Summary

We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent sub-types, calculate subtype proportions in individual samples, and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics, and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts.

Availability and implementation

http://bioconductor.org/packages/debCAM

Supplementary information

Supplementary dataSupplementary data are available at Bioinformatics online.

中文翻译：

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debCAM：用于复杂组织的完全无监督反卷积的生物导体 R 包。

概括

我们开发了一种完全无监督的反卷积方法，可根据批量表达谱将复杂组织分解为分子独特的组织或细胞亚型。我们实现了一个 R 包，通过 Convex Analysis of Mixtures (debCAM) 进行反卷积，它可以自动检测组织/细胞特异性标记，确定组成亚型的数量，计算单个样本中的亚型比例，并估计组织/细胞特异性表达配置文件。我们展示了 debCAM 在基因表达、甲基化、蛋白质组学和成像数据方面的性能和生物医学效用。通过增强的数据预处理和先验知识整合，debCAM 软件工具将允许生物学家在许多生物医学环境中对组织重塑进行更全面、更公正的表征。

可用性和实施

http://bioconductor.org/packages/debCAM

补充资料

补充数据补充数据可在生物信息学在线获得。