In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3217-3217
Abstract:
Tissue data deconvolution has been broadly utilized to characterize the variation of cell components and their relative abundance in cancer tumor microenvironment. Numerous deconvolution methods have been developed with different mathematical and biological considerations. Our previous studies have derived a rigorous mathematical condition for a less biased deconvolution. We have demonstrated a cell type can be reliably inferred from an omics data only of the cell type is with distinct signature genes, i.e., the cell type specific expression signature of the selected marker genes of all cell types are orthogonal or at least non-positively associated with each other, which is named as strong or weak “identifiable” conditions. Our analysis suggests the identifiability of a certain cell type is mostly determined by the significance of its gene markers, which is data set and tissue microenvironment dependent. However, such data set specificity has been commonly ignored in existing methods. In this study, we developed a comprehensive web server, namely ISMD (an Integrated web Server for Multi-omics data Deconvolution), to recommend a best deconvolution method and optimize the gene markers and cell types in analyzing a specific data set. The server solved to two fundamental challenges in the practical application of deconvolution methods, namely (1) method selection and (2) parameter optimization. Specifically, ISMD utilized a semi-supervised approach for method recommendation and parameter optimization, which first evaluate the species, sample size, and tissue type of a given data set and test the low rank structure of commonly used cell type specific markers for cell type and signature gene selection. ISMD can handle the blood, normal, inflammatory, and cancer tissue, and central nervous and hematopoietic systems of human and mouse. The semi-supervised knowledge transfer encoded in ISMD enables its application to multi-omics data including general transcriptomics, ATAC-seq and DNA-methylation data. The method has been validated by using a large collection of tissue samples. A user friend web-server has been released through https://github.com/xiaoyulu95/ISMD. We anticipate ISMD can help to increase the specificity and rigorousness in the application of tissue deconvolution methods, to bring more insights of the role of different cell types and their interactions and stimulate the discovery of new therapeutic targets in a tumor micro-environment. Citation Format: Xiaoyu Lu, Pengtao Dang, Szu-Wei Tu, Wennan Chang, Changlin Wan, Chi Zhang, Sha Cao. An integrated web server for multi-omics data deconvolution [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3217.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2020-3217
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2020
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
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