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#transcriptomics — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #transcriptomics, aggregated by home.social.

  1. Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
    academic.oup.com/bib/article/2

    Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

    However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

    On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

    #machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

  2. Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
    academic.oup.com/bib/article/2

    Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

    However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

    On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

    #machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

  3. Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
    academic.oup.com/bib/article/2

    Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

    However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

    On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

    #machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

  4. Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
    academic.oup.com/bib/article/2

    Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

    However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

    On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

    #machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

  5. Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
    academic.oup.com/bib/article/2

    Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.

    However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.

    On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.

    #machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics

  6. Curious about the dark side of plant biology?

    Check out our latest preprint on the secrets of black pigmentation in Rubus:

    doi.org/10.64898/2026.05.05.72

    #Anthocyanins #Transcriptomics #PlantScience #Fruits
    @PuckerLab

  7. Curious about the dark side of plant biology?

    Check out our latest preprint on the secrets of black pigmentation in Rubus:

    doi.org/10.64898/2026.05.05.72

    #Anthocyanins #Transcriptomics #PlantScience #Fruits
    @PuckerLab

  8. Curious about the dark side of plant biology?

    Check out our latest preprint on the secrets of black pigmentation in Rubus:

    doi.org/10.64898/2026.05.05.72

    #Anthocyanins #Transcriptomics #PlantScience #Fruits
    @PuckerLab

  9. Curious about the dark side of plant biology?

    Check out our latest preprint on the secrets of black pigmentation in Rubus:

    doi.org/10.64898/2026.05.05.72

    #Anthocyanins #Transcriptomics #PlantScience #Fruits
    @PuckerLab

  10. Curious about the dark side of plant biology?

    Check out our latest preprint on the secrets of black pigmentation in Rubus:

    doi.org/10.64898/2026.05.05.72

    #Anthocyanins #Transcriptomics #PlantScience #Fruits
    @PuckerLab

  11. 🧬 Could a single metric decode how genes are regulated across cells?

    🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0044

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

  12. 🧬 Could a single metric decode how genes are regulated across cells?

    🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0044

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

  13. 🧬 Could a single metric decode how genes are regulated across cells?

    🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0044

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

  14. 🧬 Could a single metric decode how genes are regulated across cells?

    🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0044

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

  15. 🧬 Could a single metric decode how genes are regulated across cells?

    🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0044

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics

  16. 🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

    🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0052

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

  17. 🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

    🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0052

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

  18. 🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

    🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0052

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

  19. 🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

    🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0052

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

  20. 🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?

    🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: doi.org/10.34133/csbj.0052

    📚 CSBJ - A Science Partner Journal: spj.science.org/journal/csbj

    #RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA

  21. Youthful antics predict lifespan — at least for these fish

    You snooze, you lose: young killifish (Nothobranchius furzeri) that take daytime naps have relatively short lives. Credit: Andrew…
    #NewsBeep #News #Science #Ageing #Animalbehaviour #AU #Australia #HumanitiesandSocialSciences #multidisciplinary #Transcriptomics
    newsbeep.com/au/536817/

  22. Youthful antics predict lifespan — at least for these fish

    You snooze, you lose: young killifish (Nothobranchius furzeri) that take daytime naps have relatively short lives. Credit: Andrew…
    #NewsBeep #News #Science #Ageing #Animalbehaviour #AU #Australia #HumanitiesandSocialSciences #multidisciplinary #Transcriptomics
    newsbeep.com/au/536817/

  23. At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

  24. At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

  25. At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

  26. At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

  27. At #PAG33? Stop by the Galaxy booth to get snacks and talk with us about how we can help you get FREE, reproduceable, publishable high performance computing workflows. #Assembly, #Transcriptomics, #Epigenetics, and much more!

  28. europesays.com/ie/199053/ Transcriptomic analysis of a compatible tobacco-herbivore interaction and the role of jasmonoyl-L-isoleucine hydrolase 1 in response to growth/defense trade-off | BMC Plant Biology #Agriculture #CRISPRCas9 #Éire #Growth/defenseTradeOff #Herbivory #IE #Ireland #JasmonoylLIsoleucineHydrolase(JIH1) #NNicotianaTabacum(tobacco) #PlantSciences #Science #Transcriptomics #TreeBiology

  29. Is spatial transcriptomics data preprocessing giving you a headache? You're not alone! 🤯
    Join our webinar designed for new researchers to simplify the complex world of image-based spatial omics. We'll walk through the practical workflow step-by-step, from initial cell segmentation to cleaning up your data.
    Get the foundational skills you need. 📅 Sign up here: t1p.de/lqbg1

    #Transcriptomics #SpatialTranscriptomics #Omics #DataScience #ResearchWebinar #GHGA #ELIXIR #deNBI