An Automated Respiratory Data Pipeline for Waveform Characteristic Analysis
Whole body barometric flow through plethysmography is used to study respiratory output in mouse and rat genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams including respiratory-pressure waveforms, O and CO measurement, and other data poin...
Gespeichert in:
| Veröffentlicht in: | The FASEB journal Jg. 36 Suppl 1 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
01.05.2022
|
| ISSN: | 1530-6860 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Whole body barometric flow through plethysmography is used to study respiratory output in mouse and rat genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams including respiratory-pressure waveforms, O
and CO
measurement, and other data points for a reasonable estimation of tidal volume and key metabolic parameters. These experiments typically produce large amounts of data that are time-consuming and difficult to analyze and organize. For analysis, the investigator may use cumbersome hand annotation subject to observer bias or several software products may be combined to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems that limit options for those using bespoke respiratory measurement systems. To address these deficiencies, we developed a software pipeline for processing raw respiratory recordings and associated metadata (experimental design, age, weight, temperature, etc.) into operative respiratory outcomes, publication-worthy graphs, and robust statistical analyses. This pipeline consists of three modules. The first module is a Python-based graphical user interface (GUI) for uploading all relevant data files and allows the user to define and label independent, dependent, and covariate variables relevant to the experimental design, statistical analysis, and graphical outputs. Additionally, parameters can be programmed to define waveform feature segmentation to include manual annotations. In the second module, BASSPRO, the data are processed with a Python program to segment the respiratory waveform based on user-defined thresholds for movement artifacts and unique features. BASSPRO quantifies respiratory cycle components and demarks unique features including sighs, apneas, expiratory reflexes, and user-defined entities. The module also allows for browsing the raw signals with markup of automatically annotated breaths to facilitate validation of breath detection and to permit manual annotation of features, if desired. The third module, STAGG, is an R-based program that accepts the BASSPRO output along with configuration files from the GUI to graph the desired variables while performing a linear mixed effects statistical model analysis of the data with appropriate post-hoc tests. Following graph generation, statistically significant differences in respiratory variables are automatically annotated in the graphs. Following statistical testing, residual analyses are provided that allow the user to determine if a transformation of their data may be appropriate. If so, transformations can be selected within the GUI so no external data analyses are necessary. The open-source program offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. |
|---|---|
| AbstractList | Whole body barometric flow through plethysmography is used to study respiratory output in mouse and rat genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams including respiratory-pressure waveforms, O
and CO
measurement, and other data points for a reasonable estimation of tidal volume and key metabolic parameters. These experiments typically produce large amounts of data that are time-consuming and difficult to analyze and organize. For analysis, the investigator may use cumbersome hand annotation subject to observer bias or several software products may be combined to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems that limit options for those using bespoke respiratory measurement systems. To address these deficiencies, we developed a software pipeline for processing raw respiratory recordings and associated metadata (experimental design, age, weight, temperature, etc.) into operative respiratory outcomes, publication-worthy graphs, and robust statistical analyses. This pipeline consists of three modules. The first module is a Python-based graphical user interface (GUI) for uploading all relevant data files and allows the user to define and label independent, dependent, and covariate variables relevant to the experimental design, statistical analysis, and graphical outputs. Additionally, parameters can be programmed to define waveform feature segmentation to include manual annotations. In the second module, BASSPRO, the data are processed with a Python program to segment the respiratory waveform based on user-defined thresholds for movement artifacts and unique features. BASSPRO quantifies respiratory cycle components and demarks unique features including sighs, apneas, expiratory reflexes, and user-defined entities. The module also allows for browsing the raw signals with markup of automatically annotated breaths to facilitate validation of breath detection and to permit manual annotation of features, if desired. The third module, STAGG, is an R-based program that accepts the BASSPRO output along with configuration files from the GUI to graph the desired variables while performing a linear mixed effects statistical model analysis of the data with appropriate post-hoc tests. Following graph generation, statistically significant differences in respiratory variables are automatically annotated in the graphs. Following statistical testing, residual analyses are provided that allow the user to determine if a transformation of their data may be appropriate. If so, transformations can be selected within the GUI so no external data analyses are necessary. The open-source program offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. |
| Author | Ward, Christopher Twitchell-Heyne, Avery Lusk, Savannah Ray, Russell Allen, Genevera Chang, Andersen |
| Author_xml | – sequence: 1 givenname: Savannah surname: Lusk fullname: Lusk, Savannah organization: Baylor College of Medicine, Pearland, TX – sequence: 2 givenname: Christopher surname: Ward fullname: Ward, Christopher organization: Baylor College of Medicine, Houston, TX – sequence: 3 givenname: Andersen surname: Chang fullname: Chang, Andersen organization: Rice University, Houston, TX – sequence: 4 givenname: Avery surname: Twitchell-Heyne fullname: Twitchell-Heyne, Avery organization: Baylor College of Medicine, Houston, TX – sequence: 5 givenname: Genevera surname: Allen fullname: Allen, Genevera organization: Rice University, Houston, TX – sequence: 6 givenname: Russell surname: Ray fullname: Ray, Russell organization: Baylor College of Medicine, Houston, TX |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35551872$$D View this record in MEDLINE/PubMed |
| BookMark | eNo1z9lKxDAUgOEgirPoK2heoDVLk55elrrigDIqXg6naYoZupFkhL69A-rVd_fDvyKnwzhYQq45Szkr9E2Lwdb7VDAhUqnTN55utcjUCVlyJVmiQbMFWYWwZ4xxxvU5WUilFIdcLMlzOdDyEMceo23o1obJeYyjn-ktRqSvbrKdGyxtR08_8dse7Wn1hR5NtN6F6AwtB-zm4MIFOWuxC_byzzX5uL97rx6TzcvDU1VuEsMFZEmrDapGM22gzQQYBbLOCpBMcps3hcg5cJBQqBYyQF1LVAIKo_NagzguiDW5-u1Oh7q3zW7yrkc_7_6vxA98FE__ |
| ContentType | Journal Article |
| Copyright | FASEB. |
| Copyright_xml | – notice: FASEB. |
| DBID | NPM |
| DOI | 10.1096/fasebj.2022.36.S1.R6245 |
| DatabaseName | PubMed |
| DatabaseTitle | PubMed |
| DatabaseTitleList | PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1530-6860 |
| ExternalDocumentID | 35551872 |
| Genre | Journal Article |
| GroupedDBID | --- -DZ -~X .55 0R~ 0VX 123 18M 1OB 1OC 29H 2WC 33P 34G 39C 3O- 4.4 53G 5GY 5RE 85S AAHQN AAMMB AAMNL AANLZ AAYCA ABCUV ABDNZ ABEFU ABJNI ABOCM ACCZN ACGFS ACIWK ACNCT ACPOU ACPRK ACXQS ACYGS ADKYN ADXHL ADZMN AEFGJ AEIGN AENEX AEUYR AEYWJ AFFNX AFFPM AFRAH AFWVQ AGCDD AGHNM AGXDD AGYGG AHBTC AI. AIDQK AIDYY AITYG AIURR AIZAD ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMYDB BFHJK BIYOS C1A CS3 DCZOG DU5 D~5 E3Z EBS EJD F5P F9R H13 HGLYW HZ~ H~9 J5H L7B LATKE LEEKS MEWTI MVM NEJ NPM O9- OHT OVD Q-A RHI RJQFR ROL SAMSI SJN SUPJJ TEORI TFA TR2 TWZ U18 VH1 W8F WH7 WHG WOQ WXSBR X7M XJT XOL XSW Y6R YBU YHG YKV YNH YSK Z0Y ZCA ZE2 ZGI ZXP ~KM |
| ID | FETCH-LOGICAL-c1284-f6ca5d606c8f428c583b4983031e7d92718183895f848a6b3a5289c67b6820012 |
| IngestDate | Mon Jul 21 05:57:45 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | FASEB. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c1284-f6ca5d606c8f428c583b4983031e7d92718183895f848a6b3a5289c67b6820012 |
| PMID | 35551872 |
| ParticipantIDs | pubmed_primary_35551872 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-May |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-May |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | The FASEB journal |
| PublicationTitleAlternate | FASEB J |
| PublicationYear | 2022 |
| SSID | ssj0001016 |
| Score | 2.3891957 |
| Snippet | Whole body barometric flow through plethysmography is used to study respiratory output in mouse and rat genetic and disease models. Turn-key commercial and... |
| SourceID | pubmed |
| SourceType | Index Database |
| Title | An Automated Respiratory Data Pipeline for Waveform Characteristic Analysis |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35551872 |
| Volume | 36 Suppl 1 |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELYWCohLVZ59UOQDt1UCiWPHPqYFhFRphWAR3JDtdaTlEVaw3cKJv96xHW8CggoOvUSrWLIif99OvpnMA6EtqpVJldyJjE5YlGnBIyW4juzwN2kMIWmp3LCJvNfjZ2fisNN5DLUwk6u8qvj9vRj9V6jhHoBtS2ffAfd0U7gBvwF0uALscH0T8IUNYIxvQIkaW3rYfEnflWPZPRyOjFOWNr3wVE6MFa3uo3vTtnnaqaStXC2f9ovjvR_d9lPYVJ7fd5c-tgyivGrCy6fSZ8232he0cgm8iSlcaU1Tjdb_Mxy71NTowDzUsdZJqNquYxPg1k4zAWMT7Cl4p9yPDAgGl7CuG1naTV604eBU2TOGl7i6iO2uMWHxcRIfsdR3nmyBOLp2KIJoogn3M4D-vfqsuXZYmkEzObN2vWeDPfWL3AY2QkqgYNuvPNEiWgi7PHNNnETpf0Ifa98CF54TS6hjqmU076eNPqygX0WFp8zALWZgywwcmIGBETgwAz9lBg7MWEUn-3v9nwdRPUsj0laBRCXTkg7AW9W8BI9TU05UJjgImMTkA5GCRAHjzgUtecYlU0RScMU1yxXjNu0uXUOz1U1lPiNMQNJJSqSThmkm5SChiogsVzQfECa_oHV_Cucj3zDlPJzP11dXvqHFhj8b6EMJf0bzHc3pyXh4d7vpcPkLGfdWCw |
| linkProvider | National Library of Medicine |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Automated+Respiratory+Data+Pipeline+for+Waveform+Characteristic+Analysis&rft.jtitle=The+FASEB+journal&rft.au=Lusk%2C+Savannah&rft.au=Ward%2C+Christopher&rft.au=Chang%2C+Andersen&rft.au=Twitchell-Heyne%2C+Avery&rft.date=2022-05-01&rft.eissn=1530-6860&rft.volume=36+Suppl+1&rft_id=info:doi/10.1096%2Ffasebj.2022.36.S1.R6245&rft_id=info%3Apmid%2F35551872&rft_id=info%3Apmid%2F35551872&rft.externalDocID=35551872 |