Fast And Robust Imputation For Mirna Expression Data Using Constrained Least Squares
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This paper introduces a new constrained least squares imputation method for miRNA expression data, showing it is faster and as accurate as existing techniques.
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The paper studies statistical imputation of missing values in high-dimensional miRNA expression datasets produced by transcriptome profiling platforms, framing the problem in a constrained least-squares framework using methods from the inverse problems literature. The authors report that their constrained least-squares approach is orders of magnitude faster than comparable literature methods while achieving greater than or equal accuracy, and they demonstrate applications in miRNA expression analysis including cancer prediction. A major caveat explicitly stated in the text is that many existing imputation methods can be cumbersome and risk introducing systematic bias, motivating the need for robustness, though the provided description does not enumerate dataset-specific limitations beyond that concern. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
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. Here 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.
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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. Here 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. data imputation constrained least squares miRNA expression analysis cancer prediction Full Text Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2022 Read the published version in BMC Bioinformatics → Version 1 posted First submitted to journal 24 Jan, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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