Dataset Information

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data.

ABSTRACT:

Summary

: Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data-driven science. We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology.

Availability and implementation

RImmPort is available as part of Bioconductor (bioconductor.org/packages/RImmPort).

Contact

rshankar@stanford.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Shankar RD

PROVIDER: S-EPMC6041747 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data.

Shankar Ravi D RD Bhattacharya Sanchita S Jujjavarapu Chethan C Andorf Sandra S Wiser Jeffery A JA Butte Atul J AJ

Bioinformatics (Oxford, England) 20170401 7

<h4>Summary</h4>: Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data-driven science. We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data acr ...[more]

PMID: 28057685

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Project description:BackgroundElectronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses.MethodsTo address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies.ResultsUsing the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression.ConclusionsThe proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.

Dataset Information

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data.

Summary

Availability and implementation

Contact

Supplementary information

Publications

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data.

Similar Datasets

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