Adaptive Savitzky–Golay Filters for Analysis of Copy Number Variation Peaks from Whole-Exome Sequencing Data

Abstract

Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis.

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

“…This paper proposes a velocity estimation method for space IR dim targets that combines multi-satellite positioning (MSP) and robust locally weighted regression. To validate the effectiveness of the proposed method, this study used MSP to directly solve the MSP combined with the LMS [26] method (MSP-LMS), the MSP combined with LWR [28] method (MSP-LWR), and the MSP combined with the ASG [31] method (MSP-ASG) for a velocity estimation comparison experiment. All the methods were implemented based on MATLAB R2021b and run on the PC (Intel Core I7-11800H + 16GDDR4) platform.…”

“…The effectiveness of the classic Savitzky-Golay filter depends on the correct choice of polynomial degree, which may lead to overfitting or loss of detail information for high-order polynomial fitting. Ochieng et al proposed an improved Adaptive Savitzky-Golay filter (ASG), which automatically adjusts parameters by dynamically modifying the smoothing results of the filter to ensure high-precision feature extraction [31]. The LWR method can adapt to the distribution and density of different data points, as well as retain the local characteristics and nonlinear trends of the data.…”