背景:高维转录组分析,无论是通过下一代测序技术还是高通量阵列,可能导致变量分散,数据缺失。数据插补是一种常见的策略,可以通过使用统计技术来填充缺失值,从而最大程度地包含样本。然而,许多数据插补方法繁琐,有引入系统性偏差的风险。
结果:我们提出了一种新的数据插补方法,使用约束最小二乘法和反问题文献中的算法,并介绍了该技术在miRNA表达分析中的应用。所提出的技术被证明可以更快地提供一个数量级的归算,与文献中的类似方法相比,精度大于或等于精度。
结论:这项研究为数据填补提供了一种稳健而有效的算法,可以使用,例如,在数据缺失的情况下提高癌症预测的准确性。
BACKGROUND: High dimensional transcriptome profiling, whether through next generation sequencing techniques or high-throughput arrays, may result in scattered variables with missing data. Data imputation is a common strategy to maximize the inclusion of samples by using statistical techniques to fill in missing values. However, many data imputation methods are cumbersome and risk introduction of systematic bias.
RESULTS: We present a new data imputation method using constrained least squares and algorithms from the inverse problems literature and present applications for this technique in miRNA expression analysis. The proposed technique is shown to offer an imputation orders of magnitude faster, with greater than or equal accuracy when compared to similar methods from the literature.
CONCLUSIONS: This study offers a robust and efficient algorithm for data imputation, which can be used, e.g., to improve cancer prediction accuracy in the presence of missing data.