Autor: |
Pan, Yue, Wang, Xusheng, Sun, Jiao, Liu, Chunyu, Peng, Junmin, Li, Qian |
Předmět: |
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Zdroj: |
Communications Biology; 4/24/2024, Vol. 7 Issue 1, p1-9, 9p |
Abstrakt: |
Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL. Presenting a deconvolution algorithm to dissect the bulk proteome by leveraging the information shared between the transcriptome and proteome, the output can be used for further downstream analyses, such as cs-protein Quantitative Trait Loci (cspQTL) mapping and cell type-specific pathology. [ABSTRACT FROM AUTHOR] |
Databáze: |
Complementary Index |
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