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将基于DNA甲基化的细胞类型反卷积与……统一起来

Unifying DNA methylation-based cell-type deconvolution with .

作者信息

Dietrich Alexander, Willruth Lina-Liv, Pürckhauer Korbinian, Oltmanns Carlos, Witte Moana, Klein Sebastian, Kraft Anke R M, Cornberg Markus, List Markus

机构信息

Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

Core Facility Microbiome, ZIEL Institute for Food & Health, Technical University of Munich, Freising, 85354, Germany.

出版信息

Bioinform Adv. 2025 Sep 1;5(1):vbaf201. doi: 10.1093/bioadv/vbaf201. eCollection 2025.

DOI:10.1093/bioadv/vbaf201
PMID:40926955
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12417072/
Abstract

SUMMARY

Cell-type deconvolution is widely applied to gene expression and DNA methylation data, but access to methods for the latter remains limited. We introduce , a new R package that simplifies access to DNA methylation-based deconvolution methods predominantly for blood data, and we additionally compare their estimates to those from gene expression and experimental ground truth data using a unique matched blood dataset.

AVAILABILITY AND IMPLEMENTATION

is available at https://github.com/omnideconv/deconvMe, the processed blood data is available at https://figshare.com/articles/dataset/methyldeconv_data/28563854/3.

摘要

摘要

细胞类型反卷积广泛应用于基因表达和DNA甲基化数据，但用于后者的方法仍然有限。我们引入了一个新的R包，该包主要针对血液数据简化了基于DNA甲基化的反卷积方法的使用，并且我们还使用一个独特的匹配血液数据集，将其估计值与来自基因表达和实验真值数据的估计值进行了比较。

可用性和实现方式

可在https://github.com/omnideconv/deconvMe获取，处理后的血液数据可在https://figshare.com/articles/dataset/methyldeconv_data/28563854/3获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/885d/12417072/47287419961c/vbaf201f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/885d/12417072/04991016f565/vbaf201f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/885d/12417072/47287419961c/vbaf201f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/885d/12417072/04991016f565/vbaf201f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/885d/12417072/47287419961c/vbaf201f2.jpg

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Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.高维细胞计数数据门控方法的综合评估与实用指南：手工门控、无监督聚类和自动门控。
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