InfiniumPurify: An R package for estimating and accounting for tumor purity in cancer methylation research.

The proposition of cancer cells in a tumor sample, named as tumor purity, is an intrinsic factor of tumor samples and has potentially great influence in variety of analyses including differential methylation, subclonal deconvolution and subtype clustering. InfiniumPurify is an integrated R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450 k array data. InfiniumPurify has three main functions getPurity, InfiniumDMC and InfiniumClust, which could infer tumor purity, differential methylation analysis and tumor sample cluster accounting for estimated or user-provided tumor purities, respectively. The InfiniumPurify package provides a comprehensive analysis of tumor purity in cancer methylation research.

Availability

The R package InfiniumPurify is available from http://cran.r-project.org/web/packages.

Introduction

Tumor purity, defined as the percentage of cancer cells in a solid tumor sample, is an important characteristic that cannot be ignored in cancer genomics or epigenomics data analysis.1, 2, 3, 4 Due to the normal cell contamination in tumor tissue, high-throughput data obtained from tumor samples are mixed signals of cancer and normal cells. Thus the purity effect must be accounted for in various data analyses such as sample clustering/classification and differential expression/methylation.5, 6 Till now, a few methods and software tools are available for tumor purity estimation, mainly based on gene expression or copy number variation data. A comprehensive review is provided by.

Here we present the InfiniumPurify, a comprehensive R package to evaluate and account for tumor purity in a series of cancer methylation researches based on Infinium 450 k array data. It includes the following functions: getPurity, which estimates tumor purities from beta value matrices of tumor and normal samples; InfiniumDMC, which performs differential methylation analysis accounting for tumor purities estimated from getPurify; InfiniumPurify, which infers purified tumor methylomes from tumor, normal samples and purities; InfiniumClust, which classified tumor samples into different methylation subtypes corrected by tumor purities.

Methods

InfiniumPurify takes beta value matrix of tumor and normal samples as input, which could be obtained from ChAMP, DMRcate, minfi or some related R packages. Note that if starting with raw CEL data of Infinium 450 k array, a normalization step is essential for data preparation. To be specific, two types of probes (type-I and type-II) are used in Infinium 450 k chip and they may have different beta distributions. Moreover, tumor samples exhibit a global different pattern with normal samples, i.e., hyper methylation in promoter regions and global hypo-methylation in the whole genome. So we prefer functional normalization in data preparation.

getPurity: estimate tumor purity from DNA methylation Infinium 450 k array data

The function getPurity is used to estimate tumor purities of tumor samples. It takes methylation beta value matrix of tumor (and optionally normal) samples and tumor type as inputs, and outputs a vector of tumor purities for all tumor samples. If normal data are available and numbers of tumor and normal samples are both sufficient large (≥20), the function first identifies a number of informative differentially methylated CpG sites (iDMCs) by comparing the methylation differences between tumor and normal samples and variation in tumor samples. Then methylation levels of the selected iDMCs are used to estimate tumor purity for each tumor sample by density evaluation of Gaussian kernel. When normal sample is unavailable or tumor/normal samples are too few to get reliable iDMCs, getPurity will load pre-selected iDMCs identified from public TCGA data to infer tumor purities. In such case, the tumor type needs to be specified by the user.

As an application, we calculated tumor purities for all tumor samples with methylation 450 k array data in TCGA, which are available from https://doi.org/10.5281/zenodo.253193. Comparison with purity estimated from other tools shows good correlation.13, 14

InfiniumDMC: differential methylation analysis accounting for tumor purity

Tumor purity could serious bias or weaken differential methylation analysis if not correctly accounting for. There are a few discussions on differential expression analysis with the consideration of tumor purity, and most of them simply add tumor purity as a covariate in regression model. However, as is showed in our work through rigorous data modeling, the tumor purity has multiplicative effect on differential methylation (as well as different expression), instead of additive.

InfiniumDMC takes beta value matrix of tumor (and optionally normal)and purities for all tumor samples as inputs. Note that the purities can be the results from getPurity or other tools. The DM calling is performed under the following two scenarios. With normal sample size more than 20, InfiniumDMC tests the significance of differential methylation comparing tumor and normal data based on a generalized least square procedure. Otherwise when normal samples are too few or unavailable, InfiniumDMC will use data from tumor samples alone and test the association between tumor beta values and tumor purities. The latter control-free DM calling method provides an alternative way to DM analysis when normal controls are not available or of low quality.

InfiniumPurify: deconvolute pure tumor methylomes

InfiniumPurify is to deconvolute pure tumor cellmethylomes from tumor samples, normal samples and tumor purity through a linear regression model. Intuitively, a CpG site is likely to be differentially methylated if it is highly correlated to tumor purities. In Figure 1, we show a CpG site with no significant methylation difference in tumor and normal samples by minfi. But its high correlation between tumor methylation and purity indicate that tumor methylations are seriously affected by tumor purity. After we corrected the purity effect by InfiniumPurify, its difference between purified tumor and normal methylomesis very significant.

Figure 1

An example showing DMCs that are only detected by InfiniumPurify. Left panel shows their methylation level distributions in tumor and normal samples. Middle panel shows correlation between purities and methylation levels. Right panel shows methylation levels of normal and tumor samples after correcting for tumor purities.

InfiniumClust: cluster tumor sample accounting for tumor purity

DNA methylation plays an important role in tumorigenesis, thus clustering of tumor samples into different epigenetic subtypes is helpful in identifying diagnostic biomarker and therapeutic target in clinical practice. InfiniumClust is the first attempt to attribute tumor samples into subtypes after correcting tumor purity effect. It assumes pure normal methylome and tumor methylomes of different subtypes follow normal distribution after arcsine transformation. The clustering membership of a tumor sample is denoted as a latent variable that is optimized by Expectation-Maximization (EM) algorithm from the tumor-normal mixture model.

InfiniumClust takes beta value matrix of and purities for a number of tumor samples and reports the probabilities of cluster membership. Given a user-specified number K of clusters, the function returns a list consisting of likelihood and membership matrix, where row corresponds to tumor samples and column corresponds to K clusters.

Conclusion

The R package InfiniumPurify contains a series of functions for DNA methylation analysis in cancer research accounting for tumor purity.

Conflict of interest

None declared.