在整合和预测 (HIP) 中异质性的扩展与R闪亮的应用
Jessica Butts1, Leif Verace1, Christine Wendt2
1Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.
Statistics in medicine
|April 25, 2025
概括
我们扩展了整合和预测中的异质性 (HIP) 方法,以分析各种数据类型和子组的复杂疾病. 我们的新工具,包括R Shiny应用程序,使这种强大的方法可用于识别疾病标志物.
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.
- 计算生物学 计算生物学
背景情况:
- 整合多个数据视图可以提高对复杂疾病的理解.
- 在复杂疾病中,子组异质性 (例如,按性别,种族) 是常见的.
- 像HIP这样的现有方法可以整合数据,但有局限性.
研究的目的:
- 扩大HIP以适应多类,Poisson和零膨胀的Poisson结果.
- 开发用户友好的工具,以实现更广泛的访问性.
- 确定常见的和特定于子组的疾病标志物.
主要方法:
- 针对不同结果类型的HIP方法的扩展建议.
- 为HIP开发了一个R Shiny应用程序.
- 用一个R包来实现HIP.
主要成果:
- 成功地将HIP扩展到新的数据类型.
- 证明了HIP用于识别性别特异性疾病标志物的应用.
- 确定了与慢性阻塞性肺病 (COPD) 相关的潜在新型基因和蛋白质.
结论:
- 扩展的HIP方法和相关工具促进了多视图数据集成和子组分析.
- 用户友好的界面增加了研究人员的可访问性.
- 这种方法有助于发现疾病特异性生物标志物.
相关概念视频
01:22Interpreting R Charts
39
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
39
01:11Introduction to R
204
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
204
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:11Variability: Analysis
115
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
115
01:25Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
01:21Friedman Two-way Analysis of Variance by Ranks
95
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...
95