Ly Lam-Ha, Vingron Martin
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
Patterns (N Y). 2021 Dec 22;3(2):100414. doi: 10.1016/j.patter.2021.100414. eCollection 2022 Feb 11.
Despite the advances in single-cell transcriptomics, the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA sequencing (scRNA-seq) data make it hard to infer networks. Imputation can be applied in order to enhance gene-gene correlations and facilitate downstream analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks. To study this, we evaluate the differences on the performance and structure of reconstructed networks before and after imputation in single-cell data. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. However, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms while warning against the indiscriminate use of imputation before network reconstruction in general.
尽管单细胞转录组学取得了进展，但基因调控网络的重建仍然具有挑战性。实验数据中大量的零计数以及缺乏针对单细胞RNA测序（scRNA-seq）数据的共识预处理流程，使得推断网络变得困难。插补可用于增强基因-基因相关性并促进下游分析。然而，尚不清楚插补方法对基因调控网络重建有何影响。为了研究这一点，我们评估了单细胞数据插补前后重建网络在性能和结构上的差异。我们观察到基因-基因相关性的膨胀，这会影响预测的网络结构，并可能总体上降低网络重建的性能。然而，在可实现结果的适度范围内，我们也给出了关于算法合理组合的建议，同时总体上警告在网络重建之前不要不加区分地使用插补。
