Trial reportPloS one2013
Efficient blind spectral unmixing of fluorescently labeled samples using multi-layer non-negative matrix factorization.
Trial report in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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.
Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Review
- SUFI: an automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and post-mortem human brain tissues.BMC neuroscience · 2023Article
- Live-cell fluorescence spectral imaging as a data science challenge.Biophysical reviews · 2022Review
- Robust functional imaging of taste sensation with a Bessel beam.Biomedical optics express · 2021Article
- Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach.PloS one · 2021Article
- Source separation approach for the analysis of spatially resolved multiply excited autofluorescence spectra during optical clearing ofBiomedical optics express · 2019Article
- Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning.PloS one · 2019Article
- Chrysalis: A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies.Journal of immunology (Baltimore, Md. : 1950) · 2019Article
- Extended Blind End-member and Abundance Extraction for Biomedical Imaging Applications.IEEE access : practical innovations, open solutions · 2019Article
- Statistically strong label-free quantitative identification of native fluorophores in a biological sample.Scientific reports · 2017Article
- Machine learning applications in cell image analysis.Immunology and cell biology · 2017Review
- Two-hierarchical nonnegative matrix factorization distinguishing the fluorescent targets from autofluorescence for fluorescence imaging.Biomedical engineering online · 2015Article
- Simultaneous decomposition of multiple hyperspectral data sets: signal recovery of unknown fluorophores in the retinal pigment epithelium.Biomedical optics express · 2014Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ample variety of labeling dyes and staining methods available in fluorescence microscopy has enabled biologists to advance in the understanding of living organisms at cellular and molecular level. When two or more fluorescent dyes are used in the same preparation, or one dye is used in the presence of autofluorescence, the separation of the fluorescent emissions can become problematic. Various approaches have been recently proposed to solve this problem. Among them, blind non-negative matrix factorization is gaining interest since it requires little assumptions about the spectra and concentration of the fluorochromes. In this paper, we propose a novel algorithm for blind spectral separation that addresses some of the shortcomings of existing solutions: namely, their dependency on the initialization and their slow convergence. We apply this new algorithm to two relevant problems in fluorescence microscopy: autofluorescence elimination and spectral unmixing of multi-labeled samples. Our results show that our new algorithm performs well when compared with the state-of-the-art approaches for a much faster implementation.
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Registered trials
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.