Although base-pair resolution DNA methylation information is obtained through most bisulfite sequencing experiments, it might be desirable to summarize methylation information over tiling windows or over a set of predefined regions (promoters, CpG islands, introns, and so on). For example, Smith et al. 9 investigated methylation profiles with RRBS experiments on gametes and zygote and summarized methylation information on 100bp tiles across the genome. Their analysis revealed a unique set of differentially methylated regions maintained in early embryo. Using tiling windows or predefined regions, such as promoters or CpG islands, is desirable when there is not enough coverage, when bases in close proximity will have similar methylation profiles, or where methylation properties of a region as a whole determines its function. In accordance with these potential analytic foci, methylKit provides functionality to do either analysis on tiling windows across the genome or predefined regions of the genome. After reading the base pair methylation information, users can summarize the methylation information on pre-defined regions they select or on tiling windows covering the genome (parameter for tiles are user provided). Then, subsequent analyses, such as clustering or differential methylation analysis, can be carried out with the same functions that are used for base pair resolution analysis.

We demonstrated the capabilities of methylKit using an example data set from seven breast cancer cell lines from Sun et al. 23 . Four of the cell lines express estrogen receptor-alpha (MCF7, T47D, BT474, ZR75-1), and from here on are referred to as ER+. The other three cell lines (BT20, MDA-MB-231, MDA-MB-468) do not express estrogen receptor-alpha, and from here on are referred to as ER-. It has been previously shown that ER+ and ER- tumor samples have divergent gene expression profiles and that those profiles are associated with disease outcome 24 25 . Methylation profiles of these cell lines were measured using reduced RRBS 10 . The R objects contained the methylation information for breast cancer cell lines and functions that produce plots and other results that are shown in the remainder of this manuscript are in Additional file 4.

Read coverage per base and % methylation per base are the basic information contained in the methylKit data structures. methylKit has functions for easy visualization of such information (Figure 2a and 2b for % methylation and read coverage distributions, respectively - for code see Additional file 4). In normal cells, % methylation will have a bimodal distribution, which denotes that the majority of bases have either high or low methylation. The read coverage distribution is also an important metric that will help reveal if experiments suffer from PCR duplication bias (clonal reads). If such bias occurs, some reads will be asymmetrically amplified and this will impair accurate determination of % methylation scores for those regions. If there is a high degree of PCR duplication bias, read coverage distribution will have a secondary peak on the right side. To correct for this issue, methylKit has the option to filter bases with very high read coverage.

We have also included methods to assess sample similarity. Users can calculate pairwise correlation coefficients (Pearson, Kendall or Spearman) between the %methylation profiles across all samples. However, to ensure comparable statistics, a new data structure is formed before these calculations, wherein only cytosines covered in all samples are stored. Subsequently, pairwise correlations are calculated, to produce a correlation matrix. This matrix allows the user to easily compare correlation coefficients between pairs of samples and can also be used to perform hierarchical clustering using 1- correlation distance. methylKit can also further visualize similarities between all pairs of samples by creating scatterplots of the %methylation scores (Figure 3). These functions are essential for detecting sample outliers or for functional clustering of samples based on their molecular signatures.

methylKit can also be used to cluster samples hierarchically in a variety of ways. The user can specify the distance metric between samples ('1 - correlation' 'Euclidean', 'maximum', 'manhattan', 'canberra', 'binary' or 'minkowski') as well as the agglomeration method to be used in the hierarchical clustering algorithm (for example, 'Ward's method', or 'single/complete linkage', and so on). Results can either be returned as a dendrogram object or a plot. Dendrogram plots will be color coded based on user defined groupings of samples. For example, we found that most ER+ and ER- samples clustered together except MDMB231 (Figure 4a). Moreover, the user may be interested in employing other more model-intensive clustering algorithms to their data. Users can easily obtain the %methylation data from methylKit object and perform their own analysis with the multitude of R-packages already available for clustering. An example of such a procedure (k-means clustering) is shown in Additional file 4.

methylKit can be used to perform Principal Component Analysis (PCA) on the samples' %-methylation profiles (see for example 26 ). PCA can reduce the high dimensionality of a data set by transforming the large number of regions to a few principal components. The principal components are ordered so that the first few retain most of the variation present in the original data and are often used to emphasize grouping structure in the data. For example, a plot of the first two or three principal components could potentially reveal a biologically meaningful clustering of the samples. Before the PCA is performed, a new data matrix is formed, containing the samples and only those cytosines that are covered in all samples. After PCA, methylKit then returns to the user a 'prcomp' object, which can be used to extract and plot the principal components. We found that in the breast cancer data set, PCA reveals a similar clustering to the hierarchical clustering where MDMB231 is an outlier.

Differential methylation patterns have been previously described in malignancies 27 28 29 and can be used to differentiate cancer and normal cells 30 . In addition, normal human tissues harbor unique DNA methylation profiles 7 . Differential DNA methylation is usually calculated by comparing methylation levels between multiple conditions, which can reveal important locations of divergent changes between a test and a control set. We have designed methylKit to implement two main methods for determining differential methylation across all regions: logistic regression and Fisher's exact test. However, the data frames in methylKit can easily be used with other statistical tests and an example is shown in Additional file 4 (using a moderated t-test, although we maintain that most natural tests for this kind of data are Fisher's exact and logistic regression based tests). For our example data set we compared ER+ to ER- samples, with our 'control group' being the ER- set.

In logistic regression, information from each sample is specified (the number of methylated Cs and number of unmethylated Cs at a given region), and a logistic regression test will be applied to compare fraction of methylated Cs across the test and the control groups. More specifically, at a given base/region we model the methylation proportion P_i, for sample i= 1,...,n (where n is the number of biological samples) through the logistic regression model:

log ( P i /(1 -  P i )) =  β 0 + β 1 * T i

where T_i denotes the treatment indicator for sample i, T_i = 1 if sample i is in the treatment group and T_i = 0 if sample i is in control group. The parameter β₀ denotes the log odds of the control group and β₁ the log oddsratio between the treatment and control group. Therefore, independent tests for all the bases/regions of interest are against the null hypothesis H₀: β₁= 0. If the null hypothesis is rejected it implies that the logodds (and hence the methylation proportions) are different between the treatment and the control group and the base/region would subsequently be classified as a differentially methylated cytosine (DMC) or region (DMR). However, if the null hypothesis is not rejected it implies no statistically significant difference in methylation between the two groups. One important consideration in logistic regression is the sample size and in many biological experiments the number of biological samples in each group can be quite small. However, it is important to keep in mind that the relevant sample sizes in logistic regression are not merely the number of biological samples but rather the total read coverages summed over all samples in each group separately. For our example dataset, we used bases with at least 10 reads coverage for each biological sample and we advise (at least) the same for other users to improve power to detect DMCs/DMRs.

In addition, we have designed methylKit such that the logistic regression framework can be generalized to handle more than two experimental groups or data types. In such a case, the inclusion of additional treatment indicators is analogous to multiple regression when there are categorical variables with multiple groups. Additional covariates can be incorporated into model (1) by adding to the right side of the model:

α 1 * Covariat e 1,i + . . . + α K * Covariat e K,i

where Covariate_1,i, ..., Covariate_K,i denote K measured covariates (continuous or categorical) for sample i = 1,...,n and α₁,..., α_k denote the corresponding parameters.

The Fisher's exact test compares the fraction of methylated Cs in test and control samples in the absence of replicates. The main advantage of logistic regression over Fisher's exact test is that it allows for the inclusion of sample specific covariates (continuous or categorical) and the ability to adjust for confounding variables. In practice, the number of samples per group will determine which of the two methods will be used (logistic regression or Fisher's exact test). If there are multiple samples per group, methylKit will employ the logistic regression test. Otherwise, when there is one sample per group, Fisher's exact test will be used.

Following the differential methylation test and calculation of P-values, methylKit will use the sliding linear model (SLIM) method to correct P-values to q-values 31 , which corrects for the problem of multiple hypothesis testing 32 33 . However, we also implemented the standard false discovery rate (FDR)-based method (Benjamini-Hochberg) as an option for P-value correction, which is faster but more conservative. Finally, methylKit can use multi-threading so that differential methylation calculations can be parallelized over multiple cores and be completed faster.

We have designed methylKit to allow a user to specify the parameters that define the DMCs/DMRs based on: q-value, %methylation difference, and type of differential methylation (hypo-/hyper-). By default, it will extract bases/regions with a q-value <0.01 and %methylation difference >25%. These defaults can easily be changed when calling get.methylDiff() function. In addition, users can specify if they want hyper-methylated bases/regions (bases/regions with higher methylation compared to control samples) or hypo-methylated bases/regions (bases/regions with lower methylation compared to control samples). In the literature, hyper- or hypo-methylated DMCs/DMRs are usually defined relative to a control group. In our examples, and in methylKit in general, a control group is defined when creating the objects through supplied treatment vector, and hyper-/hypomethylation definitions are based on that control group.

Furthermore, DMCs/DMRs can be visualized as horizontal barplots showing percentage of hyper- and hypo-methylated bases/regions out of covered cytosines over all chromosomes (Figure 5a). We observed higher levels of hypomethylation than hypermethylation in the breast cancer cell lines, which indicates that ER+ cells have lower levels of methylation. Since another common way to visualize differential methylation events is with a genome browser, methylKit can output bedgraph tracks (Figure 5b) for use with the UCSC Genome Browser or Integrated Genome Viewer.

To discern the biological impact of differential methylation events, each event must be put into its genomic context for subsequent analysis. Indeed, Hansen et al. 34 showed that most variable regions in terms of methylation in the human genome are CpG island shores, rather than CpG islands themselves. Thus, it is interesting to know the location of differential methylation events with regard to CpG islands, their shores, and also the proximity to the nearest transcription start site (TSS) and gene components. Accordingly, methylKit can annotate differential methylation events with regard to the nearest TSS (Figure 6a) and it also can annotate regions based on their overlap with CpG islands/shores and regions within genes (Figures 6b and 6c are output from methylKit).

As with most genome-wide assays, the regions of interest for DNA methylation analysis may be quite numerous. For example, several reports show that Alu elements are aberrantly methylated in cancers 35 36 and enhancers are also differentially methylated 37 38 . Since users may need to focus on specific genomic regions and require customized annotation for capturing differential DNA methylation events, methylKit can annotate differential methylation events using user-supplied regions. As an example, we identified differentially methylated bases of ER+ and ER- cells that overlap with ENCODE enhancer regions 39 , and we found a large proportion of differentially methylated CpGs overlapping with the enhancer marks, and then plotted them with methylKit (Figure 6d).