ArticleBioinformatics (Oxford, England)2022
hCoCena: horizontal integration and analysis of transcriptomics datasets.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- High-throughput transcriptome analyses from ASPIRO, a phase 1/2/3 study of gene replacement therapy for X-linked myotubular myopathy.American journal of human genetics · 2023Trial
- Maturation-Dependent Constraints in the Ability of Human Monocytes to Initiate Trained Immunity.MedComm · 2026Article
- Longitudinal clinical proteomics reveals pneumonia type-specific protein biomarkers and autoantibodies.JCI insight · 2026Article
- TREM2 expression level is critical for microglial state, metabolic capacity and efficacy of TREM2 agonism.Nature communications · 2026Article
- Longitudinal clinical proteomics reveals pneumonia type-specific protein biomarkers and autoantibodies.medRxiv : the preprint server for health sciences · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Deep immune profiling delineates hallmarks of disease heterogeneity in extrapulmonary tuberculosis.Nature communications · 2025Article
- A systems-level insight into PHB-driven metabolic adaptation orchestrated by the PHB-binding transcriptional regulator AniA (PhaR).mSystems · 2025Article
- Article
- Transcriptomic profile of microglia following inflammation-sensitized hypoxic-ischemic brain injury in neonatal rats suggests strong contribution to neutrophil chemotaxis and activation.Journal of neuroinflammation · 2025Article
- Article
- Oxidative phosphorylation is a key feature of neonatal monocyte immunometabolism promoting myeloid differentiation after birth.Nature communications · 2025Article
- hCoCena: A toolbox for network-based co-expression analysis and horizontal integration of transcriptomic datasets.STAR protocols · 2024Article
- A clinical protocol for a German birth cohort study of the Maturation of Immunity Against respiratory viral Infections (MIAI).Frontiers in immunology · 2024Article
- Data Integration from Heterogeneous Control Levels for the Purposes of Analysis within Industry 4.0 Concept.Sensors (Basel, Switzerland) · 2022Article
- Human variation in population-wide gene expression data predicts gene perturbation phenotype.iScience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationTranscriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding and comparison of different conditions and phenotypes. Since large study designs with a broad variety of conditions are costly and laborious, extensive comparisons are hindered when utilizing only a single dataset. Thus, there is an increased need for tools that allow the integration of multiple transcriptomic datasets with subsequent joint analysis, which can provide a more systematic understanding of gene co-expression and co-functionality within and across conditions. To make such an integrative analysis accessible to a wide spectrum of users with differing levels of programming expertise it is essential to provide user-friendliness and customizability as well as thorough documentation.
resultsThis article introduces horizontal CoCena (hCoCena: horizontal construction of co-expression networks and analysis), an R-package for network-based co-expression analysis that allows the analysis of a single transcriptomic dataset as well as the joint analysis of multiple datasets. With hCoCena, we provide a freely available, user-friendly and adaptable tool for integrative multi-study or single-study transcriptomics analyses alongside extensive comparisons to other existing tools. AVAILABILITY AND IMPLEMENTATION: The hCoCena R-package is provided together with R Markdowns that implement an exemplary analysis workflow including extensive documentation and detailed descriptions of data structures and objects. Such efforts not only make the tool easy to use but also enable the seamless integration of user-written scripts and functions into the workflow, creating a tool that provides a clear design while remaining flexible and highly customizable. The package and additional information including an extensive Wiki are freely available on GitHub: https://github.com/MarieOestreich/hCoCena. The version at the time of writing has been added to Zenodo under the following link: https://doi.org/10.5281/zenodo.6911782. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.