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相关概念视频

Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
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
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

1.6K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
1.6K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

148
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
148
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.6K
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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相关实验视频

Updated: Jun 8, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

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在大规模多重比较中控制定向错误发现率.

Wenjuan Liang1,2, Dongdong Xiang1, Yajun Mei3

  • 1KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China.

Journal of applied statistics
|November 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的统计方法,用于识别表达不足和过度表达的基因. 该程序有效地控制了两个方向的错误发现,改进了基因表达分析.

关键词:
基因表达 基因表达 基因表达数据驱动的数据驱动.边际的FDR是什么多次测试多次测试多次测试单独控制单独的控制.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

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相关实验视频

Last Updated: Jun 8, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

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科学领域:

  • 生物统计学 生物统计学
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 高通量技术产生了大量的基因表达数据.
  • 鉴定差异表达的基因 (表达不足和表达过多) 对于疾病研究至关重要.
  • 现有的方法往往无法单独控制低表达和过度表达的基因的错误发现.

研究的目的:

  • 开发一种用于基因表达分析多重测试的新型统计程序.
  • 为了单独控制表达不足和过度表达的基因的错误发现率.
  • 为了最大限度地发现真实发现,同时保持对两个方向的虚假阳性的控制.

主要方法:

  • 采用了三分类多重测试框架.
  • 开发了一个实用的,数据驱动的程序.
  • 该程序旨在控制明显表达不足和过度表达的基因的错误发现率.

主要成果:

  • 建议的程序在理论上是有效的和最佳的.
  • 它最大限度地提高了真正发现的预期数量.
  • 它同时控制了表达不足和过度表达的基因的错误发现率,在名义水平上具有灵活性.
  • 在两个大型基因组数据集上证明了有效性.

结论:

  • 开发的程序为识别定向基因表达变化提供了有效的解决方案.
  • 与现有方法相比,它可以更好地控制虚假发现.
  • 灵活性和最佳性使其成为基因组数据分析的宝贵工具.