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相关概念视频

Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

816
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
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Correlations02:20

Correlations

32.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

209
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
209
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

192
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
192
Bonferroni Test01:10

Bonferroni Test

2.7K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.7K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K

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相关实验视频

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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基于 permutation 的差异关联分析的限制.

Hoseung Song1, Michael C Wu1

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Genetic epidemiology
|November 10, 2023
PubMed
概括

使用排列方法的差异关联分析 (DCA) 经常失败. 基于变的DCA难以控制I型错误,这表明在识别生物相关性中的真实差异方面表现不佳.

科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 比较生物系统有助于生物科学的进步.
  • 不同相关性分析 (DCA) 识别了不同条件的不同基因组特征关系.

研究的目的:

  • 检查基于换的DCA的局限性.
  • 调查基于 permutation 的 DCA 的不良性能实例.

主要方法:

  • 分析不同条件下的分子变化.
  • 使用DCA中的 permutation来松散参数假设.
  • 在零假设下检查I型错误控制.

主要成果:

  • 基于变的DCA违反了在null下样本可交换性的假设.
  • 基于变换的DCA在特定情况下表现不佳.
  • 基于变换的DCA通常无法控制I型错误.

结论:

  • 基于变的DCA具有重要的局限性.
  • 该方法的性能问题源于违反了虚假假设的假设.
关键词:
不同的共同表达的差异表达.差异性相关性差异性相关性交换能力可以交换.变换试验试验 变换试验

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  • 建议在使用基于变的DCA进行生物相关性分析时谨慎使用.