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BayesFlow: latent modeling of flow cytometry cell populations.

In English

Författare

Summary, in English

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.

Publiceringsår

2016

Språk

Engelska

Publikation/Tidskrift/Serie

BMC Bioinformatics

Volym

17

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

BioMed Central (BMC)

Ämne

  • Mathematical Analysis

Nyckelord

  • Flow cytometry
  • Bayesian hierarchical models
  • Model-based clustering

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1471-2105