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Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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Updated: Sep 18, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

stGNN:基于深度图形学习和统计建模的空间信息化细胞类型解卷.

Juntong Zhu¹, Daoyuan Wang², Siqi Chen²

¹School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China.

Interdisciplinary sciences, computational life sciences

|June 26, 2025

概括

此摘要是机器生成的。

stGNN是一种新的空间图学习方法,通过整合空间和基因表达信息,准确地解决空间转录组学数据中的细胞类型. 它的性能优于现有的方法,使复杂组织结构的高分辨率分析成为可能.

关键词:

细胞类型的解细胞类型.深度图形学习 (deep graph learning) 是一种深度图形学习的方法.单细胞RNA测序的参考基准是单细胞RNA测序.空间转录组学空间转录组学统计建模统计建模

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

相关实验视频

Last Updated: Sep 18, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

空间转录学 (ST) 彻底改变了组织分析,但缺乏单细胞分辨率.
现有的方法往往忽视空间上下文或未能对准参考数据,限制了细胞类型映射的准确性.

研究的目的:

开发stGNN,一个新的空间信息图学习框架,用于ST数据中准确的细胞类型解卷.
通过整合空间上下文和统计建模来改进细胞类型的分辨率和映射.

主要方法:

开发了一种使用图形卷积网络 (GCN) 和空间和非空间表示的自动编码器的双编码模块.
实施了适应性注意力机制,以整合多个规模的空间结构.
利用负日志概率损失函数来对准ST和单细胞RNA测序 (scRNA-seq) 数据分布.

主要成果:

在六个不同的ST数据集 (10xVisium,Slide-seqV2,Visium HD) 中,stGNN始终超过了七种最先进的方法.
在高分辨率下成功地在小鼠大脑组织中分辨出不同的皮质层.
在不同的ST分辨率上展示了有效的性能.

结论:

stGNN提供了一个强大的,准确的框架,用于分析复杂组织中的细胞类型组成和空间分布.
该方法通过有效利用空间信息和对准参考数据集来提高解卷精度.