Evidence map›Paper›PMID 42639087›Full record

ArticleFrontiers in bioinformatics2026

LungMicroHostR: an R package for integrated host-microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data.

Nan Li, Jing Hu, Wanning Tong, Chengdong Liu, Yun Ding, Ning Li, Zhigang Cai

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Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Nan LiDepartment of Cardiothoracic Surgery, Naval Medical Center, Naval Medical University, Shanghai, China.
Jing HuDepartment of Cardiothoracic Surgery, Naval Medical Center, Naval Medical University, Shanghai, China.
Wanning TongDepartment of Respiratory Medicine, Naval Medical Center, Naval Medical University, Shanghai, China.
Chengdong LiuDepartment of Cardiothoracic Surgery, Naval Medical Center, Naval Medical University, Shanghai, China.
Yun DingDepartment of Cardiothoracic Surgery, Naval Medical Center, Naval Medical University, Shanghai, China.
Ning LiDepartment of Cardiothoracic Surgery, Naval Medical Center, Naval Medical University, Shanghai, China.
Zhigang CaiDepartment of Cardiothoracic Surgery, Naval Medical Center, Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Bronchoalveolar lavage fluid metagenomic next-generation sequencing captures microbial profiles and host-derived molecular measurements from the same respiratory specimen, but downstream analysis requires coordinated handling of low-biomass microbial signals, negative-control information and multiple feature tables. Methods: We developed LungMicroHostR, an R package for downstream host-microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data. The package brings processed microbial profiles, host-derived molecular measurements, sample metadata and negative-control information into a unified R workflow for feature filtering, comparative model evaluation, visualization and reproducible reporting. Results: Using the public GSE252118 resource comprising 402 samples from patients with lung cancer or pulmonary infections, LungMicroHostR assembled matched microbial, host and clinical feature tables, estimated prevalence in negative controls and compared host transcriptomic, microbial-profile and combined host-microbial models. In the test set, the 10-feature host transcriptome nearest-centroid model achieved an AUC of 0.772 (95% confidence interval, 0.680-0.860), the five-feature RNA microbial logistic model achieved an AUC of 0.745 (0.655-0.832), and the combined host transcriptome-RNA microbial logistic model achieved an AUC of 0.765 (0.655-0.866) with balanced accuracy of 0.720. An external PRJNA714488 BALF shotgun metagenomic dataset was additionally analysed at the mOTU level; LungMicroHostR matched the resulting feature table with phenotype metadata and generated a 388-feature by 26-sample microbial abundance matrix. Discussion: LungMicroHostR provides documented functions for respiratory metagenomic analyses that require joint evaluation of microbial profiles, host-derived measurements, negative-control information and external microbial feature tables.

Indexed as

bronchoalveolar lavage fluidhost–microbiome analysislung cancernegative controlsrespiratory metagenomicsR package

Identifiers

PMID42639087
PMCPMC13500579

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