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Cell Syst. 2017 Jun 28;4(6):651-655.e5. doi: 10.1016/j.cels.2017.05.012. Epub 2017 Jun 21.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

Author information

1
Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) S.r.l., IRCCS, Via Piero Maroncelli 40, 47014 Meldola (FC), Italy.
2
Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, 6726 Szeged, Hungary.
3
Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland.
4
Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, 6726 Szeged, Hungary; University of Szeged, Faculty of Dentistry, Tisza Lajos körút 64, 6720 Szeged, Hungary.
5
Insitute of Biochemistry, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland.
6
KTH Royal Institute of Technology, School of Computer Science and Communication, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden.
7
Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, 6726 Szeged, Hungary; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland. Electronic address: horvath.peter@brc.mta.hu.

Abstract

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.

KEYWORDS:

cell classification; fluorescence microscopy; high-content screening; image processing; machine learning; multi-parametric analysis; oncology; open-source software; phenotypic discovery; single-cell analysis

PMID:
28647475
DOI:
10.1016/j.cels.2017.05.012
[Indexed for MEDLINE]
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