1.Introduction to batch effects [Rmd]
- batch effects 产生的原因：measurements are affected by laboratory conditions, reagent lots 试剂批号, and personnel differences.
- 本章中将介绍：how to detect, interpret, model, and adjust for batch effects
- With data from several laboratories, we can in fact estimate the γγ, if we assume they average out to 0.
- Or we can consider them to be random effects and simply estimate a new estimate and standard error with all measurements.
2.Confounding [Rmd]
- Correlation is not causation
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Example of Simpson’s Paradox 举了一个例子，展示了不仔细剖析，混淆反应会造成的影响
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Simpson’s paradox in baseball 第二个小例子
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Confounding: High-throughput Example 第三个例子，不同种族的基因序列，由于采样年份的影响，最终的结论值得剖析。验证方法是在同一个种族的两个年份的基因差异表达，也发现了非常多的差异基因。
3.Confounding exercises
- library(dagdata) 想要成功运行代码，应该需要仔细看看这个book前面的introduction。但是鉴于时间问题，本次先不看了。
- 代码主要涉及Simpson’s Paradox例子，只是换成了hard major，详细地介绍了分析思路。
4.EDA with PCA [Rmd]
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Discovering Batch Effects with EDA 现在开始介绍如何detect batch effects
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探索性数据分析(Exploratory Data Analysis，简称EDA)
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用一个公开数据库中未经处理的数据集做例子。
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step1：加载数据；step2：发现有相关系数为1的两组数据，删除；
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Calculating the PCs 计算成分
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We have seen how PCA combined with EDA can be a powerful technique to detect and understand batches.
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In a later section, we will see how we can use the PCs as estimates in factor analysis to improve model estimates.
5.EDA with PCA exercises
- 我感觉好像就是有时候分析，要细致考虑一些影响因素，不然就会被confounding所迷惑，导致得出错误的结论。然后PCA技术可以帮助进行这样的分析。
6.Adjusting with linear models [Rmd]
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Adjusting for Batch Effects with Linear Models
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Combat is a popular method and is based on using linear models to adjust for batch effects.
7.Adjusting with linear models exercises
- 这个例子主要引起batch effect的原因还是获得sample的日期不同
8.Factor analysis [Rmd]
- 同样需要用到PCA
9.Factor analysis exercises
- 不要过度校正
10.Adjusting with factor analysis [Rmd]
11.Adjusting with factor analysis exercises