Dataset Information

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

ABSTRACT: In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

SUBMITTER: Pyne S

PROVIDER: S-EPMC4077578 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

Pyne Saumyadipta S Lee Sharon X SX Wang Kui K Irish Jonathan J Tamayo Pablo P Nazaire Marc-Danie MD Duong Tarn T Ng Shu-Kay SK Hafler David D Levy Ronald R Nolan Garry P GP Mesirov Jill J McLachlan Geoffrey J GJ

PloS one 20140701 7

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, ...[more]

PMID: 24983991

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Project description:Detecting the presence of prostate cancer (PCa) and distinguishing low- or intermediate-risk disease from high-risk disease early, and without the need for potentially unnecessary invasive biopsies remains a significant clinical challenge. The aim of this study is to determine whether the T and B cell phenotypic features which we have previously identified as being able to distinguish between benign prostate disease and PCa in asymptomatic men having Prostate-Specific Antigen (PSA) levels < 20 ng/ml can also be used to detect the presence and clinical risk of PCa in a larger cohort of patients whose PSA levels ranged between 3 and 2617 ng/ml. The peripheral blood of 130 asymptomatic men having elevated Prostate-Specific Antigen (PSA) levels was immune profiled using multiparametric whole blood flow cytometry. Of these men, 42 were subsequently diagnosed as having benign prostate disease and 88 as having PCa on biopsy-based evidence. We built a bidirectional Long Short-Term Memory Deep Neural Network (biLSTM) model for detecting the presence of PCa in men which combined the previously-identified phenotypic features (CD8+CD45RA-CD27-CD28- (CD8+ Effector Memory cells), CD4+CD45RA-CD27-CD28- (CD4+ Effector Memory cells), CD4+CD45RA+CD27-CD28- (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA), CD3-CD19+ (B cells), CD3+CD56+CD8+CD4+ (NKT cells) with Age. The performance of the PCa presence 'detection' model was: Acc: 86.79 ( ± 0.10), Sensitivity: 82.78% (± 0.15); Specificity: 95.83% (± 0.11) on the test set (test set that was not used during training and validation); AUC: 89.31% (± 0.07), ORP-FPR: 7.50% (± 0.20), ORP-TPR: 84.44% (± 0.14). A second biLSTM 'risk' model combined the immunophenotypic features with PSA to predict whether a patient with PCa has high-risk disease (defined by the D'Amico Risk Classification) achieved the following: Acc: 94.90% (± 6.29), Sensitivity: 92% (± 21.39); Specificity: 96.11 (± 0.00); AUC: 94.06% (± 10.69), ORP-FPR: 3.89% (± 0.00), ORP-TPR: 92% (± 21.39). The ORP-FPR for predicting the presence of PCa when combining FC+PSA was lower than that of PSA alone. This study demonstrates that AI approaches based on peripheral blood phenotyping profiles can distinguish between benign prostate disease and PCa and predict clinical risk in asymptomatic men having elevated PSA levels.

Dataset Information

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

Publications

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

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