用综合测试对全基因组的多媒体分析,使用一般化的伯克-斯统计数据
1Institute of Statistical Science, Academia Sinica, Nankang, Taipei 11529, Taiwan.
Bioinformatics (Oxford, England)
|September 4, 2023
概括
本研究介绍了MACtest,这是一款用于基因组学多媒体分析的R包,改善了在大数据集中稀疏调解效应的识别. 它增强了在复杂的生物研究中发现因果机制的统计能力.
科学领域:
- 基因组学就是基因组学.
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 调解分析评估从通过调解者接触到结果的因果关系途径.
- 高通量技术产生大规模的基因组和蛋白质组数据,需要强大的统计方法.
- 传统的调解分析方法在复合零假设下缺乏稀疏效应的统计能力,导致错过的发现.
研究的目的:
- 为了将调解分析的复合零假设测试扩展到多媒体场景.
- 开发一个R包 (MACtest) 用于在基因组研究中进行高效和强大的多媒体分析.
- 通过吸烟引起的表观遗传异常来调节的新型基因和网络的识别.
主要方法:
- 利用了综合测试框架与综合测试框架相结合的概括的伯克-斯统计.
- 开发了三种方法:多变量 (密集/多样效应),对比 (稀疏/一致效应) 和混合.
- 实现了R包MACtest.中的方法.
主要成果:
- 该MACtest包为多媒体分析提供调整的P值,增强稀疏效应的功率.
- 对肺腺癌数据集的分析确定了由吸烟引起的表观遗传异常调节的潜在基因和网络.
- 在现实世界基因组和蛋白质组研究中证明了MACtest的实用性.
结论:
- 在高通量基因组学时代,MACtest为多媒体分析提供了一种强大而灵活的工具.
- 开发的方法提高了真正因果机制的检测,特别是在稀疏效果场景下.
- 该套件有助于研究复杂的生物学问题,例如环境因素对表观遗传的调节.
更多相关视频
08:27Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
3.7K
10:40Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
Published on: December 22, 2017
10.5K
相关概念视频
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...
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
Genome-wide Association Studies-GWAS
13.6K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
13.6K
Bonferroni Test
2.8K
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...
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Friedman Two-way Analysis of Variance by Ranks
233
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...
233
Test for Homogeneity
2.0K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.0K
Significance Testing: Overview
3.4K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
3.4K
