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Bajikar, S.S.* ; Fuchs, C. ; Roller, A. ; Theis, F.J. ; Janes, K.A.*

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

Proc. Natl. Acad. Sci. U.S.A. 111, E626-E635 (2014)
Verlagsversion DOI
Open Access Gold
Cell-to-cell variations in gene regulation occur in a number of biological contexts, such as development and cancer. Discovering regulatory heterogeneities in an unbiased manner is difficult owing to the population averaging that is required for most global molecular methods. Here, we show that we can infer single-cell regulatory states by mathematically deconvolving global measurements taken as averages from small groups of cells. This averaging-and-deconvolution approach allows us to quantify single-cell regulatory heterogeneities while avoiding the measurement noise of global single-cell techniques. Our method is particularly relevant to solid tissues, where single-cell dissociation and molecular profiling is especially problematic.
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Icb_biostatistics Icb_Latent Causes Icb_ML
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Breast Cancer ; Morphogenesis ; Noise ; Systems Biology; Human Breast-cancer; Messenger-rna-seq; Single-cell; Gene-expression; Saccharomyces-cerevisiae; Noise; Reveals; Tissues; Morphogenesis; Proliferation
Sprache englisch
Veröffentlichungsjahr 2014
HGF-Berichtsjahr 2014
ISSN (print) / ISBN 0027-8424
e-ISSN 1091-6490
Zeitschrift Proceedings of the National Academy of Sciences of the United States of America
Quellenangaben Band: 111, Heft: 5, Seiten: E626-E635 Artikelnummer: , Supplement: ,
Verlag National Academy of Sciences
Verlagsort Washington
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
DOI 10.1073/pnas.1311647111
WOS ID WOS:000330587600016
Scopus ID 84893499959
Erfassungsdatum 2014-01-23
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