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dc.contributor.authorChen, Shu-Hwaen_US
dc.contributor.authorKuo, Wen-Yuen_US
dc.contributor.authorSu, Sheng-Yaoen_US
dc.contributor.authorChung, Wei-Chunen_US
dc.contributor.authorHo, Jen-Mingen_US
dc.contributor.authorLu, Henry Horng-Shingen_US
dc.contributor.authorLin, Chung-Yenen_US
dc.date.accessioned2018-08-21T05:53:40Z-
dc.date.available2018-08-21T05:53:40Z-
dc.date.issued2018-05-08en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://dx.doi.org/10.1186/s12859-018-2069-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/144993-
dc.description.abstractBackground: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. Results: Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v-Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. Conclusions: Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems.en_US
dc.language.isoen_USen_US
dc.titleA gene profiling deconvolution approach to estimating immune cell composition from complex tissuesen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s12859-018-2069-6en_US
dc.identifier.journalBMC BIOINFORMATICSen_US
dc.citation.volume19en_US
dc.citation.issue4en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000432288800002en_US
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