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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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 transcriptomics data

Gene expression array technology has reached the stage of being routinely used to study clinical samples in search of diagnostic and prognostic biomarkers. Due to the nature of array experiments, the number of “null-hypotheses” to test, one for each gene, can be huge (a few tens of thousands). Multiple testing corrections are often necessary in order to. screen non-informative genes and reduce the number of null-hypotheses. One of the commonly used methods for multiple testing control is to calculate the false discovery rate (FDR) which is the ratio of the number of false rejections among the total number of rejections. FDR adjustment on raw $\mathrm{p}$-values is effective in controlling false positives but is known to reduce the ability to detect true differentially expressed genes.

In transcriptomics studies, $\mathrm{PCA}$ is often used for the location of genes relative to each other in a reduced experiment space. Genes are plotted with respect to the two orthogonal linear combinations of experiments that contain the most variance (Lu et al., 2011). Transcriptomics also use other multivariate tools for classification and clustering (Tibshirani et al., 2002). A very fast and effective classification strategy is linear discriminant analysis. In classification problems there are positive training examples that are known members of the class under. study and negative training examples that are examples known not to be members of the class. The test examples are compared to both sets of training examples, and the determination of which set is most similar to the test case is established. In this process the test example is “classified” based on training examples. Clustering is a commonly used categorizing technique in many scientific areas using K-means grouping technique. Using this approach the user can cluster data based on some specified metric into a given number of clusters. Users can cluster arrays or genes as desired into a pre-specified number of clusters. The algorithm has a randomized starting point so results may vary from run to run.

## 统计代写|主成分分析代写Principal Component Analysis代考|Urinary peptides and biomarker discovery study

PCA was used in order to distinguish urine samples containing or not pseudo or artificial spiked-in analytes or pseudo biomarkers (Benkali et al., 2008). The objectives were to analyze variations in the data and distinguish their sources. These variations could arise from (a) experimental variations due to changes in the instrument or experimental conditions, (b) real variations but of no interest in the primary objective, such as male versus female subjects, drug treatments, metabolites of a therapeutic agent… and (c) relevant differences that reflect changes in the system under study (spiked-in or not spiked-in). The experiment consisted in using human urines from 20 healthy volunteers splitted in two groups of ten, one which was spiked-in with few synthetic peptides at a certain variable concentration and the other without. Urines were processed using the same peptide extraction solid phase extraction (SPE) protocol, by the same experimentalist, and peptide compositions were recorded by off-line nanoLC-MS MALDI TOF/TOF. Data were processed with MarkerView software version $1.2$ (www.absciex.com). PCA preprocessing consisted in using Pareto scaling without weighing and no autoscaling because Pareto scaling is known to reduce but not completely eliminate the significance of intensity, which is appropriate for MS because larger peaks are generally more reliable and all variables are equivalent. Different scaling methods are worth trying because they can reveal different features in the data with peak finding options and Pareto normalization (Van der Berg et al., 2006).

# 主成分分析代考

## 统计代写|主成分分析代写Principal Component Analysis代考|Urinary peptides and biomarker discovery study

PCA 用于区分是否含有假或人工掺入分析物或假生物标志物的尿样（Benkali 等人，2008 年）。目标是分析数据的变化并区分它们的来源。这些变化可能来自（a）由于仪器或实验条件的变化而导致的实验变化，（b）实际变化但对主要目标不感兴趣，例如男性与女性受试者、药物治疗、治疗剂的代谢物…… (c) 反映研究中系统变化的相关差异（加标或未加标）。该实验包括使用来自 20 名健康志愿者的人类尿液，他们分成两组，每组 10 人，一组以特定的可变浓度掺入少量合成肽，另一组则不掺入。使用相同的肽提取固相萃取 (SPE) 方案，由相同的实验人员处理尿液，并通过离线 nanoLC-MS MALDI TOF/TOF 记录肽成分。使用 MarkerView 软件版本处理数据1.2(www.absciex.com)。PCA 预处理包括使用 Pareto 标度，无需称重，也无需自动标度，因为已知 Pareto 标度可降低但不能完全消除强度的重要性，这适用于 MS，因为较大的峰通常更可靠并且所有变量都是等效的。不同的缩放方法值得尝试，因为它们可以通过峰值查找选项和 Pareto 归一化来揭示数据中的不同特征（Van der Berg 等人，2006）。

## 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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