Abstract
Multiplexed immunohistochemical (IHC) methods have been developed to evaluate multiple protein biomarkers in a single formalin-fixed paraffin-embedded (FFPE) tissue section. Since distinct populations of resident and recruited immune cells in tissues (and tumors) not only regulate progression of malignant disease, these also represent targets for novel immune-based therapies; thus, improved tissue biomarker assessment evaluating immune responses in situ are needed. To objectively identify distinct cell subsets in tissues and tumors, we adopted sparse coding approaches enabling modeling of data vectors as sparse linear combinations of basis elements, to audit cellular presence and phenotypes using image cytometry datasets with unbiased assessments. By doing comparative analyses between manual gating (ground truth) and sparse coding, we report that results are comparable as obtained by manual gating strategies, and demonstrate robustness and objectivity of this novel bioinformatics approach.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| Subtitle of host publication | Smarter Technology for a Healthier World, EMBC 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4046-4049 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509028092 |
| DOIs | |
| State | Published - Sep 13 2017 |
| Event | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: Jul 11 2017 → Jul 15 2017 |
Other
| Other | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 7/11/17 → 7/15/17 |
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ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Multiplexed immunohistochemistry image analysis using sparse coding. / Chang, Young Hwan; Tsujikawa, Takahiro; Margolin, Adam; Coussens, Lisa; Gray, Joe.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4046-4049 8037744.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiplexed immunohistochemistry image analysis using sparse coding
AU - Chang, Young Hwan
AU - Tsujikawa, Takahiro
AU - Margolin, Adam
AU - Coussens, Lisa
AU - Gray, Joe
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Multiplexed immunohistochemical (IHC) methods have been developed to evaluate multiple protein biomarkers in a single formalin-fixed paraffin-embedded (FFPE) tissue section. Since distinct populations of resident and recruited immune cells in tissues (and tumors) not only regulate progression of malignant disease, these also represent targets for novel immune-based therapies; thus, improved tissue biomarker assessment evaluating immune responses in situ are needed. To objectively identify distinct cell subsets in tissues and tumors, we adopted sparse coding approaches enabling modeling of data vectors as sparse linear combinations of basis elements, to audit cellular presence and phenotypes using image cytometry datasets with unbiased assessments. By doing comparative analyses between manual gating (ground truth) and sparse coding, we report that results are comparable as obtained by manual gating strategies, and demonstrate robustness and objectivity of this novel bioinformatics approach.
AB - Multiplexed immunohistochemical (IHC) methods have been developed to evaluate multiple protein biomarkers in a single formalin-fixed paraffin-embedded (FFPE) tissue section. Since distinct populations of resident and recruited immune cells in tissues (and tumors) not only regulate progression of malignant disease, these also represent targets for novel immune-based therapies; thus, improved tissue biomarker assessment evaluating immune responses in situ are needed. To objectively identify distinct cell subsets in tissues and tumors, we adopted sparse coding approaches enabling modeling of data vectors as sparse linear combinations of basis elements, to audit cellular presence and phenotypes using image cytometry datasets with unbiased assessments. By doing comparative analyses between manual gating (ground truth) and sparse coding, we report that results are comparable as obtained by manual gating strategies, and demonstrate robustness and objectivity of this novel bioinformatics approach.
UR - http://www.scopus.com/inward/record.url?scp=85032176290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032176290&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037744
DO - 10.1109/EMBC.2017.8037744
M3 - Conference contribution
C2 - 29060785
AN - SCOPUS:85032176290
SP - 4046
EP - 4049
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
ER -