ArticleTranslational cancer research2014
Statistical strategies for microRNAseq batch effect reduction.
Article in Translational cancer research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- miRNA Library Preparation Optimisation for Low-Concentration and Low-Volume Paediatric Plasma Samples.Non-coding RNA · 2025Article
- Magnetic transferrin nanoparticles (MTNs) assay as a novel isolation approach for exosomal biomarkers in neurological diseases.Biomaterials research · 2023Article
- Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks.Scientific reports · 2022Article
- An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks.Biomedicines · 2020Article
- Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application.Evolutionary bioinformatics online · 2020Article
- Cross-Regional View of Functional and Taxonomic Microbiota Composition in Obesity and Post-obesity Treatment Shows Country Specific Microbial Contribution.Frontiers in microbiology · 2019Article
- Multi-perspective quality control of Illumina RNA sequencing data analysis.Briefings in functional genomics · 2017Article
- Pan-cancer analysis of systematic batch effects on somatic sequence variations.BMC bioinformatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. Among the many difficulties of RNAseq analysis, correctly adjusting for batch effect is a pivotal one for large-scale RNAseq based studies. The batch effect of RNAseq data is most obvious in microRNA (miRNA) sequencing studies. Using real miRNA sequencing (miRNAseq) data, we evaluated several batch removal techniques and discussed their effectiveness. We illustrate that by adjusting for batch effect, more reliable differentially expressed genes can be identified. Our study on batch effect in miRNAseq data can serve as a guideline for future miRNAseq studies that might contain batch effect.
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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.