Copy number variation detection using single cell sequencing data
Copy number variation detection using single cell sequencing data
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DOI：
10.1145/3459930.3469556
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Fatima Zare;Jacob Stark;S. Nabavi
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文献类型：
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作者：
Fatima Zare;Jacob Stark;S. Nabavi
单细胞测序（SCS）已成为发现重要生物学知识的关键手段。数据分析对于从SCS数据中提取准确而有意义的信息起着至关重要的作用。然而，与批量测序相比，SCS在数据分析方面带来了新的挑战。本文提出了一种针对SCS数据的CNV检测算法。该方法首先利用AIC方法找到生成读计数信号的最佳窗口大小，并去除读计数信号中的异常值。然后，采用一种基于全变分法的分割方法，识别出显著变化点，检测出CNV片段；最后，它根据细胞的CNV模式对细胞进行分层聚类，并使用Z-score来提高细胞间的CNV检测。我们使用真实数据和模拟数据来评估该方法的性能，并将其与其他常用的CNV检测方法进行了比较。我们的研究表明，该方法在灵敏度和错误发现率方面优于现有的CNV检测方法。
Single-cell sequencing (SCS) has emerged as a critical means of discovering important biological knowledge. Data analysis plays an essential role in extracting accurate and meaningful information from SCS data. However, compared to bulk sequencing, SCS introduces new challenges in data analysis. In this paper, we present a novel CNV detection algorithm for SCS data. The proposed method, first, finds the optimal window size for generating read count signal using the AIC approach and removes outliers from the read count signal. Then, using a novel segmentation method based on the Total Variation approach, the method identifies significant change points and detects CNV segments. Finally, it uses the hierarchical clustering of cells based on their CNV patterns and employs Z-score to improve CNV detection across the cells. We used real and simulated data to evaluate the performance of the proposed method and compared its performance with those of other commonly used CNV detection methods. We show that the proposed method outperforms the existing CNV detection methods in terms of sensitivity and false discovery rate.