty DOI Creative Commons
Wei Zhang, Yaxin Xu, Xiaoying Zheng

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(6)

Опубликована: Сен. 23, 2024

Abstract Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity diversity. Precise identification cell types essential establishing a robust foundation downstream analyses prerequisite understanding heterogeneous mechanisms. However, accuracy existing warrants improvement, highly accurate often impose stringent equipment requirements. Moreover, unsupervised learning-based approaches are constrained by need to input number prior, which limits their widespread application. In this paper, we propose novel algorithm framework named WLGG. Initially, capture underlying nonlinear information, introduce weighted distance penalty term utilizing Gaussian kernel function, maps data from low-dimensional space high-dimensional linear space. We subsequently Lasso constraint on regularized graphical model enhance its ability characteristics. Additionally, utilize Eigengap strategy predict obtain predicted labels via spectral clustering. The experimental results 14 test datasets demonstrate superior clustering WLGG over 16 alternative methods. Furthermore, analysis, including marker gene identification, pseudotime inference, functional enrichment analysis based similarity matrix algorithm, substantiates reliability offers valuable insights into biological dynamic processes regulatory

Язык: Английский

Процитировано

0