ArticleFrontiers in genetics2012
Effect of normalization on statistical and biological interpretation of gene expression profiles.
Article in Frontiers in genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- From Local to Systemic: The Journey of Tick Bite Biomarkers in Australian Patients.International journal of molecular sciences · 2025Article
- Mechanisms of mechanical overload-induced skeletal muscle hypertrophy: current understanding and future directions.Physiological reviews · 2023Review
- Evaluation and comparison of different breast cancer prognosis scores based on gene expression data.Breast cancer research : BCR · 2023Article
- Estimands in epigenome-wide association studies.Clinical epigenetics · 2021Article
- Disentangling the Progression of Non-alcoholic Fatty Liver Disease in the Human Gut Microbiota.Frontiers in microbiology · 2021Article
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- Genetic effects on the commensal microbiota in inflammatory bowel disease patients.PLoS genetics · 2019Article
- Expression analysis of RNA sequencing data from human neural and glial cell lines depends on technical replication and normalization methods.BMC bioinformatics · 2018Article
- Comprehensive Bioinformatic Analysis Genes Associated to the Prognosis of Liposarcoma.Medical science monitor : international medical journal of experimental and clinical research · 2018Article
- Article
- Screening for interaction effects in gene expression data.PloS one · 2017Article
- Bioinformatics analysis of differentially expressed pathways related to the metastatic characteristics of osteosarcoma.International journal of molecular medicine · 2016Article
- Improved intra-array and interarray normalization of peptide microarray phosphorylation for phosphorylome and kinome profiling by rational selection of relevant spots.Scientific reports · 2016Article
- Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis.Medical oncology (Northwood, London, England) · 2015Article
- The evolution and adaptive potential of transcriptional variation in sticklebacks--signatures of selection and widespread heritability.Molecular biology and evolution · 2015Article
- Transcriptional coexpression network reveals the involvement of varying stem cell features with different dysregulations in different gastric cancer subtypes.Molecular oncology · 2014Article
- Additive, epistatic, and environmental effects through the lens of expression variability QTL in a twin cohort.Genetics · 2014Article
- Gene expression profiles associated with acute myocardial infarction and risk of cardiovascular death.Genome medicine · 2014Article
- Genetic and nongenetic variation revealed for the principal components of human gene expression.Genetics · 2013Article
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4 authors.
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Abstract
An under-appreciated aspect of the genetic analysis of gene expression is the impact of post-probe level normalization on biological inference. Here we contrast nine different methods for normalization of an Illumina bead-array gene expression profiling dataset consisting of peripheral blood samples from 189 individual participants in the Center for Health Discovery and Well Being study in Atlanta, quantifying differences in the inference of global variance components and covariance of gene expression, as well as the detection of variants that affect transcript abundance (eSNPs). The normalization strategies, all relative to raw log2 measures, include simple mean centering, two modes of transcript-level linear adjustment for technical factors, and for differential immune cell counts, variance normalization by interquartile range and by quantile, fitting the first 16 Principal Components, and supervised normalization using the SNM procedure with adjustment for cell counts. Robustness of genetic associations as a consequence of Pearson and Spearman rank correlation is also reported for each method, and it is shown that the normalization strategy has a far greater impact than correlation method. We describe similarities among methods, discuss the impact on biological interpretation, and make recommendations regarding appropriate strategies.
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