Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing.
Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing.
复制标题
噪声正则化消除了单细胞RNA-SEQ数据预处理中的相关性伪影。
DOI：
10.1016/j.patter.2021.100211
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发表时间：
2021-03-12
期刊：
影响因子：
--
通讯作者：
Lim WK
中科院分区：
文献类型：
--
作者：
Zhang R;Atwal GS;Lim WK
随着单细胞RNA测序（scRNA-seq）技术的快速发展，已经提出了许多数据预处理方法来解决该技术中固有的许多系统误差和技术可变性。虽然这些方法已被证明是有效的恢复个别基因的表达，适用于推断基因-基因协会和随后的基因网络重建尚未系统地研究。在这项研究中，我们对Human Cell Atlas骨髓数据的五种代表性scRNA-seq标准化/插补方法进行了基准测试，以了解它们对推断的基因-基因关联的影响。我们的研究结果表明，相当数量的虚假相关性引入的数据预处理步骤，由于过度平滑的原始数据。我们提出了一种模型无关的噪声正则化方法，可以有效地消除相关伪影。噪声正则化的基因-基因相关性被进一步用于重建基因共表达网络，并成功地揭示了几个已知的免疫细胞模块。scRNA-seq预处理方法在推断基因-基因关联方面进行了基准测试。在数据预处理步骤中引入了虚假相关性。提出了噪声正则化方法来消除相关性伪影。基因共表达网络可以从噪声正则化的相关性构建。我们对五种有代表性的单细胞RNA测序数据预处理方法进行了基准测试，重点关注它们在推断基因-基因表达相关性方面的影响。我们发现，大量的相关性工件已被引入预处理步骤，由于数据过平滑，提出的问题，从这些预处理数据计算的相关性可能是不可靠的，应该谨慎对待。然后，我们提出了一种噪声正则化方法来惩罚过平滑数据，它可以有效地消除伪影，同时保留大部分真实的相关性。正则化的相关性可以进一步应用于构建基因-基因相关网络，这有助于获得对复杂生物系统的机理认识。从单细胞RNA测序数据中可靠地推断基因-基因相关性对于重建全球基因网络和进一步揭示生物学见解是有价值的。在我们的基准测试研究中，我们观察到在各种方法的数据预处理步骤中引入了相当数量的相关性伪影。我们在相关性计算过程中提出了一种模型无关的噪声正则化方法，可以有效地去除虚假相关性，并使研究能够在scRNA测序数据中剖析基因-基因关联。
With the rapid advancement of single-cell RNA-sequencing (scRNA-seq) technology, many data-preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene network reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on Human Cell Atlas bone marrow data with respect to their impacts on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data-preprocessing steps due to oversmoothing of the raw data. We proposed a model-agnostic noise-regularization method that can effectively eliminate the correlation artifacts. The noise-regularized gene-gene correlations were further used to reconstruct a gene co-expression network and successfully revealed several known immune cell modules. scRNA-seq preprocessing methods were benchmarked on inferring gene-gene associations Spurious correlations have been introduced during the data-preprocessing steps A noise-regularization method was proposed to eliminate the correlation artifacts Gene co-expression network can be constructed from the noise-regularized correlations In this study, we benchmarked five representative single-cell RNA-sequencing data-preprocessing methods with a focus on their influence in inferring gene-gene expression correlations. We found that substantial correlation artifacts have been introduced during the preprocessing steps due to data oversmoothing, raising the issue that correlation computed from these preprocessed data may not be reliable and should be treated with caution. We then proposed a noise-regularization method to penalize the oversmoothed data, which can effectively eliminate the artifacts while retaining the majority of the true correlations. The regularized correlations can be further applied to construct gene-gene correlation networks, which is helpful for obtaining mechanistic insights into the complex biological systems. Reliable inference of gene-gene correlation from single-cell RNA-sequencing data can be valuable in reconstructing global gene networks and further uncovering biological insights. In our benchmarking study, we observed that a considerable amount of correlation artifacts was introduced during the data-preprocessing steps from various methods. We proposed a model-agnostic noise-regularization approach in the correlation calculation procedure that can effectively remove the spurious correlations and empower studies looking to dissect gene-gene association in scRNA-sequencing data.
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