Advanced

BayesFlow: latent modeling of flow cytometry cell populations.

Johnsson, Kerstin LU ; Wallin, Jonas LU and Fontes, Magnus LU (2016) In BMC Bioinformatics 17(1).
Abstract
Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in... (More)
Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Flow cytometry, Bayesian hierarchical models, Model-based clustering
in
BMC Bioinformatics
volume
17
issue
1
publisher
BioMed Central
external identifiers
  • pmid:26755197
  • wos:000367874000001
  • scopus:84953851427
ISSN
1471-2105
DOI
10.1186/s12859-015-0862-z
language
English
LU publication?
yes
id
19fc7cac-9174-498f-a412-37744704b729 (old id 8592365)
date added to LUP
2016-02-23 07:18:05
date last changed
2017-10-01 04:00:47
@article{19fc7cac-9174-498f-a412-37744704b729,
  abstract     = {Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.},
  articleno    = {25},
  author       = {Johnsson, Kerstin and Wallin, Jonas and Fontes, Magnus},
  issn         = {1471-2105},
  keyword      = {Flow cytometry,Bayesian hierarchical models,Model-based clustering},
  language     = {eng},
  number       = {1},
  publisher    = {BioMed Central},
  series       = {BMC Bioinformatics},
  title        = {BayesFlow: latent modeling of flow cytometry cell populations.},
  url          = {http://dx.doi.org/10.1186/s12859-015-0862-z},
  volume       = {17},
  year         = {2016},
}