有的方法不需要单细胞细胞类型表达谱或细胞marker 进行完全推断比如CDSeq,linseed.大部分算法结合单细胞测序细胞表达谱的先验知识对bulk 数据进行推断,如 CIBESORT,CIBESORTX,CellMix,MuSiC,SCDC,TED.去推断细胞成分比例,我可以用如下数学公式进行描述这个Deconvolution问题:Y=WX+E Y 表示 N个基因 m 个样本的bulk ...
贝叶斯棱镜由Deconvolution modules反卷积模块和Embedding learning module嵌入学习模块组成。反卷积模块对来自scRNA-seq的细胞类型特异性表达谱进行建模,以共同估计肿瘤(或非肿瘤)样品的大量RNA-seq表达的细胞类型组成和细胞类型特异性基因表达的后验分布。嵌入学习模块使用期望最大化 (EM) 来使用恶性基因程序的线性组合来近...
infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial.#We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated ...
▶Bulk组学对下一步分析的意义:通过去卷积分析(deconvolution analysis)推测参与PAH相关基因共表达模块的细胞类型,以及如何进一步结合细胞类型和组织特异性信息来丰富对PAH的理解。 Comment 随着单细胞转录组学以及空间转录组学技术的不断普及,单纯的bulk RNA-seq可能无法满足高质量研究的要求。若想在这一领域脱颖而出,...
RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions
MuSiC(MUlti-Subject SIngle Cell deconvolution)是来自宾夕法尼亚大学Biostatistics, Epidemiology and Informatics系的Mingyao Li课题组于2019年发表于Nature Communication的一个工具R包,可根据单细胞转录组信息推测Bulk RNA-seq细胞组成。而后,该团队又于2022年在Briefing in bioinformatics发表了扩展版本MuSiC2,可以考虑更...
The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a c
BayesPrism 使用从匹配或相似组织类型收集的scRNA-seq样本,对大量RNA-seq(和空间转录组学)进行细胞类型和基因表达反褶积。将scRNA-seq作为先验信息,估计P(θ,Z|X,ϕ),即细胞类型分数θ和细胞类型特异性基因表达Z在每个群体中的联合后验分布,条件是参考ϕ和每个观察群体X。
MuSiC(MUlti-Subject SIngle Cell deconvolution)是来自宾夕法尼亚大学Biostatistics, Epidemiology and Informatics系的Mingyao Li课题组于2019年发表于Nature Communication的一个工具R包,可根据单细胞转录组信息推测Bulk RNA-seq细胞组成。而后,该团队又于2022年在Briefing in bioinformatics发表了扩展版本MuSiC2,可以考虑更...
seq per sample. Furthermore, as more knowledge on cellular and spatial heterogeneity is acquired by scRNA-seq and spatial approaches, bulk RNA-seq profiles can be better interpreted, e.g., by computational deconvolution of the bulk profile [15]. Hence, bulk RNA-seq will remain a central ...