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Title: Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-basedmore » scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at www.bioconductor.org. It has been downloaded nearly 6,000 times since 2014.« less
Authors:
 [1] ;  [2] ;  [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
Frontiers in Oncology
Additional Journal Information:
Journal Volume: 6; Journal ID: ISSN 2234-943X
Publisher:
Frontiers Research Foundation
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; flow cytometry; clusters; meta-clusters; template; matching; classification
OSTI Identifier:
1379582

Azad, Ariful, Rajwa, Bartek, and Pothen, Alex. Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples. United States: N. p., Web. doi:10.3389/fonc.2016.00188.
Azad, Ariful, Rajwa, Bartek, & Pothen, Alex. Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples. United States. doi:10.3389/fonc.2016.00188.
Azad, Ariful, Rajwa, Bartek, and Pothen, Alex. 2016. "Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples". United States. doi:10.3389/fonc.2016.00188. https://www.osti.gov/servlets/purl/1379582.
@article{osti_1379582,
title = {Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples},
author = {Azad, Ariful and Rajwa, Bartek and Pothen, Alex},
abstractNote = {We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at www.bioconductor.org. It has been downloaded nearly 6,000 times since 2014.},
doi = {10.3389/fonc.2016.00188},
journal = {Frontiers in Oncology},
number = ,
volume = 6,
place = {United States},
year = {2016},
month = {8}
}

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