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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|PCA for metabolomics data

Humic acids are one of the major chemical components of humic substances, which are the major organic constituents of soil (humus), peat, coal, many upland streams, dystrophic lakes, and ocean water. They are produced by biodegradation of dead organic matter. They are not a single acid; rather they are a complex mixture of many different acids containing carboxyl and phenolate groups so that the mixture behaves functionally as a dibasic acid or, occasionally, as a tribasic acid. Humic acids can form complexes with ions that are commonly found in the environment, creating humic colloids. Humic and fulvic acids (fulvic acids are humic acids of lower molecular weight and higher oxygen content than other humic acids) are commonly used as a soil supplement in agriculture, and less commonly as a human nutritional supplement. Humic and fulvic acids are considered as soil bioindicators and reflect an equilibrium between living organic and non-organic matters.

Mass spectrometry has been used to estimate signature analytes and patterns specific to some soils (Mugo \& Bottaro, 2004). Fulvic acids were prepared from a soil using different extraction protocols resulting in 5 samples, H1, H1H2, EVM1, EVM2 and EAA. Are these extraction protocols similar and which analytes are they extracting more efficiently? MALDI MS spectra from 150 to $1500 \mathrm{~m} / \mathrm{z}$ range were recorded in the presence of the MALDI matrix alpha-cyano-4-hydroxycinammic acid (CHCA). Normalization of intensities were done with the $379 \mathrm{~m} / \mathrm{z}$ analyte in common to these samples, and Pareto scaling was chosen during the alignment process performed by MarkerView. Figure $9 \mathrm{~A}$ shows that the $\mathrm{PCA}$ analysis reveals poor separation of samples with $\mathrm{PC}{1}$ explaining $25.8 \%, \mathrm{PC}{2} 18.7 \%$ and $\mathrm{PC}_{3} 14.8 \%$. of variability (a total of $59.3 \%$ captured). Samples are not so well separated by the first $\mathrm{PC}$ axis, demonstrating the large influence of factors other than soil extraction differences (chemical precipitation, physical precipitation, filtration). Discriminant Analysis associated with PCA (supervised PCA-DA) was attempted to further separate these known 5 groups (Figure 9B). This supervised technique means that it uses class information based on the assigned sample group to improve their separation. Figure $9 \mathrm{~B}$ shows a dramatic improved separation but this may be based on noise. Peaks which are randomly more intense in one group as compared to another can possibly influence the results, and careful examination of loading plots as well as analyte profiles across the samples is necessary to avoid batch effects. This analysis is also affected by samples incorrectly assigned to wrong group and outliers.

## 统计代写|主成分分析代写Principal Component Analysis代考|What are batch effects

Batch effect is one overlooked complication with “omics” studies and occurs because highthroughput measurements are affected by multiple factors other than the primary tested biological conditions (Leek et al, 2010; Leek \& Storey, 2008). These factors are included in a comprehensive list among which are laboratory conditions, reagents batches, highly trained personnel differences, and hardware maintenance. Batch effect becomes a problem when these conditions vary during the course of an experiment, and it becomes a major problem when the various batch effects are possibly correlated with an outcome of interest and lead to incorrect conclusions (Ransohoff, 2005; Baggerly, et al., 2004). Batch effects are defined as a sub-group of measurements that have qualitatively different behaviors across conditions and are primarily unrelated to the biological or scientific variables under study. Typical batch effect is seen when all samples of a certain group are measured first, and when all samples of a second group are measured next. Batch effect occurs too when a particular batch of reagent (ex: Taq polymerase enzyme for PCR experiments) is used with all samples of the first group, and another reagent batch is used with all samples of the second group. Typical batch effects are also seen when an experimentalist/technician acquires all samples from the first group and a different experimentalist/technician works with the other group or when the instrument’s characteristics (example for MALDI mass spectrometry: laser or detector replacements) used to acquire the data have been deeply modified. Data normalization generally does not remove batch effect unless normalization takes into account the study design or takes into account the existence of a batch problem.

# 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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