Evidence map›Paper›PMID 27510222›Full record

ArticleBMC medical genomics2016

Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection.

Maziyar Baran Pouyan, Vasu Jindal, Javad Birjandtalab, Mehrdad Nourani

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Maziyar Baran PouyanQuality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA.
Vasu JindalQuality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA.
Javad BirjandtalabQuality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA.
Mehrdad NouraniQuality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA. nourani@utdallas.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMeasurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is identification of the number of cellular populations which heavily affects the accuracy of results. Furthermore, anomaly detection is crucial in flow cytometry experiments. In this work, we propose a two-stage clustering technique for cell type identification in single subject flow cytometry data and extend it for anomaly detection among multiple subjects.

resultsOur experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91 %) in identifying main cellular populations. Furthermore, our anomaly detection technique evaluated on Acute Myeloid Leukemia dataset results in only <2 % false positives.

Indexed as

BiomarkersCellsCluster AnalysisFlow CytometryFuzzy LogicHumansLeukemia, Myeloid, AcuteMarkov ChainsBiomarkersAnomaly detectionBiaxial gatingCell-type populationFlow cytometrySingle-cell technologyTwo-stage clustering

Identifiers

PMID27510222
PMCPMC4980779

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.