Zim4rv:一种R包,用于模拟基于区域的罕见变异的零膨胀计数表型
Xiaomin Liu1, Yi-Ju Li1,2,3, Qiao Fan4
1Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
BMC bioinformatics
|January 17, 2025
概括
这项研究介绍了ZIM4rv,这是一个R包,用于用零膨胀计数数据进行罕见变异关联测试. 它扩展了包括负二项式分布的方法,增强了复杂特征的遗传分析.
科学领域:
- 遗传学 遗传学 是一个
- 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:
- 下一代测序刺激了基因基础的罕见变异关联测试的开发,主要用于二进制和连续的表型.
- 对于偏离标准分布的特征存在有限的方法,需要新的分析方法.
- 之前的工作为计数数据引入了零膨胀的Poisson (ZIP) 基于负载 (ZIP-b) 和内核 (ZIP-k) 的测试.
研究的目的:
- 扩展罕见变异关联测试方法,以适应计数数据的负二项式分布.
- 在一个用户友好的R.包中开发和实施这些扩展方法.
- 为分析零膨胀计数结果中的罕见变异提供一个集成的工作流.
主要方法:
- 介绍了ZIM4rv R包,其中包括新的负载和内核测试.
- 基于零膨胀的Poisson (ZIP-b,ZIP-k) 和负双项 (ZINB-b,ZINB-k) 的罕见变异关联测试的实施.
- 包括零和非零结果的临时两阶段分析.
主要成果:
- 该ZIM4rv包提供了一个全面的工具,用于罕见变异关联分析与零膨胀计数数据.
- 该包包括以前开发的基于ZIP的测试和新的基于负二项式的测试.
- 通过应用到来自ROSMAP队列的神经质斑点计数数据来证明它的实用性.
结论:
- R包ZIM4rv提供了一个集成的工作流程,用于对零膨胀计数数据的罕见变异关联测试.
- 该工具增强了对具有非标准计数分布的复杂特征的遗传关联分析.
- 促进在遗传研究中更广泛地应用罕见变异分析.
相关概念视频
Distributions to Estimate Population Parameter
4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
380
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
380
Statistical Methods for Analyzing Epidemiological Data
295
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
295
Assumptions of Survival Analysis
92
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
92
Truncation in Survival Analysis
151
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
151
Parametric Survival Analysis: Weibull and Exponential Methods
343
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
343


