Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing

With the rapid advancement of single-cell RNA-seq (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 networks 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 impact on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data preprocessing steps due to over-smoothing 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 gene co-expression network and successfully revealed several known immune cell modules.

Reconstruction of Interior Sound Fields of Vibrating Shells with an Open Spherical Microphone Array

Spherical acoustic holography was utilized to reconstruct the interior sound field of an enclosed space with vibrating boundaries using an open spherical microphone array. The interior sound fields of vibrating shells, including a pulsating shell, a [Formula: see text]-axis oriented oscillating shell, a partially vibrating shell and a point-excited vibrating shell, were reconstructed, and numerical simulations were carried out to examine the impact of reconstruction parameters, the radius of the microphone array, the number of microphones, the distribution of microphones on the array surface, the wave number, the number of basis functions used, and the radius of the reconstruction surface on the accuracy of reconstruction. In order to minimize the error of reconstruction caused by a variety of factors and uncertainties, such as the measurement noise, regularization treatments were introduced into the process of reconstructing, to suppress the divergent trends of the reconstruction error along with the increase of the wave number and the increase of the radius of the reconstruction surface. Results showed that a Tikhonov regularization method with generalized cross validation (GCV) could yield the least error of reconstruction among the investigated regularization methods.