Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis

Philippe Flores; Joseph K. Chege; Konstantin Usevich; Martin Haardt; Arie Yeredor; David Brie

doi:10.1109/CAMSAP58249.2023.10403522

Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis

Philippe Flores
, Joseph K. Chege
, Konstantin Usevich
, Martin Haardt
, Arie Yeredor
, David Brie

School of Electrical Engineering

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we study three recently proposed probability mass function (PMF) estimation methods for flow cytometry data analysis. By modeling the PMFs as a mixture of simpler distributions, we can reformulate the PMF estimation problem as three different tensor-based approaches: a least squares coupled tensor factorization approach, a least squares partially coupled tensor factorization approach, and a Kullback-Leibler divergence (KLD)-based expectation-maximization (EM) approach. In the coupled methods, the full PMF is estimated from lower-order empirical marginal distributions, while the EM approach estimates the full PMF directly from the observed data. The three approaches are evaluated in the context of simulated and real data experiments.

Original language	English
Title of host publication	2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	451-455
Number of pages	5
ISBN (Electronic)	9798350344523
DOIs	https://doi.org/10.1109/CAMSAP58249.2023.10403522
State	Published - 2023
Event	9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 - Herradura, Costa Rica Duration: 10 Dec 2023 → 13 Dec 2023

Publication series

Name	2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

Conference

Conference	9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Country/Territory	Costa Rica
City	Herradura
Period	10/12/23 → 13/12/23

Keywords

Flow Cytometry
Low-Rank Tensor Decomposition
Naïve Bayes Model
Probability Mass Functions (PMF)

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing
Control and Optimization
Instrumentation

Access to Document

10.1109/CAMSAP58249.2023.10403522

Cite this

Flores, P., Chege, J. K., Usevich, K., Haardt, M., Yeredor, A., & Brie, D. (2023). Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis. In 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 (pp. 451-455). (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP58249.2023.10403522

Flores, Philippe ; Chege, Joseph K. ; Usevich, Konstantin et al. / Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis. 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 451-455 (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023).

@inproceedings{4cdb2331874a4ce19b8c345f6cf5b258,

title = "Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis",

abstract = "In this paper, we study three recently proposed probability mass function (PMF) estimation methods for flow cytometry data analysis. By modeling the PMFs as a mixture of simpler distributions, we can reformulate the PMF estimation problem as three different tensor-based approaches: a least squares coupled tensor factorization approach, a least squares partially coupled tensor factorization approach, and a Kullback-Leibler divergence (KLD)-based expectation-maximization (EM) approach. In the coupled methods, the full PMF is estimated from lower-order empirical marginal distributions, while the EM approach estimates the full PMF directly from the observed data. The three approaches are evaluated in the context of simulated and real data experiments.",

keywords = "Flow Cytometry, Low-Rank Tensor Decomposition, Na{\"i}ve Bayes Model, Probability Mass Functions (PMF)",

author = "Philippe Flores and Chege, \{Joseph K.\} and Konstantin Usevich and Martin Haardt and Arie Yeredor and David Brie",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 ; Conference date: 10-12-2023 Through 13-12-2023",

year = "2023",

doi = "10.1109/CAMSAP58249.2023.10403522",

language = "English",

series = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "451--455",

booktitle = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",

address = "United States",

}

Flores, P, Chege, JK, Usevich, K, Haardt, M, Yeredor, A & Brie, D 2023, Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis. in 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023, Institute of Electrical and Electronics Engineers Inc., pp. 451-455, 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023, Herradura, Costa Rica, 10/12/23. https://doi.org/10.1109/CAMSAP58249.2023.10403522

Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis. / Flores, Philippe; Chege, Joseph K.; Usevich, Konstantin et al.
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 451-455 (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis

AU - Flores, Philippe

AU - Chege, Joseph K.

AU - Usevich, Konstantin

AU - Haardt, Martin

AU - Yeredor, Arie

AU - Brie, David

PY - 2023

Y1 - 2023

N2 - In this paper, we study three recently proposed probability mass function (PMF) estimation methods for flow cytometry data analysis. By modeling the PMFs as a mixture of simpler distributions, we can reformulate the PMF estimation problem as three different tensor-based approaches: a least squares coupled tensor factorization approach, a least squares partially coupled tensor factorization approach, and a Kullback-Leibler divergence (KLD)-based expectation-maximization (EM) approach. In the coupled methods, the full PMF is estimated from lower-order empirical marginal distributions, while the EM approach estimates the full PMF directly from the observed data. The three approaches are evaluated in the context of simulated and real data experiments.

AB - In this paper, we study three recently proposed probability mass function (PMF) estimation methods for flow cytometry data analysis. By modeling the PMFs as a mixture of simpler distributions, we can reformulate the PMF estimation problem as three different tensor-based approaches: a least squares coupled tensor factorization approach, a least squares partially coupled tensor factorization approach, and a Kullback-Leibler divergence (KLD)-based expectation-maximization (EM) approach. In the coupled methods, the full PMF is estimated from lower-order empirical marginal distributions, while the EM approach estimates the full PMF directly from the observed data. The three approaches are evaluated in the context of simulated and real data experiments.

KW - Flow Cytometry

KW - Low-Rank Tensor Decomposition

KW - Naïve Bayes Model

KW - Probability Mass Functions (PMF)

UR - https://www.scopus.com/pages/publications/85184996507

U2 - 10.1109/CAMSAP58249.2023.10403522

DO - 10.1109/CAMSAP58249.2023.10403522

M3 - Conference contribution

T3 - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

SP - 451

EP - 455

BT - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

Y2 - 10 December 2023 through 13 December 2023

ER -

Flores P, Chege JK, Usevich K, Haardt M, Yeredor A, Brie D. Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis. In 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 451-455. (2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023). doi: 10.1109/CAMSAP58249.2023.10403522

Probability Mass Function Estimation Approaches with Application to Flow Cytometry Data Analysis

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Keywords

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