DOI: 10.57760/sciencedb.35991
title: Processed serum and BALF metabolomics data for ARDS
title: KL-6 assisted subtyping of ARDS: from subtype-specific metabolomics to LPCAT1 as a pathogenic target

description: This dataset comprises processed metabolomics data generated using untargeted LC–MS analysis from serum and bronchoalveolar lavage fluid (BALF) samples collected from patients with pulmonary infection–induced acute respiratory distress syndrome (ARDS) and non-ARDS controls. Patient samples were collected between June 2023 and March 2024, during hospitalization and at a single time point per subject, representing a cross-sectional study design without longitudinal follow-up or geospatial information.The dataset is provided in Microsoft Excel (.xlsx) format and consists of multiple data sheets. In each data table, rows correspond to annotated metabolite features, while columns represent individual biological samples. Sample grouping and naming conventions are described in the accompanying README file. All values represent normalized metabolite peak areas (arbitrary units, a.u.), reflecting relative abundances rather than absolute concentrations.In the original metabolomics data, some metabolite features were below the detection limit in individual samples and therefore contained missing values. During data processing, missing values were imputed using half of the minimum non-zero value for each metabolite across the dataset, resulting in complete data matrices for downstream statistical analyses. As untargeted metabolomics data, the measurements may be influenced by sample preparation, instrumental drift, and signal noise; however, analytical stability was monitored using quality control samples, and normalization procedures were applied to reduce systematic variability.This dataset does not include raw LC–MS files and is intended to support transparency, data reuse, and reproducibility of downstream statistical, differential metabolite, and pathway analyses. The data files are stored in a standard Excel format and can be accessed and analyzed using commonly available software such as Microsoft Excel, R, or Python.
description: This dataset comprises processed metabolomics data generated using untargeted LC–MS analysis from serum and bronchoalveolar lavage fluid (BALF) samples collected from patients with pulmonary infection–induced acute respiratory distress syndrome (ARDS) and non-ARDS controls. Patient samples were collected between June 2023 and March 2024, during hospitalization and at a single time point per subject, representing a cross-sectional study design without longitudinal follow-up or geospatial information.The dataset is provided in Microsoft Excel (.xlsx) format and consists of multiple data sheets. In each data table, rows correspond to annotated metabolite features, while columns represent individual biological samples. Sample grouping and naming conventions are described in the accompanying README file. All values represent normalized metabolite peak areas (arbitrary units, a.u.), reflecting relative abundances rather than absolute concentrations.In the original metabolomics data, some metabolite features were below the detection limit in individual samples and therefore contained missing values. During data processing, missing values were imputed using half of the minimum non-zero value for each metabolite across the dataset, resulting in complete data matrices for downstream statistical analyses. As untargeted metabolomics data, the measurements may be influenced by sample preparation, instrumental drift, and signal noise; however, analytical stability was monitored using quality control samples, and normalization procedures were applied to reduce systematic variability.This dataset does not include raw LC–MS files and is intended to support transparency, data reuse, and reproducibility of downstream statistical, differential metabolite, and pathway analyses. The data files are stored in a standard Excel format and can be accessed and analyzed using commonly available software such as Microsoft Excel, R, or Python.