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Updated: Jul 5, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Published on: June 26, 2013

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基于PCA-DBSCAN的异常值去除方法用于血液-SERS数据分析.

Miaomiao Liu1, Tingyin Wang1, Qiyi Zhang1

  • 1Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350117, China. tywang@fjnu.edu.cn.

Analytical methods : advancing methods and applications
|January 17, 2024
PubMed
概括

一种新的方法,主要组件分析和基于密度的应用程序与噪音的空间聚类 (PCA-DBSCAN),有效地从癌症查数据中删除异常值. 这提高了用于早期癌症检测的表面增强拉曼光谱 (SERS) 模型的准确性.

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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