Solid tissues collected from patient-driven clinical settings are composed of both normal and cancer cells, which often precede complications in data analysis and epigenetic findings. The Purity estimation of samples is crucial for reliable genomic aberration identification and uniform inter-sample and inter-patient comparisons as well. Here, an effective and flexible method has been developed and designed to estimate the level of methylation, which infers tumor purity without prior knowledge from the other datasets. The comprehensive analysis of our approach on Illumina Infinium 450 k methylation microarray explains that TCGA Breast Cancer data exhibits improved performance for purity assessment. This assessment has a strong correlation with other advanced methods.

从患者驱动的临床环境中收集的实体组织由正常细胞和癌细胞组成，这通常先于数据分析和表观遗传发现的并发症。样本的纯度估计对于可靠的基因组畸变识别以及统一的样本间和患者间比较也至关重要。在这里，已经开发并设计了一种有效且灵活的方法来估计甲基化水平，该方法无需其他数据集的先验知识即可推断肿瘤纯度。对我们在 Illumina Infinium 450 k 甲基化微阵列上的方法的综合分析解释了 TCGA 乳腺癌数据显示出改进的纯度评估性能。这种评估与其他先进方法有很强的相关性。