Normalization of RNA-seq data using factor analysis of control genes or samples
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Summary
This work proposes a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes or samples and leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods.
- Type
- article
- Published
- 2014-08-24
- Cited by
- 1,936
- References
- 46
- Access
- Open access
- OpenAlex
- https://openalex.org/W2039521726
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1966718
Keywords
Normalization (sociology), RNA-Seq, Inference, Computer science, Computational biology
References
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- Development and applications of single cell transcriptome analysis
- Generalized Linear Models
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- Silencing of odorant receptor gene expression by G protein βγ signaling ensures the expression of one odorant receptor per olfactory sensory neuron
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
- Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.
- Correction for hidden confounders in the genetic analysis of gene expression
- A scaling normalization method for differential expression analysis of RNA-seq data
- The use of miRNA microarrays for the analysis of cancer samples with global miRNA decrease
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- edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
Cited by
- Bioinformatics approaches to single-cell analysis in developmental biology.
- RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles
- Reference standards for next-generation sequencing
- Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis
- DCARS: Differential correlation across ranked samples
- Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts
- The Regulation of Gene Expression During Memory Consolidation in the Hippocampus
- Integrative Analyses of Cancer Data: A Review from a Statistical Perspective
- Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression.
- Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates
- Mutant p53 cooperates with the SWI/SNF chromatin remodeling complex to regulate VEGFR2 in breast cancer cells
- Standardization efforts enabling next-generation sequencing and microarray based biomarkers for precision medicine.
- RNA sequencing of the nephron transcriptome: a technical note
- Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
- How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets
- Focused human gene expression profiling using dual-color reverse transcriptase multiplex ligation-dependent probe amplification.
- Claudin multigene family in channel catfish and their expression profiles in response to bacterial infection and hypoxia as revealed by meta-analysis of RNA-Seq datasets.
- Computational and analytical challenges in single-cell transcriptomics
- Statistical methods for detecting differentially methylated loci and regions
- Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses
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