ArticleBriefings in bioinformatics2024
Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Gut microbiota responses to isocaloric macronutrient modulation in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial.The American journal of clinical nutrition · 2026Trial
- Transcriptional programs controlling sporulation in Phytophthora respond to nutrition, humidity, and cryptochrome-mediated light signals.PLoS pathogens · 2026Article
- Transcriptome Profiling of Leaves and Roots from Rooibos (Plants (Basel, Switzerland) · 2026Article
- Chronic IL-21 exposure reshapes pulmonary environment, elevating risk of respiratory diseases.Cellular and molecular life sciences : CMLS · 2026Article
- EBV Type 1 versus Type 2: A determinant of NK cell anti-tumor activity in Burkitt lymphoma.bioRxiv : the preprint server for biology · 2026Article
- Ensemble Machine Learning on Bulk RNA-Seq Identifies 17-Gene Signature Predicting Neoadjuvant Chemotherapy Response in Breast Cancer.Current issues in molecular biology · 2026Article
- REDAC: RNA-seq expression data analysis chatbot.Bioinformatics advances · 2026Article
- Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data.MethodsX · 2025Article
- Transcriptomic signatures of prostate cancer progression: a comprehensive RNA-seq study.3 Biotech · 2025Article
- Gene Co-Expression Analysis Reveals Functional Differences Between Early- and Late-Onset Alzheimer's Disease.Current issues in molecular biology · 2025Article
- From bench to bytes: a practical guide to RNA sequencing data analysis.Frontiers in genetics · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.
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