BACKGROUND Tumor purity plays an important role in understanding the pathogenic mechanism of tumors. The purity of tumor samples is highly sensitive to tumor heterogeneity. Due to Intratumoral heterogeneity of genetic and epigenetic data, it is suitable to study the purity of tumors. Among them, there are many purity estimation methods based on copy number variation, gene expression and other data, while few use DNA methylation data and often based on selected information sites. Consequently, how to choose methylation sites as information sites has an important influence on the purity estimation results. At present, the selection of information sites was often based on the differentially methylated sites that only consider the mean signal, without considering other possible signals and the strong correlation among adjacent sites. RESULTS Considering integrating multi-signals and strong correlation among adjacent sites, we propose an approach, PEIS, to estimate the purity of tumor samples by selecting informative differential methylation sites. Application to 12 publicly available tumor datasets, it is shown that PEIS provides accurate results in the estimation of tumor purity which has a high consistency with other existing methods. Also, through comparing the results of different information sites selection methods in the evaluation of tumor purity, it shows the PEIS is superior to other methods. CONCLUSIONS A new method to estimate the purity of tumor samples is proposed. This approach integrates multi-signals of the CpG sites and the correlation between the sites. Experimental analysis shows that this method is in good agreement with other existing methods for estimating tumor purity.

中文翻译：

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PEIS：一种肿瘤纯度评估的新方法，通过基于DNA甲基化数据的信号整合来识别信息位点。

背景技术肿瘤纯度在理解肿瘤的致病机理中起重要作用。肿瘤样品的纯度对肿瘤异质性高度敏感。由于遗传和表观遗传学数据的瘤内异质性，适合研究肿瘤的纯度。其中，有许多基于拷贝数变异，基因表达和其他数据的纯度估算方法，而很少使用DNA甲基化数据，通常基于选定的信息位点。因此，如何选择甲基化位点作为信息位点对纯度估算结果有重要影响。当前，信息位点的选择通常基于仅考虑平均信号而没有考虑其他可能信号和相邻位点之间的强相关性的差异甲基化位点。结果考虑到整合多信号和相邻位点之间的强相关性，我们提出了一种通过选择信息性差异甲基化位点来估计肿瘤样品纯度的方法，即PEIS。将PEIS应用于12个可公开获得的肿瘤数据集，结果表明PEIS在估算肿瘤纯度方面提供了准确的结果，与其他现有方法具有高度一致性。此外，通过比较不同信息位点选择方法在评估肿瘤纯度方面的结果，表明PEIS优于其他方法。结论提出了一种估计肿瘤样品纯度的新方法。这种方法集成了CpG站点的多信号以及站点之间的相关性。