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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|主成分分析代写Principal Component Analysis代考|How to find evidence of batch effects

The first step in addressing batch and other technical effects is to develop a thorough and meticulous study plan. Studies with experiments that run over long periods of time, and large-scale, inter-laboratory experiments, are highly susceptible to batch effects. Intralaboratory experiments spanning several days and several personnel changes are also susceptible to batch effects. Steps necessary to analyze batch effects require different. levels of analysis, according to the recent review of Leek J.T (Leek et al., 2010). What follows are some of the recommended actions:. Performing a hierarchical clustering of samples that assigns a label to the biological variables and to the batch surrogates estimates, such as laboratory and processing time; plotting individual features (gene expression, peptides or metabolites abundances). versus biological variables and batch surrogates using ggobi for example; calculating principal components of the high-throughput data and identifying components that correlate with batch surrogates. If some batch effects are present in the data, artifacts must be estimated directly, using surrogate variable analysis (SVA) (Leek et al., 2007). Recently, the EigenMS algorithm has been developed and. implemented within a pipeline of bioinformatic tools of DanteR in order to correct for technical batch effects in MS proteomics data analysis (Polpitya et al,, 2008; Karpievitch et al., 2009). The algorithm uses an SVA approach to estimate systematic residual errors using singular value decomposition taking account primary biological factors and substracting those estimates from raw data in the pre-processing data analysis. The estimated/surrogate variables should be treated as standard covariates in subsequent analyses or adjusted for use with tools such as Combat (Johnson \& Li, 2007). After adjustments that. include surrogate variables (at least processing time and date), the data must be reclustered to ensure that the clusters are not still driven by batch effects.

## 统计代写|主成分分析代写Principal Component Analysis代考|How to avoid batch effects

Measures and steps must be taken to minimize the probability of confusion between biological and batch effects. High-throughput experiments should be designed to distribute batches and other potential sources of experimental variation across biological groups. PCA of the high-throughput data allows the identification of components that correlate with batch surrogate variables.

Another approach to avoid and prevent batch effects is to record all parameters that are important for the acquisition of the measures and the relevant information related to demographic and grouping factors. The structure of a database under MySQL with attractive web graphic user interface (GUI) should be conceived at the same time as the study design is defined. Such a database was constructed for a mass spectrometry based biomarker discovery project in kidney transplantation in a French national multicenter project. The BiomarkerMSdb database structure contains 6 linked tables: Demographic data, Peptide Extraction Step, Liquid Chromatography Separation, Probot Fractionation, Spectrometry Acquisition and Data Processing. Figure 10 shows the details of the form that the user must complete to record demographic data of patients enrolled in this project. This approach, with an internet interface used to facilitate data exchange between laboratories enrolled in the project, allows to keep track of essential parameters that could interfere with future interpretations of the results. At minimum, analyses should report the processing group, the analysis time of all samples in the study, the personnel involved, along with the biological variables of interest, so that the results can be verified independently. This is called data traceability.

# 主成分分析代考

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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