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Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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Updated: Jan 17, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

通过RECODE平台对单细胞数据进行全面的降噪.

¹Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan.

Cell reports methods

|September 18, 2025

概括

此摘要是机器生成的。

这项研究将单细胞RNA测序 (RNA-seq) 的工具RECODE增强到iRECODE,减少技术和批量噪音. 这提高了跨转录组,表观组和空间数据的分析准确性.

关键词:

CP:计算生物学计算机生物学 CP:系统生物学系统生物学批量纠正批量纠正这是维度的诅咒.放弃放弃放弃放弃高维统计的高维统计.归算是指指责一个人.降低噪音减少噪音噪声差异稳定正常化规范化一个单细胞的Hi-C.单细胞的奥米克.空间转录学空间转录学

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

相关实验视频

Last Updated: Jan 17, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 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

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

单细胞测序提供了深刻的生物学见解,但受到技术噪音和批量效应的影响.
这些文物掩盖了细胞结构,使罕见的细胞类型识别和多数据集比较变得复杂.
现有的方法很难在各种单细胞数据类型中全面解决噪声问题.

研究的目的:

升级RECODE工具,同时减少单细胞数据中的技术和批量噪声.
将RECODE的适用性扩展到RNA测序之外的各种单细胞模式.
提高噪声减轻技术的准确性和计算效率.

主要方法:

开发iRECODE,这是RECODE算法的扩展,用于联合降噪.
适应RECODE平台用于单细胞高通量染色体构造捕获 (Hi-C) 和空间转录组学数据.
算法改进以提高降噪精度和计算性能.

主要成果:

在单细胞数据集中,iRECODE有效地减少了技术和批量噪声.
增强的RECODE平台在转录基因组,表观基因组和空间数据中表现出强大的性能.
在计算效率和分析准确度方面取得了显著的改进.

结论:

升级后的RECODE平台,包括iRECODE,为单细胞分析中的噪声减轻提供了多功能和强大的解决方案.
这种工具可以更可靠地检测罕见细胞类型,并促进准确的数据集交叉比较.
增强的RECODE可以从多种单细胞数据模式中获得更深入的生物学见解.