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. 2017 Jan 25;18(1):17.
doi: 10.1186/s13059-016-1143-5.

Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies

Xiaoqi Zheng  1 , Naiqian Zhang  2 , Hua-Jun Wu  3 , Hao Wu  4
Affiliations

Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies

Xiaoqi Zheng et al. Genome Biol. .

Abstract

We present a set of statistical methods for the analysis of DNA methylation microarray data, which account for tumor purity. These methods are an extension of our previously developed method for purity estimation; our updated method is flexible, efficient, and does not require data from reference samples or matched normal controls. We also present a method for incorporating purity information for differential methylation analysis. In addition, we propose a control-free differential methylation calling method when normal controls are not available. Extensive analyses of TCGA data demonstrate that our methods provide accurate results. All methods are implemented in InfiniumPurify.

Keywords: Cancer epigenomics; DNA methylation; Differential methylation; Tumor purity.

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Figures

Fig. 1
Fig. 1
A flowchart to illustrate the InfiniumPurify algorithm, including purity estimation and DM calling procedures
Fig. 2
Fig. 2
Purity estimates from TCGA data. a Scatter plots showing comparison of purities for all TCGA tumor samples from InfiniumPurify with ESTIMATE, ABSOLUTE, LUMP, IHC, and CPE, respectively. b Correlations between InfiniumPurify and other estimates for all TCGA cancer types. c Distribution of estimated tumor purities from InfiniumPurify for all TCGA cancer types
Fig. 3
Fig. 3
Correlations between tumor purity and methylation levels are high for DMCs. a Distribution densities of correlations between tumor purity and methylation levels for all CpG sites, from observed and randomly sampled data, based on LUAD data. b Boxplots of correlations, stratified by rank-sum test statistics. c Boxplots of rank-sum test statistics, stratified by correlations
Fig. 4
Fig. 4
Differential methylation analysis results, with normal control. a Numbers of differential methylated CpG sites (FDR < 0.01). b Spatial correlations among test statistics from nearby CpG sites. c Average pan-cancer correlation of test statistics. d Enrichment p values for top 1000 differentially methylated genes within “PATHWAY-IN-CANCER” from KEGG
Fig. 5
Fig. 5
Differential methylation analysis, without normal control. a Examples of ROC curves from InfiniumPurify control-free DMC calling model, where results from tumor-normal comparison are treated as gold standard. b AUCs for selected cancer types in TCGA. c Heatmap showing overlaps of the top 50,000 DMCs among different cancer types
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