A gene profiling deconvolution approach to estimating immune cell composition from complex tissues

Shu Hwa Chen, Wen Yu Kuo, Sheng Yao Su, Wei Chun Chung, Jen Ming Ho, Henry Horng Shing Lu, Chung Yen Lin

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Background: 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. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.

Original languageEnglish
Article number154
JournalBMC Bioinformatics
Volume19
DOIs
Publication statusPublished - May 8 2018
Externally publishedYes

Fingerprint

Deconvolution
Profiling
Genes
Tissue
Gene
Cell
Chemical analysis
Flow cytometry
Gene expression
Flow Cytometry
Oncology
Galaxies
Cancer
Lymphocytes
Biopsy
Computer operating systems
Microarrays
Immunohistochemistry
Tumor-Infiltrating Lymphocytes
Gene Expression Profile

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

A gene profiling deconvolution approach to estimating immune cell composition from complex tissues. / Chen, Shu Hwa; Kuo, Wen Yu; Su, Sheng Yao; Chung, Wei Chun; Ho, Jen Ming; Lu, Henry Horng Shing; Lin, Chung Yen.

In: BMC Bioinformatics, Vol. 19, 154, 08.05.2018.

Research output: Contribution to journalArticle

Chen, Shu Hwa ; Kuo, Wen Yu ; Su, Sheng Yao ; Chung, Wei Chun ; Ho, Jen Ming ; Lu, Henry Horng Shing ; Lin, Chung Yen. / A gene profiling deconvolution approach to estimating immune cell composition from complex tissues. In: BMC Bioinformatics. 2018 ; Vol. 19.
@article{4f1fb8db5fc745efac93ca4e95a0ca6e,
title = "A gene profiling deconvolution approach to estimating immune cell composition from complex tissues",
abstract = "Background: 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. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.",
author = "Chen, {Shu Hwa} and Kuo, {Wen Yu} and Su, {Sheng Yao} and Chung, {Wei Chun} and Ho, {Jen Ming} and Lu, {Henry Horng Shing} and Lin, {Chung Yen}",
year = "2018",
month = "5",
day = "8",
doi = "10.1186/s12859-018-2069-6",
language = "English",
volume = "19",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",

}

TY - JOUR

T1 - A gene profiling deconvolution approach to estimating immune cell composition from complex tissues

AU - Chen, Shu Hwa

AU - Kuo, Wen Yu

AU - Su, Sheng Yao

AU - Chung, Wei Chun

AU - Ho, Jen Ming

AU - Lu, Henry Horng Shing

AU - Lin, Chung Yen

PY - 2018/5/8

Y1 - 2018/5/8

N2 - Background: 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. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.

AB - Background: 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. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.

UR - http://www.scopus.com/inward/record.url?scp=85046629208&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046629208&partnerID=8YFLogxK

U2 - 10.1186/s12859-018-2069-6

DO - 10.1186/s12859-018-2069-6

M3 - Article

C2 - 29745829

AN - SCOPUS:85046629208

VL - 19

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

M1 - 154

ER -