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Modified variational bayes EM estimation of hidden markov tree model of cell lineages

Olariu, Victor LU ; Coca, Daniel ; Billings, Stephen A. ; Tonge, Peter ; Gokhale, Paul ; Andrews, Peter W. and Kadirkamanathan, Visakan (2009) In Bioinformatics 25(21). p.2824-2830
Abstract

Motivation: Human pluripotent stem cell lines persist in culture as a heterogeneous population of SSEA3 positive and SSEA3 negative cells. Tracking individual stem cells in real time can elucidate the kinetics of cells switching between the SSEA3 positive and negative substates. However, identifying a cell's substate at all time points within a cell lineage tree is technically difficult. Results: A variational Bayesian Expectation Maximization (EM) with smoothed probabilities (VBEMS) algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction... (More)

Motivation: Human pluripotent stem cell lines persist in culture as a heterogeneous population of SSEA3 positive and SSEA3 negative cells. Tracking individual stem cells in real time can elucidate the kinetics of cells switching between the SSEA3 positive and negative substates. However, identifying a cell's substate at all time points within a cell lineage tree is technically difficult. Results: A variational Bayesian Expectation Maximization (EM) with smoothed probabilities (VBEMS) algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction of the types of cells in synthetic and real stem cell lineage trees are presented.

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author
; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioinformatics
volume
25
issue
21
pages
7 pages
publisher
Oxford University Press
external identifiers
  • scopus:70350688144
  • pmid:19628503
ISSN
1367-4803
DOI
10.1093/bioinformatics/btp456
language
English
LU publication?
no
id
3dc35208-3da1-4075-a616-d19751cd9026
date added to LUP
2019-05-24 14:01:42
date last changed
2025-04-04 14:40:00
@article{3dc35208-3da1-4075-a616-d19751cd9026,
  abstract     = {{<p>Motivation: Human pluripotent stem cell lines persist in culture as a heterogeneous population of SSEA3 positive and SSEA3 negative cells. Tracking individual stem cells in real time can elucidate the kinetics of cells switching between the SSEA3 positive and negative substates. However, identifying a cell's substate at all time points within a cell lineage tree is technically difficult. Results: A variational Bayesian Expectation Maximization (EM) with smoothed probabilities (VBEMS) algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction of the types of cells in synthetic and real stem cell lineage trees are presented.</p>}},
  author       = {{Olariu, Victor and Coca, Daniel and Billings, Stephen A. and Tonge, Peter and Gokhale, Paul and Andrews, Peter W. and Kadirkamanathan, Visakan}},
  issn         = {{1367-4803}},
  language     = {{eng}},
  month        = {{11}},
  number       = {{21}},
  pages        = {{2824--2830}},
  publisher    = {{Oxford University Press}},
  series       = {{Bioinformatics}},
  title        = {{Modified variational bayes EM estimation of hidden markov tree model of cell lineages}},
  url          = {{http://dx.doi.org/10.1093/bioinformatics/btp456}},
  doi          = {{10.1093/bioinformatics/btp456}},
  volume       = {{25}},
  year         = {{2009}},
}