ArticleBMC bioinformatics2016
A semi-parametric statistical model for integrating gene expression profiles across different platforms.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- A Statistical Framework for Measuring Reproducibility and Replicability of High-Throughput Experiments From Multiple Sources.Statistics in medicine · 2026Article
- A comparison of the TempO-Seq and Affymetrix microarray platform using RTqPCR validation.BMC genomics · 2024Article
- Building flexible and robust analysis frameworks for molecular subtyping of cancers.Molecular oncology · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Aberrant mitochondrial homeostasis at the crossroad of musculoskeletal ageing and non-small cell lung cancer.PloS one · 2022Article
- Gene Expression Profiles of the Aging Rat Hippocampus Imply Altered Immunoglobulin Dynamics.Frontiers in neuroscience · 2022Article
- PCNA in Cervical Intraepithelial Neoplasia and Cervical Cancer: An Interaction Network Analysis of Differentially Expressed Genes.Frontiers in oncology · 2021Article
- Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.Scientific reports · 2020Article
- S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data.Nucleic acids research · 2020Article
- Shambhala: a platform-agnostic data harmonizer for gene expression data.BMC bioinformatics · 2019Article
- Expression analysis of RNA sequencing data from human neural and glial cell lines depends on technical replication and normalization methods.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundDetermining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection.
methodsWe propose a rank-based semi-parametric model to determine DEGs using information across different sources and apply it to the integration of RNA-seq and microarray data. By incorporating both the significance of differential expression and the consistency across platforms, our method effectively detects DEGs with moderate but consistent signals. We demonstrate the effectiveness of our method using simulation studies, MAQC/SEQC data and a synthetic microRNA dataset.
conclusionsOur integration method is not only robust to noise and heterogeneity in the data, but also adaptive to the structure of data. In our simulations and real data studies, our approach shows a higher discriminate power and identifies more biologically relevant DEGs than eBayes, DEseq and some commonly used meta-analysis methods.
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