The Chip Analysis Methylation Pipeline

Yuan Tian, Tiffany J Morris, Amy P Webster, Zhen Yang, Stephan Beck, Andrew Feber, and Andrew E Teschendorff

2018-05-10

1 New Updates

ChAMP paper has been published on Bioconductor! As author of ChAMP, we really appriciate all your help and suggestions, in the future, I will continually make this package better and better.

If you have any bugs to report, or any suggestions for ChAMP, please email champ450k@gmail.com or tianyuan1991hit@gmail.com.

2 Introduction

The ChAMP package is designed for the analysis of Illumina Methylation beadarray data (EPIC and 450k) and provides a pipeline that integrates currently available 450k and EPIC analysis methods. This includes a variety of different data import methods (e.g. from .idat files or a beta-valued matrix) and Quality Control plots. Type-2 probe correction methods include SWAN1, Peak Based Correction (PBC)2 and BMIQ3 (the default choice). The popular Functional Normalization4 function offered by the minfi56 package is also available. The singular value decomposition (SVD) method7 allows an in-depth look at batch effects, and for correction of multiple batch effects the ComBat method8 has been implemented. Adjusting for cell-type heterogeneity can be done via RefbaseEWAS9. ChAMP also includes a function for infering copy-number alterations from either 450k or EPIC data10. For the identification of Differentially Methylated Regions (DMR), ChAMP offers the new Probe Lasso method11, in addition to previous DMR detection functions Bumphunter12 and DMRcate13. For users who need to find Differentially Methylated Blocks14, the new version of ChAMP includes a function to detect these. Gene Set Enrichment Analysis (GSEA) is also possible and the new version of ChAMP incorporates methods that correct for the bias caused by unequal representation of probes among genes15. Also, the new version of ChAMP incorporates the FEM package16, which can infer gene modules in user-specified gene-networks that exhibit differential methylation between phenotypes.

While a number of other pipelines and packages for 450k or EPIC array analysis are available (including IMA17, minfi5, methylumi18, RnBeads19 and wateRmelon20 ), ChAMP provides a more comprehensive and complete analysis pipeline from reading original data files to final tertiary analysis results, such as GSEA, which streamlines methylation array analysis for researchers. The new version ChAMP also provides a series of Shiny and Plotly-based WebBrower Interactive analysis functions (GUI functions), to help scientists view ChAMP results. This requires an interactive WebBrower-based framework, for calling Graph System locally or remotely. For example, X11 for most Linux Systems.

3 Installation

It is essential that you have R 3.3 or above already installed on your computer or server. ChAMP is a pipeline that utilises many other Bioconductor packages that are currently available from CRAN and Bioconductor. For all of the steps of the pipeline to work, make sure that you have upgraded Bioconductor to newest version (3.5).

After you have R and Bioconductor installed properly, you can start to install ChAMP. The easiest way to install it is by typing following code into your R session:

If you have any problems with Bioconductor, another option is to install ALL dependent packages, then install ChAMP. There are a lot if packages relied on by ChAMP. You can use the following command in R to install most of them:

When you are installing ChAMP, you may encounter some errors saying that some packages are not installed. These errors are caused by recursively depending on R packages, for example, ChAMP uses minfi, minfi uses lumi, and lumi uses other packages, so if lumi was not installed on your computer, ChAMP would fail. To solve these errors, you simply need to check those error messages, find out which packages required are missing, then install it with command biocLite("YourErrorPackage") directly. Then retry installing ChAMP, it may take 3-4 times, but eventually it should work.

If you are using ChAMP version above 2.8.3, note that you MUST install latest ChAMPdata package(version 2.8.1) to make new ChAMP works.

After installation, you should be able to load the ChAMP package in your R session:

4 Test Data

The package contains two test datasets, one is HumanMethylation450 data (.idat) and the other is simulated EPIC data, which can be used to test functions available in ChAMP. This can be loaded by pointing the directory to the testDataSet like below:

The 450k lung tumor data set contains only 8 samples, 4 lung tumor samples (T) and 4 control samples (C). We use this data set to show the functionality of ChAMP.

For the EPIC Simulation Data Set, user may use following code to load it:

This Simulation Data Set contains 16 samples, all of them are actually originally from one sample but modified into DMP and DMR, also alone with some errors variance. In this 16 sample dataset, we simulated 8 samples are control and 8 sample as case, samples are marked in pd of EPICSimData object. In this data, we randomly choose 5000 regions from clusterMaker() function of bumphunter package. In each region, we randomly selected some continually CpGs as DMR, then increased or decreased beta value of this DMR. So there should have less than 5000 DMRs (4700+) in this data set, because some simulated DMRs contains only 1-2 CpGs, they will not be regarded as DMR in champ.DMR() function. Users may use this data to test the functionality of ChAMP on EPIC data.

5 ChAMP Pipeline

5.1 Pipeline Introduction

ChAMP Pipeline Figure above outlines the steps in the ChAMP pipeline. Each step can be run individually as a separate function. This allows integration between ChAMP’s result and other analysis pipelines. In above pipeline plot, all functions of ChAMP are included, which are categorized using three colors:

  • Blue functions represent functions for Methylation Data Preparation, like Loading, Normalization, Quality Control checks etc.
  • Red functions represent functions for generating analysis results, like Differentially Methylated Positions (DMPs), Differentially Methylated Regions (DMRs), Differentially methylated Blocks, EpiMod (a method for detecting differentially methylated gene modules derived from FEM package), Pathway Enrichment Results etc.
  • Yellow functions represent GUI interface for dataset and analysis results.

Solid gray line in above plot represent stream of pipeline, while dash gray line represent functions may not necessarily be used, which depends on your data and projects.

In the above plot, there are some functions and connections (lines) marked with green gleam, which indicate the Main Analysis Pipeline. ChAMP combines many functions, but not all of them may be used for a successful analysis. The green gleam represents a main analysis pipeline most likely to be used for various data sets. We also marked steps for these main pipeline steps, which should be helpful for new users. The black dot in the middle of the plot represents fully prepared methylation datasets, which is the milestone between data preparation and data analysis. Thus, we suggest you save the fully prepared data since your other analyses may be started from it directly.

5.2 Full Pipeline

The full pipeline can be run at once with one command:

When running the full pipeline through the champ.process() function a number of parameters can be adjusted. These parameters are related to all functions involved in the ChAMP package, but not all parameters are covered, otherwise champ.process() function would be too large; please check the manual of ChAMP for parameter settings.

5.3 Separated Steps

We note that champ.process() may not always run successfully, which is likely due to particularities of specific data sets . For example, the covariate of interest in the pd file may not be denoted “Sample_Group” but another category, for example, “Disease”, but in the ChAMP function, “Sample_Group” is set as default as the variable of interest. If you encounter any problems when using champ.process(), we recommend you apply it in a step-by-step fashion:

We note that the above implementation of the ChAMP functions uses default parameter specifications, and that therefore parameters can be adjusted and integrated with users’ other unique analysis tools and methods. For details of parameter options, please check the relevant sections of this vignette, or the ChAMP manual.

5.5 Computational Requirements

The ability to run the pipeline on a large number of samples depends somewhat on the memory available. The ChAMP pipeline runs 200 samples successfully on a computer with 8GB of memory. Beyond this it may be necessary to find a server/cluster to run the analysis on.

The champ.load() function uses the most memory. If you plan to run the analysis more than once it is recommended to run myLoad <- champ.load() and save this list for future analyses.

In champ.DMR(), champ.norm() and champ.Block() functions, some methods may use parallel method to accelerate the process. If your server or computer has more cores, you may specify more cores at the same time to make the function run faster, though this may cost more memory. You can use the following code in R to detect number of cores.

GUI functions requires more things. A basic framework for a GUI environment is required. If you run ChAMP on your own computer, it should be fine. But if you run ChAMP on a remote server, a local display system is essential to use these WebBrower-based GUI functions. X11 is a very common used tool for this. For Windows users, Xmanager or MobaxTerm are excellent choices.

A significant improvement for ChAMP is that users can also conduct full analysis even if they are not starting from raw .idat files. As long as you have a methylation beta matrix and the corresponding phenotypes (pd) file, you can conduct nearly all of the ChAMP analysis. This makes analysis easier for users who have beta-values only but not original idat files, for example if users obtain data from TCGA or GEO.

6 Description of ChAMP Pipelines

As previously mentioned, users have the option to run each of the ChAMP functions individually to allow integration with other pipelines or to save the results at each step. Below we describe each function in further detail.

6.1 Loading Data

Loading data is always the first step. ChAMP provides a loading function to get data from .idat files (with pd file (Sample_Sheet.csv) in it). The old champ.load() function utilises minfi to load data from .idat files, but now we provide a new method “ChAMP” to read data. The two loading methods effectively return the same objects, except that the old version would also return rgSet and mset mset objects, which might be used as input to SWAN and FunctionalNormliazation method in champ.norm(). By default this loads data from the current working directory, in this directory you should have all .idat files and a sample sheet. The sample sheet currently needs to be a .csv file as came with your results following hybridization. The champ.load() function used to load the data from the idat files automatically filters out the SNPs that might influence the analysis result. The SNP list was generated from Zhou’s Nucleic Acids Research paper in 201621, which is designed specifically for 450K and EPIC, also with population differences considered. As such, for 450k bead array data, before filtering for low quality probes, the dataset will include 485,512 probes. And for EPIC bead array data, before filtering probes the dataset, will include 867,531 probes.

Currently the default method for loading is “ChAMP”. You may set “method” in champ.load() to use classical minfi way. Note that “ChAMP” method in champ.load() is merely a combination of champ.import() and champ.filter(). If you are not satisfied with result returned by champ.load(), say you want to get beadcount, detection P matrix, or Meth UnMeth matrix, you may use champ.import() to generate them.

User may invoke following code to load data set:

Alternatively, the user can load the data from the dataset object testDataSet in ChAMPdata package, using:

It is important to check the pd file (Sample_Sheet.csv). Most pd files are similar in format, but their most valuable information are not always stored in same column, for example, for most pd files, Sample_Group means groups of phenotype wants to be researched, but in some pd files, similar information may be stored in columns named as “Diagnose” or “CancerType”, so the user MUST understand their pd file well to achieve a successful analysis.

##    Sample_Name Sample_Plate Sample_Group Pool_ID Project Sample_Well
## C1          C1           NA            C      NA      NA         E09
## C2          C2           NA            C      NA      NA         G09
## C3          C3           NA            C      NA      NA         E02
## C4          C4           NA            C      NA      NA         F02
## T1          T1           NA            T      NA      NA         B09
## T2          T2           NA            T      NA      NA         C09
## T3          T3           NA            T      NA      NA         E08
## T4          T4           NA            T      NA      NA         C09
##     Array      Slide                   Basename                  filenames
## C1 R03C02 7990895118 Test450K/7990895118_R03C02 Test450K/7990895118_R03C02
## C2 R05C02 7990895118 Test450K/7990895118_R05C02 Test450K/7990895118_R05C02
## C3 R01C01 9247377086 Test450K/9247377086_R01C01 Test450K/9247377086_R01C01
## C4 R02C01 9247377086 Test450K/9247377086_R02C01 Test450K/9247377086_R02C01
## T1 R06C01 7766130112 Test450K/7766130112_R06C01 Test450K/7766130112_R06C01
## T2 R01C02 7766130112 Test450K/7766130112_R01C02 Test450K/7766130112_R01C02
## T3 R01C01 7990895118 Test450K/7990895118_R01C01 Test450K/7990895118_R01C01
## T4 R01C02 7990895118 Test450K/7990895118_R01C02 Test450K/7990895118_R01C02

As we can see here, the Sample_Group contains two phenotypes: C and T, which is what we want to compare. In this data set, C means “Control”, while T means “Tumor”.

6.2 Filtering Data

ChAMP provides a comprehensive filtering function called champ.filter() which can take any data matrix as input (beta, M, Meth, UnMeth, intensity) and do filtering on it. champ.filter() does not call for any specific object or class result, nor does it need an IDAT file, as long as you have a single beta matrix, the filtering can be done. In champ.filter(), since some probes and samples might be removed for reason of low quality, NA might be still exist in filtered data. So, we provided a parameter autoimpute in the function to conduct imputation on remain probes, if NAs are still existing. Actually, imputation work is done by champ.impute() function, reader may check that function for more information.

For some filtering methods, additional data must be provided, for example if you want to perform filtering using detection P-values, you need to provide a detection P-value matrix. Also, if you want to perform filtering based on beadcounts, you need to provide the beadcount information. If you read IDAT file with champ.load() function’s default method “ChAMP”, beadcount information should be included in returned result. Also champ.filter() is designed to do filtering on multiple types of data frames from same data set, say you have beta matrix, M matrix, intensity matrix from same .idat file, you may use champ.filter() to do filtering on them at the same time, which would keep their compliance in future analysis.

  • First, you must input at least one data matrix.
  • Second, if you provide as input multiple matrices, they must have EXACTLY the same rownames and colnames. For example, if you want to do filtering on intensity data and M value at the same time, you MUST make sure they have the same name, otherwise, ChAMP will assume they are data frames from different sources. Then, champ.filter() would stop filtering process.
  • Since low quality samples (e.g. those with many undetected probes) may be removed during the process of filtering, it is important to ensure that the Sample_Name identifiers in your pd file are exactly the same as colnames of your data matrix
  • Fourth, if you want to do imputation, you must provide the detection P matrix, beta or M matrix, and the parameter ProbeCutoff can not be 0, this parameter controls threshold of NA ratio that a probe should be removed or not. If any of these three conditions fail, champ.filter() would reset autoimpute parameter as FALSE, then imputation process would not be done.
  • Fifth, if you want to filter by beadcount, you must provide the beadcount, champ.import() would return beads information. Or, you can use beadcount function from wateRmelon package to extract is from rgSet object from “minfi” method..

The champ.filter() function is the after process of champ.import() which is a novel loading function coded by ChAMP team. You may use them like below:

Then the result of champ.filter() (myLoad in above code), should be the same result as old minfi method.

Actually During the loading process, champ.load() function would also do some filtering. Below are filtering steps which would be performed during both champ.load() and champ.filter().

  • First filter is for probes with detection p-value (< 0.01). This utilises detection p-value stored in .idat file. champ.import() would read these information and form them into data frame. For each probe, if it’s p value is less then 0.01, it would be considered as failed probe. A failedSamples result will be printed to the screen, showing the fraction of failed probes per sample. If any of these values is high (>0.1) you may want to consider removing that sample from the analysis and rerun it. Previously we found that in many cases, only one or two samples in a big data set are not qualifed for analysis, and they may have 70% or even 80% of failed probes. So if we only do filtering on probes, about 80% of probes will be masked, so we developed a new filtering system on both sample and probe quality. If a certain sample’s failed probes’ ratio is above a particular threshold (default = 0.1), then this sample will be discarded, and filtering of probes will be carried on the remaining samples. The threshold of sample and probe can be controlled by the parameter SampleCutoff and ProbeCutoff separately.

  • Second, ChAMP will filter out probes with <3 beads in at least 5% of samples per probe. This default can be changed with the filterBeads parameter or the frequency can be adjusted with the beadCutoff parameter.

  • Third, ChAMP will by default filter out all non-CpG probes contained in your dataset.

  • Fourth, by default ChAMP will filter all SNP-related probes. The SNP list comes from Zhou’s Nucleic Acids Research paper in 2016. Note that if you know which population your data is from, you can choose certain populations to do the filtering. Otherwise ChAMP would use General Recommended Probes provided by Zhou to do filtering. You just need to assign “population” parameter to achieve this.

  • Fifth, by default setting, ChAMP will filter all multi-hit probes. The multi-hit probe list comes from Nordlund’s Genome Biology Paper in 201322.

  • Sixth, ChAMP will filter out all probes located in chromosome X and Y. This is also a default setting, but user can change it with filterXY parameter.

All above filterings are controlled by parameters in the champ.load() and champ.filter() function, and users may adjust them as they wish. Note that, though most ChAMP functions are supporting isolated beta matrix analysis, which means not relying on .idat files from the beginning, champ.load() can not perform filtering on beta matrix alone. For users have no .IDAT data but beta matrix and Sample_Sheet.csv, you may want perform filtering using the champ.filter() function and then use following functions to do analysis.

In addition to the filtering capability, we also provide a function CpG.GUI() for the user to check their distribution of CpGs on chromosome, CpG island, TSS reagions. e.g. This function can be used for any CpGs list, for example, during your analysis, whenever you get a significant CpG list from DMR, you can use the following function to check the distribution of your CpG vector:

This is a useful function to demonstrate the distribution of your CpG list. If you get a DMP list, you may use this function to check your DMP’s distribution on chromosome.

6.3 Further quality control and exploratory analysis

Quality Control is an important process to make sure dataset is suitable for downstream analysis. champ.QC() function and QC.GUI() function would draw some plots for user to easily check their data’s quality. Usually, there are three plots that would be generated:

  • mdsPlot (Multidimensional Scaling Plot): This plot allows a visualization of the similarity of samples based on the top 1000 most variable probes amongst all samples. Samples are colored by Sample Groups in this plot.
  • densityPlot: The beta distributions for each sample; users may use this figure to find samples which deviate significantly from others and which may not be of good quality (e.g. incomplete bisulfite conversion). NB: It also serves to identify and confirm methylated or unmethylated control samples, if these were included in the study).
  • dendrogram: The clustering plot for all samples, you may select different method to generate this plot. There is a parameter Feature.sel="None" in champ.QC() function. While “None” means the distance between samples would be calculated directly by all probes (your server may crash if you directly do this on a large data set), “SVD” means champ.QC() function would use SVD method to do deconvolute on beta matrix, then select significant components based only EstDimRMT() method from “isva” package. Then the distance between samples would be calculated based on these components.

Or you may use QC.GUI() function as below, which however may be memory intensive, so make sure you have a good server or computer when you run this GUI function in ChAMP.

In contrast to champ.QC(), QC.GUI() will provide five interactive plot. These plots are as follows; mdsPlot, type-I&II densityPlot, sample beta distribution plot, dendrogram plot and top 1000 most variable CpG’s heatmap. Users may easily interact with these plots to see if your samples are qualified for further analysis, and check clustering result and top CpGs.

  • Probe-Type-I&II plot: the beta distributions of Type-I and Type-II probes in your dataset, which could help you to check your data set’s normalization status.
  • Top variable CpGs’ heatmap: This heatmap would select most variable CpGs, and create a heatmap based on the methylation values at these sites.

6.4 Normalization

On Illumina beadarrays, probes come in two different designs (called type-I and type-II), with different hybridization chemistries, which means that probes from these two different designs will exhibit different distributions. This is a technical effect and is independent of variations caused by differences in the biological characteristics (e.g. CpG density) of type-I and type-II probes. The most marked difference between the type-I and type-II methylation distributions is that the type-II distribution exhibit a reduced dynamic range. In supervised analyses, this could cause a bias in the selection of type-I over type-II probes. For DMR detection, where type-I and type-II probes may fall in the same regions, it is clear that adjustment is paramount. In general, adjustment for the type-II probe bias is recommended, and you can use the champ.norm() function to perform this normalization.

In champ.norm(), we provide four methods to do this type-II probe normalization: BMIQ3, SWAN1, PBC2 and FunctionalNormliazation4. There are some key differences between each method, and users may read related papers to select the most appropriate one for their analysis.

Currently, FunctionalNormalization requires rgSet, SWAN requires mset and rgSet objects generated from IDAT files (as described in the minfi package description) while FunctionalNormliazation requires an rgSet object. Consequently, FunctionalNormliazation and SWAN normalization methods only works for analyses for “minfi” loading method.

Note that the BMIQ function has been updated to version 1.6, which should provide better normalization for EPIC array data. We note that BMIQ may not converge and provide output for certain samples, and this may happen if the methylation distribution of the sample deviates markedly from a 3-state beta-mixture distribution (as it may happen for methylated/unmethylated controls), or because of poor sample quality. A new feature is that the BMIQ function can now be run in parallel, so if your computer has more than one core, you may set parameter “cores” in function to accelerate the function. If you set parameter PDFplot=TRUE, the plot for BMIQ function would be saved in resultsDir.

The code to do Normalization is below:

After Normalization, you may use the QC.GUI() function to check the result again; remember to set the beta parameter in QC.GUI() function as “myNorm”.

6.5 SVD Plot

The singular value decomposition method (SVD) implemented by Teschendorff7 for methylation data is a powerful tool for assessing the number and nature of the significant components of variation in a dataset. These components of variation would ideally correlate with biological factors of interest, but generally also correlate with technical sources of variation (e.g. batch effects). We strongly advise users to collect as much information as possible of the samples being analysed (e.g. dates of hybridization, season in which samples were collected, epidemiological information, etc etc) and to include all of these factors when correlating to SVD components. If samples have been loaded from .idat files then the 18 internal probe controls on the beadchip (including bisulphite conversion efficiency) will be included if you set parameter RGEffect=TRUE in the champ.SVD() function. Also compared with the old version of the ChAMP package, the current version of champ.SVD() would detect all valid factors to perform analysis, which means the plot contains the two following conditions:

  • Covariates are not associated with name, Sample_Name or File_Name
  • Covariates contain at least two values(for example, for ‘BeadChip ID’ to be tested as a covariate, samples from at least two different BeadChips must be included in the study)

champ.SVD() function detects all valid covariates in your pd file. Thus if you have some unique covariates you want to be analysed, you may combine them into your pd file, allowing champ.SVD() to test for association with the most significant components of variation in the dataset. Note that champ.SVD() will only uses phenotype data in the form of a pd file. So, if you have your own covariates which you want to be analysed with your current pd file, please cbind() your covariates along with myLoad$pd, then use this object as input for the pd parameter of the champ.SVD() function.

Note that, for different type of covariates (categorical and numeric), champ.SVD()uses different methods to calculate the significance between covariates and components (Krustal.Test and Linear Regression) . Thus, please make sure your pd object is a data frame and numeric covariates are transferred into “numeric” type, while categorical covariates are transferred into “factor” or “character” type. If your Age covariate was assigned as “character”, the champ.SVDfunction will not detect that it should be analysed as numeric. Thus, we suggest users have very clear understanding of their dataset and pd files.

While champ.SVD() is running, all detected covariates will be printed on the screen, so that the user may check if the covariates you want to analyse are correct. The result is a heatmap (saved as SVDsummary.pdf) of the top principal components correlated to the covariates information provided. In champ.SVD() we used Random Matrix Theory from isva package to detect numbers of latent variables. If our method detected more than 20 components, we would select only top 20. In the heatmap, the darker colours represent a more significant p-value, indicating a stronger correlation of the SVD component with a factor of interest. If the SVD analysis shows that technical factors account for a substantial fraction of variation, then it will be necessary to implement other normalization methods (e.g ComBat) that may help remove this technical variation. ComBat is included in the ChAMP pipeline to remove variation related to the beadchip, position and/or plate, but it may be used to remove other batch effects revealed in the SVD analysis.

You may use the following code to generate the SVD plot:

Above plot is generated on our Demo 450K array data set. Since there are only 8 samples in that data set, so the final headmap plot looks not complicated. Below we used another plot from GSE40279, this data contains 656 sample, and a complicated pd file, with covariates like Age and Race included. Below we are showing the result of SVD plot we generated by using champ.SVD() function.

In above plot, Age is a numeric variable, while other factors are categorical factors. The darker block means stronger correlation between deconvoluted components and covariates.

6.6 Batch Effect Correction

This function implements the ComBat normalization method8 that was developed for microarrays. The sva R package is used to implement this function. ComBat is specifically defined in the ChAMP package to correct for batch effects. More advanced users can implement ComBat using sva documentation to adjust for other batch effects. You need to set parameter batchname=c("Slide") in the function to correct them.

Note that champ.runCombat() would extract batch from your batchname, so please make sure you understand you dataset perfectly. Another important thing is, previously champ.runCombat() would automatically assume Sample_Group is the covariates need to be analysed, but now, we added a parameter called variablename so that users may assign their own covariates to analysis, thus for some data set whose disease phenotype is NOT Sample_Group, please remember to assign variablename parameter.

ComBat algorithm uses an empirical Bayes method to correct for technical variation. If ComBat were applied directly to beta values, the output may not be bounded between 0 and 1. For this reason ChAMP logit transforms beta values before ComBat adjustment and then computes the reverse logit transformation following the ComBat adjustment. If the user has chosen to use M-values, please assign logitTrans=FALSE.

During the function champ.runCombat(), the program would automatically detect all valid factors might be corrected and list on screen, so that user may check if there are the batches you want to correct. Then if the batchname assigned by user are correct, the function would started to do batch correction.

In the latest version, champ.runCombat() would also check if user’s Batch and Variable counfounded with each other. Confounding means all samples from one phenotype of your variable are comes from certain one phenotype of your batch, which means, if you correct batches, your variable-contained information are also gone, which is not allowed in Combat function of SVA package, that’s why we made check beforehead.

You can use following code to do batch correction:

The ComBat function could be a very time consuming step in the pipeline if you have a large number of samples. After correction, you may use champ.SVD() function to check the corrected result.

6.7 Differential Methylation Probes

Differentially Methylated Probes (DMPs) is the most common desired output. Here users may use champ.DMP() function to calculate differential methylation, and can use DMP.GUI() to check the result.

champ.DMP() function implements the limma package to calculate the p-value for differential methylation using a linear model. Our latest champ.DMP() function would support numeric variable like age, and categorical variables which contain more than two phenotypes, like “tumor”, “metastasis”, “control”. If our function detected numeric variables like age, linear regression would be conducted on each CpG sites, to find your covariate-related CpGs, say age-related CpGs. While categorical variables detected, champ.DMP() would do contrast comparison between each two phenotypes in your covariate. Say you have covariate as “tumor”, “metastasis”, “control”, then champ.DMP() would compare “tumor–metastatic”, “tumor-control”, and “metastatis-control”. Each comparison would return one data frame containing significantly differential methylated probes.

The output of champ.DMP() includes some data frames of P-value, t-statistic and difference in mean methylation (this is labeled as logFC since it is analogous to the log fold-change in gene expression analysis) between two groups (for categorical covariate only). It also includes the annotation for each probe, the average beta value for the sample group, and the delta beta value for the two groups used in the comparison (delta value is same as logFC, we kept is here because very old version ChAMP used it).

In our latest version champ.DMP(), numeric variables are fully support not, if you have covariate like ages or time series data. However, more advanced users can run limma with other settings and use the output (including all probes and their p-values) for the DMR hunter.

Users may use following code to get DMPs (still we are using Demo 450K test data here):

##       Contrasts
## Levels pT-pC
##     pC    -1
##     pT     1

We can check the result of champ.DMP() as below. Remember that in champ.DMP(), each returned result would be stored in an element in a list. Each element in the list is one result of DMPs detected between two phenotypes. If your covariate is numeric variable, the returned data frame name is “NumericVariable”. If your covariate is categorical variable, the returned name would be “PhenoA_to_PhenoB”, where “PhenoA” and “PhenoB” are two name of your phenotypes.

In new version of ChAMP, we include a GUI interface for user to check the result of myDMP generated from above code. You just need to provide the “unmodified” result of champ.DMP (myDMP), orginal beta matrix you used to generat DMP (beta) and covariates you want to analyse. DMP.GUI() function would automatically detect if your covariate is numeric or categorical. If your covariate is numeric, DMP.GUI() would plot scatter plot for each CpGs, showing trend of beta value along with your covariate. If your covariate is categorical variable like cancer/normal, DMP.GUI() would plot boxplot for significantly differential methylated CpGs. You can use following code to open DMP.GUI() function.