3ee1d7956cf33434ef97d93b6670368d pmc_34442375.pdf ec24ae955d537a11b6195770622a05f29360e07e pmc_34442375.pdf 056fa8e1189cd023a01664fca7e61b17982acdf1bfaaedab073db82281d2e0e7 pmc_34442375.pdf Title: Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer Subject: Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets. Keywords: genetic diseases; cohort analyzer; human phenotype ontology; cluster analysis; phenotype quality assessment Author: Elena Rojano, José Córdoba-Caballero, Fernando M. Jabato, Diana Gallego, Mercedes Serrano, Belén Pérez, Álvaro Parés-Aguilar, James R. Perkins, Juan A. G. Ranea and Pedro Seoane-Zonjic Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Jul 27 15:13:09 2021 CEST ModDate: Tue Jul 27 15:26:31 2021 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 25 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3840481 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HPMJPC+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 KHNHJA+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 LSSPDW+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 UIJACC+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 27 0 BSDWLC+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 30 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 52 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 56 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 60 0 PALDJZ+CMSY10 Type 1 Builtin yes yes no 78 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 82 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 86 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 90 0 TimesNewRoman CID TrueType Identity-H yes no yes 113 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-02 03:06:01 CEST RepresentationInformation: pmc_34442375.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-03-31 12:14:56 CEST Size: 3840481 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 439 FreeObjects: 3 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Cohort Analyzer Destination: subsection.2.1 Children: Item: Title: General Statistics Calculation Destination: subsubsection.2.1.1 Item: Title: Human Phenotype Terms Frequency and Distribution Analysis Destination: subsubsection.2.1.2 Item: Title: Dataset Specificity Index Destination: subsubsection.2.1.3 Item: Title: Cluster Analysis and Information Content Distribution Destination: subsubsection.2.1.4 Item: Title: Case Studies: Assessing the Phenotypic and Functional Space within the Top Patient Clusters Destination: subsection.2.2 Item: Title: Cohort Descriptions Destination: subsection.2.3 Children: Item: Title: DECIPHER Cohort Destination: subsubsection.2.3.1 Item: Title: ID/MCA Cohort Destination: subsubsection.2.3.2 Item: Title: PMM2-CDG Cohort Destination: subsubsection.2.3.3 Item: Title: Results Destination: section.3 Children: Item: Title: Revealing Differences between Cohorts in Terms of Phenotype Information Destination: subsection.3.1 Item: Title: Identifying Patient Subgroups with Low Information Profiles Destination: subsection.3.2 Item: Title: Removing Patients with Limited Phenotypic Information from the Cohort and Its Effect on the Dataset Properties Destination: subsection.3.3 Item: Title: Comparing Phenotype Profiles to Cluster Patients into Phenotypically-Related Subgroups Destination: subsection.3.4 Item: Title: Genomic Variant Data Analysis Destination: subsection.3.5 Item: Title: Using Variant Data to Analyse Patient Clusters Destination: subsection.3.6 Item: Title: Discussion Destination: section.4 Item: Title: References Destination: section.4 Info: Title: Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer Author: Elena Rojano, José Córdoba-Caballero, Fernando M. Jabato, Diana Gallego, Mercedes Serrano, Belén Pérez, Álvaro Parés-Aguilar, James R. Perkins, Juan A. G. Ranea and Pedro Seoane-Zonjic Subject: Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets. 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