愚人节快乐。
导入数据，设置选项
hESCs vs hiPSCs
注释文件路径
告诉软件测序数据是哪个平台的数据
测序数据路径（450K：idat文件）
选择option profile，即设置处理数据的预设值，以便分析在合理的时间内运行。
选择分析的名字
选择分析哪个区域的甲基化水平
选择identifiers.column对应列，每个identifiers.column对应一个特定的样本
选择配色方案
勾选移除性染色体上的探针
勾选bmiq归一化方法
选择需要用来比对的组别
质控
前处理
这里的归一化和methylkit中的归一化概念有所不同，这里的归一化是对一个样本中所有的位点进行归一化，而methylkit中的归一化是对覆盖度进行归一化。
同时methylkit对批次效应进行了校正。
中间文件格式
Track Hub data was generated for export to various genome browsers. Note that you need a server that is capable of serving the tracks to the genome browser via URL. Below, instructions are provided to view the tracks in the UCSC genome browser. Of course the files can also be viewed in other browsers such as the Ensembl genome browser.
Track Hub数据被生成以导出到各种基因组浏览器。
Convert the bed/bedGraph files contained in the bed/bedGraph directories (see table below) attached to this report. You can use the UCSC tool bedTobigBed/bedGraphTobigWig for this task. More information (e.g. where to obtain the tool) on how to convert can be found here. Make sure the resulting bigBed/bigWig files are moved to the corresponding UCSC track hub directory (see table(s) below).
可以使用UCSC工具将bed转换为bigbed，或者将bedGraph转为bigWig。
Bed files for each sample contain the locations of methylation sites and regions the methylation level (in the score column).
探索性分析
Dimension reduction is used to visually inspect the dataset for a strong signal in the methylation values that is related to samples' clinical or batch processing annotation. RnBeads implements two methods for dimension reduction - principal component analysis (PCA) and multidimensional scaling (MDS).
降维用于直观地检查数据集甲基化值中的强信号，该信号与样本的临床（细胞类型等）或批处理（批次效应）注释相关。RnBeads实现了两种降维方法——主成分分析(PCA)和多维标度(MDS)。
批次效应
In this section, different properties of the dataset are tested for significant associations. The properties can include sample coordinates in the principal component space, phenotype traits and intensities of control probes. The tests used to calculate a p-value given two properties depend on the essence of the data:
If both properties contain categorical data (e.g. tissue type and sample processing date), the test of choice is a two-sided Fisher's exact test.
If both properties contain numerical data (e.g. coordinates in the first principal component and age of individual), the correlation coefficient between the traits is computed. A p-value is estimated using permutation tests with 10000 permutations.
If property A is categorical and property B contains numeric data, p-value for association is calculated by comparing the values of B for the different categories in A. The test of choice is a two-sided Wilcoxon rank sum test (when A defines two categories) or a Kruskal-Wallis one-way analysis of variance (when A separates the samples into three or more categories).
Note that the p-values presented in this report are not corrected for multiple testing.
Associations between Principal Components and Traits
差异分析
In the following anlyses, p-values on the site level were computed using the limma method. I.e. hierarchical linear models from the limma package were employed and fitted using an empirical Bayes approach on derived M-values.
多层线性模型计算p值
基于衍生m值的经验贝叶斯方法进行拟合
Differential methylation on the site level was computed based on a variety of metrics. Of particular interest for the following plots and analyses are the following quantities for each site: a) the difference in mean methylation levels of the two groups being compared, b) the quotient in mean methylation and c) a statistical test (limma or t-test depending on the settings) assessing whether the methylation values in the two groups originate from distinct distributions. Additionally each site was assigned a rank based on each of these three criteria. A combined rank is computed as the maximum (i.e. worst) rank among the three ranks. The smaller the combined rank for a site, the more evidence for differential methylation it exhibits. This section includes scatterplots of the site group means as well as volcano plots of each pairwise comparison colored according to the combined ranks or p-values of a given site.
根据均值差异，均值比例以及limms统计检验的结果来对差异的甲基化位点进行排序，排序的数字越小表示这个位点越显著地差异甲基化。
我们也可以下载csv文件，里面包含：
id: site id
Chromosome: chromosome of the site
Start: start coordinate of the site
Strand: strand of the site
mean.g1,mean.g2: (where g1 and g2 is replaced by the respective group names in the table) mean methylation in each of the two groups
mean.diff: difference in methylation means between the two groups: mean.g1-mean.g2. In case of paired analysis, it is the mean of the pairwise differences.
mean.quot.log2: log2 of the quotient in methylation: log2((mean.g1+epsilon)/(mean.g2+epsilon)), where epsilon:=0.01. In case of paired analysis, it is the mean of the pairwise quotients.
diffmeth.p.val: p-value obtained from linear models employed in the limma package (or alternatively from a two-sided Welch t-test; which type of p-value is computed is specified in the differential.site.test.method option).
diffmeth.p.adj.fdr: FDR adjusted p-value of all sites
Differential methylation on the region level was computed based on a variety of metrics. Of particular interest for the following plots and analyses are the following quantities for each region: the mean difference in means across all sites in a region of the two groups being compared and the mean of quotients in mean methylation as well as a combined p-value calculated from all site p-values in the region [1]. Additionally each region was assigned a rank based on each of these three criteria. A combined rank is computed as the maximum (i.e. worst) value among the three ranks. The smaller the combined rank for a region, the more evidence for differential methylation it exhibits. Regions were defined based on the region types specified in the analysis. This section includes scatterplots of the region group means as well as volcano plots of each pairwise comparison colored according to the combined rank of a given region.
Rnbeads数据资源
看了以下Rnbeads还有针对不同甲基化数据的整合，并且也对这些数据进行了分析。具体的分析结果都可以看到。
