Daniil S Ulyanov, Alana A Ulyanova, Dmitry Y Litvinov, Alina A Kocheshkova, Alexandra Yu Kroupina, Nadejda M Syedina, Viktoria S Voronezhskaya, Andrey V Vasilyev, Gennady I Karlov, Mikhail G Divashuk
{"title":"StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis.","authors":"Daniil S Ulyanov, Alana A Ulyanova, Dmitry Y Litvinov, Alina A Kocheshkova, Alexandra Yu Kroupina, Nadejda M Syedina, Viktoria S Voronezhskaya, Andrey V Vasilyev, Gennady I Karlov, Mikhail G Divashuk","doi":"10.3389/fpls.2025.1475057","DOIUrl":null,"url":null,"abstract":"<p><p>Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (<i>Triticum aestivum</i>), durum wheat (<i>Triticum durum</i>), and triticale (× <i>Triticosecale</i>); sugar beet (<i>Beta vulgaris</i>), cocklebur (<i>Xanthium strumarium</i>) and lettuce (<i>Lactuca sativa</i>), corn (<i>Zea mays</i>) and sunflower (<i>Helianthus annuus</i>), and soybean (<i>Glycine max</i>). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1475057"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965913/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Plant Science","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3389/fpls.2025.1475057","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
期刊介绍：
In an ever changing world, plant science is of the utmost importance for securing the future well-being of humankind. Plants provide oxygen, food, feed, fibers, and building materials. In addition, they are a diverse source of industrial and pharmaceutical chemicals. Plants are centrally important to the health of ecosystems, and their understanding is critical for learning how to manage and maintain a sustainable biosphere. Plant science is extremely interdisciplinary, reaching from agricultural science to paleobotany, and molecular physiology to ecology. It uses the latest developments in computer science, optics, molecular biology and genomics to address challenges in model systems, agricultural crops, and ecosystems. Plant science research inquires into the form, function, development, diversity, reproduction, evolution and uses of both higher and lower plants and their interactions with other organisms throughout the biosphere. Frontiers in Plant Science welcomes outstanding contributions in any field of plant science from basic to applied research, from organismal to molecular studies, from single plant analysis to studies of populations and whole ecosystems, and from molecular to biophysical to computational approaches.
Frontiers in Plant Science publishes articles on the most outstanding discoveries across a wide research spectrum of Plant Science. The mission of Frontiers in Plant Science is to bring all relevant Plant Science areas together on a single platform.
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