Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction

Parker, Hilary S.; Leek, Jeffrey T.; Favorov, Alexander V.; Considine, Michael; Xia, Xiaoxin; Chavan, Sameer; Chung, Christine H.; Fertig, Elana J.

doi:10.1093/bioinformatics/btu375

Cited by 116 publications

(109 citation statements)

References 27 publications

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“…We then examined the association between the residuals of DNA methylation (independent variable) and mRNA expression (dependent variable) using a linear regression model. In FHS, we removed 25 surrogate variables (SVs) [26] from the gene expression, along with sex, age, and imputed blood cell fractions as fixed effects, and technical covariates, such as batch effects and lab effects as random effects. We also removed 25 separately computed SVs from the methylation data, along with sex, age, and imputed blood cell fractions as fixed effects, and technical covariates, such as batch effects and lab effects as random effects.…”

Section: Methodsmentioning

confidence: 99%

DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases

et al. 2016

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BackgroundChronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation.ResultsWe performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n = 8863) and trans-ethnic replication in African Americans (n = 4111). We found differential methylation at 218 CpG sites to be associated with CRP (P < 1.15 × 10–7) in the discovery panel of European ancestry and replicated (P < 2.29 × 10–4) 58 CpG sites (45 unique loci) among African Americans. To further characterize the molecular and clinical relevance of the findings, we examined the association with gene expression, genetic sequence variants, and clinical outcomes. DNA methylation at nine (16%) CpG sites was associated with whole blood gene expression in cis (P < 8.47 × 10–5), ten (17%) CpG sites were associated with a nearby genetic variant (P < 2.50 × 10–3), and 51 (88%) were also associated with at least one related cardiometabolic entity (P < 9.58 × 10–5). An additive weighted score of replicated CpG sites accounted for up to 6% inter-individual variation (R2) of age-adjusted and sex-adjusted CRP, independent of known CRP-related genetic variants.ConclusionWe have completed an EWAS of chronic low-grade inflammation and identified many novel genetic loci underlying inflammation that may serve as targets for the development of novel therapeutic interventions for inflammation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1119-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases

et al. 2016

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“…For RNA data, we normalised each tissue separately, using limma-voom 61 to remove heteroscedasticity from scaled TPM values, followed by pSVA 62 and regressing out RNA sequencing batches and clinical batches (Supplementary Methods). For the proteomics data, we regressed out batches from the log2-transformed normalised abundance values, then quantile normalised the residuals as analogous step to the differential expression analysis; all analyses were also done without quantile normalisation, with no appreciable difference in results.…”

Section: Sample Clusteringmentioning

confidence: 99%

Decoding the genomic basis of osteoarthritis

Steinberg

Southam

Butterfield

et al. 2019

Preprint

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Osteoarthritis is a serious joint disease that causes pain and functional disability for a quarter of a billion people worldwide 1 , with no disease-stratifying tools nor modifying therapy. Here, we use primary chondrocytes, synoviocytes and peripheral blood from patients with osteoarthritis to construct a molecular quantitative trait locus map of gene expression and protein abundance in disease. By integrating data across omics levels, we identify likely effector genes for osteoarthritis-associated genetic signals. We detect stark molecular differences between macroscopically intact (low-grade) and highly degenerated (high-grade) cartilage, reflecting activation of the extracellular matrix-receptor interaction pathway. Using unsupervised consensus clustering on transcriptome-wide sequencing, we identify molecularly-defined patient subgroups that correlate with clinical characteristics. Between-cluster differences are driven by inflammation, presenting the opportunity to stratify patients on the basis of their molecular profile for tailored intervention. We construct and validate a 7-gene classifier that reproducibly distinguishes between these disease subtypes. Finally, we identify potentially actionable compounds for disease modification and drug repositioning. Our findings contribute to both patient stratification and therapy development in this globally important area of unmet need.

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“…To correct for batch effects and remove unwanted variation between the first and second fish, we used the ComBat function from R Bioconductor's sva package (Parker et al 2014). After this procedure, variation between individuals was minimal (Supplemental Fig.…”

Section: Single-cell Rna-seq Data Processingmentioning

confidence: 99%

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types

et al. 2017

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The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analyzed conservation of genes specific for all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared with other immune cell types, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA sequencing of lck:GFP cells in zebrafish and obtained the first transcriptome of specific immune cell types in a nonmammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T cells, a novel type of NK-like cells, and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immunecell-specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed the higher gene turnover of trans-membrane proteins in NK cells compared with T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

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Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction

Abstract: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.

Cited by 116 publications

References 27 publications

DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases

DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases

Decoding the genomic basis of osteoarthritis

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types

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