{"id":5946,"date":"2025-07-31T09:00:07","date_gmt":"2025-07-31T09:00:07","guid":{"rendered":"https:\/\/www.ub.edu\/nutrimetabolomics\/?page_id=5946"},"modified":"2025-07-31T09:14:18","modified_gmt":"2025-07-31T09:14:18","slug":"bioinformatics-tools-for-biological-interpretation-and-data-visualization","status":"publish","type":"page","link":"https:\/\/www.ub.edu\/nutrimetabolomics\/bioinformatics-tools-for-biological-interpretation-and-data-visualization\/","title":{"rendered":"Bioinformatics tools for biological interpretation and data visualization"},"content":{"rendered":"\n<div\n\tstyle=\"background: url(https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2024\/12\/Disseny-sense-titol-3.jpg) no-repeat center center \/ cover\" class=\"omt-multi-hero has-background-color has-none-background-color wp-block-omt-multi-hero\"\t>\n\t<div class=\"container\">\n\t\t<div class=\"omt-multi-hero__content text-left\">\n\n\t\t\t<h1 class=\"omt-multi-hero__title\">Bioinformatics tools for biological interpretation and data visualization<\/h1>\n\t\t\t\t\t\t\t<div class=\"omt-multi-hero__description\">\n\t\t\t\t\t<p data-start=\"14\" data-end=\"554\">Our group has developed tools such as the <strong>Food-Biomarker Ontology (FOBI)<\/strong> (Castellano-Escuder P, et al., 2020), the first ontology designed to integrate metabolomics and nutrition data, and <strong>POMAShiny<\/strong> (Castellano-Escuder P, et al., 2021), which offers univariate and multivariate statistical methods, dimensionality reduction techniques, feature selection approaches, regularized regression analysis, machine learning\u2013based classification algorithms, predictive modeling strategies, and various high-quality interactive visualization options.<\/p>\n<p data-start=\"14\" data-end=\"554\">\n<p data-start=\"556\" data-end=\"669\" data-is-last-node=\"\" data-is-only-node=\"\">Following FAIR principles, both the source codes and data files are available through public GitHub repositories.<\/p>\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n<div\nclass=\"omt-container has-none-background-color omt-container__wrapper--none wp-block-omt-container\" id=\"block_6ea5d41a00f7bc29d5e60cc698f988d2\">\n\t<div class=\"omt-container__wrapper omt-container__wrapper--normal omt-container__wrapper--offset-\">\n\t\t<div class=\"acf-innerblocks-container\">\n\n<div\n\tclass=\"omt-left-right-image-content wp-block-omt-left-right-image-content\"\t>\n\t\t\t<div class=\"omt-left-right-image-content__wrapper omt-left-right-image-content__right-img \">\n\t\t\t<div class=\"omt-left-right-image-content__details \">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"omt-left-right-image-content__txt\"><p>The Food-Biomarker Ontology (FOBI) is the first ontology developed to integrate metabolomics and nutrition data (Castellano-Escuder P, et al., 2020). This ontology aims to link different types of foods with their associated metabolites or dietary intake biomarkers.<\/p>\n<p>FOBI comprises 1,197 terms, 4 different properties, 13 top-level food classes, 11 top-level biomarker classes, and over 4,500 relationships. Additionally, FOBI is part of the OBO Foundry project, and FOBI identifiers have been indexed in the HMDB and FooDB databases to facilitate interoperability and data exchange.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/nutrimetabolomics\/fobitools\" target=\"_blank\" class=\"omt-button omt-button--primary\">Go to Fobitools<\/a>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"omt-left-right-image-content__image-block right-image-block\">\n\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"768\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4-768x768.png\" class=\"omt-left-right-image-content__image full\" alt=\"\" srcset=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4-768x768.png 768w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4-300x300.png 300w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4-1024x1024.png 1024w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4-150x150.png 150w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4-390x390.png 390w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-4.png 1200w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\t<\/div>\n<\/div>\n\n\n<div\nclass=\"omt-container has-none-background-color omt-container__wrapper--none wp-block-omt-container\" id=\"block_6ea5d41a00f7bc29d5e60cc698f988d2\">\n\t<div class=\"omt-container__wrapper omt-container__wrapper--normal omt-container__wrapper--offset-\">\n\t\t<div class=\"acf-innerblocks-container\">\n\n<div\n\tclass=\"omt-accordion-image wp-block-omt-accordion-image\"\t>\n\t<div class=\"container\">\n\t\t\t\t\n\t\t\t<div class=\"omt-accordion-image__wrapper left-image-change\">\n\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__img-block\">\n\t\t\t\t\t\t<img decoding=\"async\" src=\"\" class=\"omt-accordion-image__img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__main\">\n\t\t\t\t\t<h1 class=\"omt-accordion-image__title h1\">Food-Biomarker Ontology (FOBI)<\/h1>\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-6.jpg\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Graphical visualization of FOBI\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-txt\">\n\t\t\t\t\t\t\t\t\t\t<p>FOBI architecture using the apple as an example.<\/p>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-6.jpg\" class=\"omt-accordion-image__inner-img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Analysis of FOBI information from OBO to a human-readable table format\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Compound ID conversion (between metabolite names, FOBI, ChemSpider, KEGG, PubChemCID, InChIKey, InChICode, and HMDB IDs)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-13.jpg\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Analysis of biological significance using ORA and MSEA methods\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-txt\">\n\t\t\t\t\t\t\t\t\t\t<ul>\n<li data-start=\"83\" data-end=\"183\">\n<p data-start=\"85\" data-end=\"183\">Chemical class enrichment analysis: ORA and MSEA using FOBI chemical classes as metabolite sets.<\/p>\n<\/li>\n<li data-start=\"184\" data-end=\"267\" data-is-last-node=\"\">\n<p data-start=\"186\" data-end=\"267\" data-is-last-node=\"\">Food enrichment analysis: ORA and MSEA using FOBI food groups as metabolite sets.<\/p>\n<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-13.jpg\" class=\"omt-accordion-image__inner-img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-9.png\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Text mining algorithm for the annotation of dietary data in free text\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n<\/div>\n\n<\/div>\n\t<\/div>\n<\/div>\n\n\n<div\nclass=\"omt-container wp-block-omt-container\" id=\"block_863b495415abeae8cb5efbdf8d519ec0\">\n\t<div class=\"omt-container__wrapper omt-container__wrapper-- omt-container__wrapper--offset-\">\n\t\t<div class=\"acf-innerblocks-container\">\n\n<div\n\tclass=\"omt-left-right-image-content wp-block-omt-left-right-image-content\"\t>\n\t\t\t<div class=\"omt-left-right-image-content__wrapper omt-left-right-image-content__left-img\">\n\t\t\t<div class=\"omt-left-right-image-content__image-block left-image-block\">\n\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"768\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11-768x768.jpg\" class=\"omt-left-right-image-content__image full\" alt=\"\" srcset=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11-768x768.jpg 768w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11-300x300.jpg 300w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11-1024x1024.jpg 1024w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11-150x150.jpg 150w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11-390x390.jpg 390w, https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-11.jpg 1200w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t<\/div>\n\t\t\t<div class=\"omt-left-right-image-content__details\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<h2 class=\"omt-left-right-image-content__title has-h-2-font-size \">POMAShiny<\/h2>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-left-right-image-content__txt\"><article class=\"text-token-text-primary w-full focus:outline-none scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" data-turn-id=\"request-68889c62-3f2c-8332-a733-85cd2ef4e7b3-18\" data-testid=\"conversation-turn-168\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @[37rem]:[--thread-content-margin:--spacing(6)] @[72rem]:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:32rem] @[34rem]:[--thread-content-max-width:40rem] @[64rem]:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"38274e4e-2876-4d07-8854-90a6441ea5c3\" data-message-model-slug=\"gpt-4o\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose dark:prose-invert w-full break-words light\">\n<p data-start=\"14\" data-end=\"411\">POMAShiny is a web-based tool that offers a structured, flexible, and user-friendly workflow for processing, exploring, and statistically analyzing metabolomics data. It is built on the POMA package from R\/Bioconductor, which enhances the reproducibility and flexibility of the analysis outside the web environment. The POMAShiny workflow is organized into four sequential and well-defined panels:<\/p>\n<ol data-start=\"413\" data-end=\"505\">\n<li data-start=\"413\" data-end=\"428\">\n<p data-start=\"415\" data-end=\"428\">Data upload<\/p>\n<\/li>\n<li data-start=\"429\" data-end=\"446\">\n<p data-start=\"431\" data-end=\"446\">Preprocessing<\/p>\n<\/li>\n<li data-start=\"447\" data-end=\"482\">\n<p data-start=\"449\" data-end=\"482\">Exploratory Data Analysis (EDA)<\/p>\n<\/li>\n<li data-start=\"483\" data-end=\"505\" data-is-last-node=\"\">\n<p data-start=\"485\" data-end=\"505\" data-is-last-node=\"\">Statistical analysis<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"mt-3 w-full empty:hidden\">\n<div class=\"text-center\"><\/div>\n<\/div>\n<div class=\"aria-live=polite absolute\">\n<div class=\"flex items-center justify-center\"><span class=\"flex items-center gap-1.5 select-none\"><span class=\"sr-only whitespace-nowrap! md:not-sr-only\">Ask ChatGPT<\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<div class=\"pointer-events-none h-px w-px\" aria-hidden=\"true\" data-edge=\"true\"><\/div>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/nutrimetabolomics\/POMA\" target=\"_blank\" class=\"omt-button omt-button--transparent\">Go to POMAShiny<\/a>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\t<\/div>\n<\/div>\n\n\n<div\nclass=\"omt-container has-none-background-color omt-container__wrapper--none wp-block-omt-container\" id=\"block_6ea5d41a00f7bc29d5e60cc698f988d2\">\n\t<div class=\"omt-container__wrapper omt-container__wrapper--normal omt-container__wrapper--offset-\">\n\t\t<div class=\"acf-innerblocks-container\">\n\n<div\n\tclass=\"omt-accordion-image wp-block-omt-accordion-image\"\t>\n\t<div class=\"container\">\n\t\t\t\t\n\t\t\t<div class=\"omt-accordion-image__wrapper left-image-change\">\n\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__img-block\">\n\t\t\t\t\t\t<img decoding=\"async\" src=\"\" class=\"omt-accordion-image__img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__main\">\n\t\t\t\t\t<h1 class=\"omt-accordion-image__title h1\">POMAShiny <\/h1>\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Data upload\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-txt\">\n\t\t\t\t\t\t\t\t\t\t<p data-start=\"14\" data-end=\"560\">POMAShiny requires two input files in CSV format: a metadata file (target) and a features file. The metadata file should include sample names in the first column, group labels (e.g., control and case) in the second, and optionally, relevant covariates from the third column onward. The features file contains the quantified features from the experiment, with one feature per column. The row order must be the same in both files. Once uploaded, POMAShiny converts the files into an MSnSet object, following the MSnbase package from R\/Bioconductor.<\/p>\n<p data-start=\"562\" data-end=\"1018\" data-is-last-node=\"\" data-is-only-node=\"\">Users can select specific samples from the metadata file to create data subsets for analysis. Additionally, POMAShiny offers an optional function to combine features that belong to the same entity (such as peptides from a protein or ions from a compound). To use this feature, a &#8220;group&#8221; CSV file is required, indicating which features should be combined. It also allows users to download a table with the coefficient of variation for the combined features.<\/p>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\" class=\"omt-accordion-image__inner-img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\"> Preprocessing\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-txt\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"flex-shrink-0 flex flex-col relative items-end\">\n<div class=\"pt-0\">\n<div class=\"gizmo-bot-avatar flex h-8 w-8 items-center justify-center overflow-hidden rounded-full\">\n<div class=\"relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8\">\n<div class=\"relative flex basis-auto flex-col -mb-(--composer-overlap-px) [--composer-overlap-px:55px] grow overflow-hidden\">\n<div class=\"relative h-full\">\n<div class=\"flex h-full flex-col overflow-y-auto [scrollbar-gutter:stable_both-edges] @[84rem]\/thread:pt-(--header-height)\">\n<div class=\"@thread-xl\/thread:pt-header-height flex flex-col text-sm pb-25\">\n<article class=\"text-token-text-primary w-full focus:outline-none scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" data-turn-id=\"request-68889c62-3f2c-8332-a733-85cd2ef4e7b3-21\" data-testid=\"conversation-turn-174\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @[37rem]:[--thread-content-margin:--spacing(6)] @[72rem]:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:32rem] @[34rem]:[--thread-content-max-width:40rem] @[64rem]:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"c74e932c-6e1b-472d-b7af-f5460f29f784\" data-message-model-slug=\"gpt-4o\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose dark:prose-invert w-full break-words light\">\n<p data-start=\"0\" data-end=\"75\"><strong data-start=\"0\" data-end=\"75\">Missing Value Imputation, Normalization, and Outlier Detection<\/strong><\/p>\n<p data-start=\"77\" data-end=\"410\"><strong data-start=\"77\" data-end=\"105\">Missing Value Imputation<\/strong><br data-start=\"105\" data-end=\"108\" \/>In metabolomics and proteomics, some values are often not detectable or quantifiable due to biological or technical reasons (e.g., imprecise detection or values below the limit of quantification). To address this, POMAShiny offers a dedicated missing value imputation panel with three sequential steps:<\/p>\n<ol>\n<li data-start=\"414\" data-end=\"461\">Distinguish between zeros and missing values.<\/li>\n<li data-start=\"465\" data-end=\"539\">Remove features with a high percentage of missing values (default: 20%).<\/li>\n<li data-start=\"543\" data-end=\"669\">Impute the remaining missing values using methods such as zero, mean, median, minimum, or the <em data-start=\"637\" data-end=\"640\">k<\/em>-nearest neighbors algorithm<\/li>\n<\/ol>\n<p><strong data-start=\"671\" data-end=\"688\">Normalization<\/strong><br data-start=\"688\" data-end=\"691\" \/>Variability in data can affect statistical results, making normalization essential. POMAShiny provides six one-step normalization methods to transform and scale the data:<\/p>\n<ul data-start=\"862\" data-end=\"974\">\n<li data-start=\"862\" data-end=\"877\">\n<p data-start=\"864\" data-end=\"877\">Autoscaling<\/p>\n<\/li>\n<li data-start=\"878\" data-end=\"895\">\n<p data-start=\"880\" data-end=\"895\">Level scaling<\/p>\n<\/li>\n<li data-start=\"896\" data-end=\"911\">\n<p data-start=\"898\" data-end=\"911\">Log scaling<\/p>\n<\/li>\n<li data-start=\"912\" data-end=\"934\">\n<p data-start=\"914\" data-end=\"934\">Log transformation<\/p>\n<\/li>\n<li data-start=\"935\" data-end=\"951\">\n<p data-start=\"937\" data-end=\"951\">Vast scaling<\/p>\n<\/li>\n<li data-start=\"952\" data-end=\"974\">\n<p data-start=\"954\" data-end=\"974\">Log Pareto scaling<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"976\" data-end=\"1081\">These approaches help correct for differences in magnitude, technical variability, or heteroscedasticity.<\/p>\n<p data-start=\"1083\" data-end=\"1420\" data-is-last-node=\"\" data-is-only-node=\"\"><strong data-start=\"1083\" data-end=\"1104\">Outlier Detection<\/strong><br data-start=\"1104\" data-end=\"1107\" \/>Outliers can be biological (natural variations) or analytical (errors during processing). These can distort statistical results and predictive modeling techniques. POMAShiny facilitates outlier detection through interactive plots and tables, with customizable options to remove them prior to statistical analysis.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"mx-[var(--mini-thread-content-inset)]\">\n<div class=\"flex min-h-[46px] justify-start\">\n<div class=\"touch:-me-2 touch:-ms-3.5 -ms-2.5 -me-1 flex flex-wrap items-center gap-y-4 p-1 select-none touch:w-[calc(100%+--spacing(3.5))] -mt-1 w-[calc(100%+--spacing(2.5))] duration-[1.5s] focus-within:transition-none hover:transition-none pointer-events-none [mask-image:linear-gradient(to_right,black_33%,transparent_66%)] [mask-size:300%_100%] [mask-position:100%_0%] motion-safe:transition-[mask-position] group-hover\/turn-messages:pointer-events-auto group-hover\/turn-messages:[mask-position:0_0] group-focus-within\/turn-messages:pointer-events-auto group-focus-within\/turn-messages:[mask-position:0_0] has-data-[state=open]:pointer-events-auto has-data-[state=open]:[mask-position:0_0]\">\n<div class=\"flex items-center\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"mt-3 w-full empty:hidden\">\n<div class=\"text-center\"><\/div>\n<\/div>\n<div class=\"aria-live=polite absolute\">\n<div class=\"flex items-center justify-center\"><span class=\"flex items-center gap-1.5 select-none\"><span class=\"sr-only whitespace-nowrap! md:not-sr-only\">Ask Chat<\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\" class=\"omt-accordion-image__inner-img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Exploratory Data Analysis (EDA)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-txt\">\n\t\t\t\t\t\t\t\t\t\t<p data-start=\"14\" data-end=\"270\">EDA helps identify uncontrolled factors and potential outliers, and it is recommended to perform it before statistical analysis. Moreover, in the absence of significant biases, EDA can provide an initial overview of the most relevant features of the study.<\/p>\n<p data-start=\"272\" data-end=\"352\">POMAShiny offers interactive and customizable visualizations for EDA, including:<\/p>\n<ul data-start=\"353\" data-end=\"452\">\n<li data-start=\"353\" data-end=\"398\">\n<p data-start=\"355\" data-end=\"398\">Volcano plots (for two-group comparisons)<\/p>\n<\/li>\n<li data-start=\"399\" data-end=\"411\">\n<p data-start=\"401\" data-end=\"411\">Boxplots<\/p>\n<\/li>\n<li data-start=\"412\" data-end=\"429\">\n<p data-start=\"414\" data-end=\"429\">Density plots<\/p>\n<\/li>\n<li data-start=\"430\" data-end=\"452\">\n<p data-start=\"432\" data-end=\"452\">Clustered heatmaps<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"454\" data-end=\"539\" data-is-last-node=\"\" data-is-only-node=\"\">It also includes options for Principal Component Analysis (PCA) and cluster analysis.<\/p>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\" class=\"omt-accordion-image__inner-img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-item\" data-image=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-title\">Statistical Analysis\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"omt-accordion-image__accordion-txt\">\n\t\t\t\t\t\t\t\t\t\t<p data-start=\"14\" data-end=\"426\">This panel includes a variety of statistical methods, ranging from the most commonly used approaches in metabolomics and proteomics data analysis to other methodologies that are less frequent in these fields. All statistical methods offered by POMAShiny are implemented in a highly intuitive way for the user and generate both downloadable tables and interactive plots as results. The available analyses include:<\/p>\n<ul data-start=\"428\" data-end=\"682\" data-is-last-node=\"\" data-is-only-node=\"\">\n<li data-start=\"428\" data-end=\"455\">\n<p data-start=\"430\" data-end=\"455\">Univariate analysis<\/p>\n<\/li>\n<li data-start=\"456\" data-end=\"505\">\n<p data-start=\"458\" data-end=\"505\">Limma (Linear Models for Microarray Data)<\/p>\n<\/li>\n<li data-start=\"506\" data-end=\"535\">\n<p data-start=\"508\" data-end=\"535\">Multivariate analysis<\/p>\n<\/li>\n<li data-start=\"536\" data-end=\"560\">\n<p data-start=\"538\" data-end=\"560\">Cluster analysis<\/p>\n<\/li>\n<li data-start=\"561\" data-end=\"589\">\n<p data-start=\"563\" data-end=\"589\">Correlation analysis<\/p>\n<\/li>\n<li data-start=\"590\" data-end=\"620\">\n<p data-start=\"592\" data-end=\"620\">Regularized regression<\/p>\n<\/li>\n<li data-start=\"621\" data-end=\"643\">\n<p data-start=\"623\" data-end=\"643\">Random forests<\/p>\n<\/li>\n<li data-start=\"644\" data-end=\"662\">\n<p data-start=\"646\" data-end=\"662\">Odds ratio<\/p>\n<\/li>\n<li data-start=\"663\" data-end=\"682\" data-is-last-node=\"\">\n<p data-start=\"665\" data-end=\"682\" data-is-last-node=\"\">Rank products<\/p>\n<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-content\/uploads\/2025\/01\/Disseny-sense-titol-14.jpg\" class=\"omt-accordion-image__inner-img\" alt=\"Image 1\" loading=\"lazy\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n<\/div>\n\n<\/div>\n\t<\/div>\n<\/div>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-5946","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/pages\/5946","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/comments?post=5946"}],"version-history":[{"count":5,"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/pages\/5946\/revisions"}],"predecessor-version":[{"id":5954,"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/pages\/5946\/revisions\/5954"}],"wp:attachment":[{"href":"https:\/\/www.ub.edu\/nutrimetabolomics\/wp-json\/wp\/v2\/media?parent=5946"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}