1 Introduction
In this manual, we will show how to use the methylKit package. methylKit is an R package for analysis and annotation of DNA methylation information obtained by high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants. But, it can potentially handle whole-genome bisulfite sequencing data if proper input format is provided.
1.1 DNA methylation
DNA methylation in vertebrates typically occurs at CpG dinucleotides, however non-CpG Cs are also methylated in certain tissues such as embryonic stem cells. DNA methylation can act as an epigenetic control mechanism for gene regulation. Methylation can hinder binding of transcription factors and/or methylated bases can be bound by methyl-binding-domain proteins which can recruit chromatin remodeling factors. In both cases, the transcription of the regulated gene will be effected. In addition, aberrant DNA methylation patterns have been associated with many human malignancies and can be used in a predictive manner. In malignant tissues, DNA is either hypo-methylated or hyper-methylated compared to the normal tissue. The location of hyper- and hypo-methylated sites gives a distinct signature to many diseases. Traditionally, hypo-methylation is associated with gene transcription (if it is on a regulatory region such as promoters) and hyper-methylation is associated with gene repression.
1.2 High-throughput bisulfite sequencing
Bisulfite sequencing is a technique that can determine DNA methylation patterns. The major difference from regular sequencing experiments is that, in bisulfite sequencing DNA is treated with bisulfite which converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected. By sequencing and aligning those converted DNA fragments it is possible to call methylation status of a base. Usually, the methylation status of a base determined by a high-throughput bisulfite sequencing will not be a binary score, but it will be a percentage. The percentage simply determines how many of the bases that are aligning to a given cytosine location in the genome have actual C bases in the reads. Since bisulfite treatment leaves methylated Cs intact, that percentage will give us percent methylation score on that base. The reasons why we will not get a binary response are:
- the probable sequencing errors in high-throughput sequencing experiments
- incomplete bisulfite conversion
- (and a more likely scenario) is heterogeneity of samples and heterogeneity of paired chromosomes from the same sample
2 Basics
2.1 Reading the methylation call files
We start by reading in the methylation call data from bisulfite sequencing with methRead function. Reading in the data this way will return a methylRawList object which stores methylation information per sample for each covered base. By default methRead requires a minimum coverage of 10 reads per base to ensure good quality of the data and a high confidence methylation percentage.
The methylation call files are basically text files that contain percent methylation score per base. Such input files may be obtained from AMP pipeline developed for aligning RRBS reads or from processBismarkAln function. However, “cytosineReport” and “coverage” files from Bismark aligner can be read in to methylKit as well.
A typical methylation call file looks like this:
## chrBase chr base strand coverage freqC freqT
## 1 chr21.9764539 chr21 9764539 R 12 25.00 75.00
## 2 chr21.9764513 chr21 9764513 R 12 0.00 100.00
## 3 chr21.9820622 chr21 9820622 F 13 0.00 100.00
## 4 chr21.9837545 chr21 9837545 F 11 0.00 100.00
## 5 chr21.9849022 chr21 9849022 F 124 72.58 27.42Most of the time bisulfite sequencing experiments have test and control samples. The test samples can be from a disease tissue while the control samples can be from a healthy tissue. You can read a set of methylation call files that have test/control conditions giving treatment vector option. For sake of subsequent analysis, file.list, sample.id and treatment option should have the same order. In the following example, first two files have the sample ids “test1” and “test2” and as determined by treatment vector they belong to the same group. The third and fourth files have sample ids “ctrl1” and “ctrl2” and they belong to the same group as indicated by the treatment vector.
library(methylKit)
file.list=list( system.file("extdata",
"test1.myCpG.txt", package = "methylKit"),
system.file("extdata",
"test2.myCpG.txt", package = "methylKit"),
system.file("extdata",
"control1.myCpG.txt", package = "methylKit"),
system.file("extdata",
"control2.myCpG.txt", package = "methylKit") )
# read the files to a methylRawList object: myobj
myobj=methRead(file.list,
sample.id=list("test1","test2","ctrl1","ctrl2"),
assembly="hg18",
treatment=c(1,1,0,0),
context="CpG",
mincov = 10
)In addition to the options we mentioned above, any tab separated text file with a generic format can be read in using methylKit, such as methylation ratio files from BSMAP. See here for an example.
2.2 Reading the methylation call files and store them as flat file database
Sometimes, when dealing with multiple samples and increased sample sizes coming from genome wide bisulfite sequencing experiments, the memory of your computer might not be sufficient enough.
Therefore methylKit offers a new group of classes, that are basically pendants to the original methylKit classes with one important difference: The methylation information, which normally is internally stored as data.frame, is stored in an external bgzipped file and is indexed by tabix (???), to enable fast retrieval of records or regions. This group contains methylRawListDB, methylRawDB, methylBaseDB and methylDiffDB, let us call them methylDB objects.
We can now create a methylRawListDB object, which stores the same content as myobj from above. But the single methylRaw objects retrieve their data from the tabix-file linked under dbpath.
library(methylKit)
file.list=list( system.file("extdata", "test1.myCpG.txt", package = "methylKit"),
system.file("extdata", "test2.myCpG.txt", package = "methylKit"),
system.file("extdata", "control1.myCpG.txt", package = "methylKit"),
system.file("extdata", "control2.myCpG.txt", package = "methylKit") )
# read the files to a methylRawListDB object: myobjDB
# and save in databases in folder methylDB
myobjDB=methRead(file.list,
sample.id=list("test1","test2","ctrl1","ctrl2"),
assembly="hg18",
treatment=c(1,1,0,0),
context="CpG",
dbtype = "tabix",
dbdir = "methylDB"
)
print(myobjDB[[1]]@dbpath)## [1] "/tmp/RtmpOhDUrw/Rbuild3bfc992febc/methylKit/vignettes/methylDB/test1.txt.bgz"Most if not all functions in this package will work with methylDB objects the same way as it does with normal methylKit objects. Functions that return methylKit objects, will return a methylDB object if provided, but there are a few exceptions such as the select, the [ and the selectByOverlap functions.
2.3 Reading the methylation calls from sorted Bismark alignments
Alternatively, methylation percentage calls can be calculated from sorted SAM or BAM file(s) from Bismark aligner and read-in to the memory. Bismark is a popular aligner for bisulfite sequencing reads, available here (???). processBismarkAln function is designed to read-in Bismark SAM/BAM files as methylRaw or methylRawList objects which store per base methylation calls. SAM files must be sorted by chromosome and read position columns, using ‘sort’ command in unix-like machines will accomplish such a sort easily. BAM files should be sorted and indexed. This could be achieved with samtools (http://www.htslib.org/doc/samtools.html).
The following command reads a sorted SAM file and creates a methylRaw object for CpG methylation. The user has the option to save the methylation call files to a folder given by save.folder option. The saved files can be read-in using the methRead function when needed.
my.methRaw=processBismarkAln( location =
system.file("extdata",
"test.fastq_bismark.sorted.min.sam",
package = "methylKit"),
sample.id="test1", assembly="hg18",
read.context="CpG", save.folder=getwd())It is also possible to read multiple SAM files at the same time, check processBismarkAln documentation.
2.4 Descriptive statistics on samples
Since we read the methylation data now, we can check the basic stats about the methylation data such as coverage and percent methylation. We now have a methylRawList object which contains methylation information per sample. The following command prints out percent methylation statistics for second sample: “test2”
getMethylationStats(myobj[[2]],plot=FALSE,both.strands=FALSE)## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 20.00 82.79 63.17 94.74 100.00
## percentiles:
## 0% 10% 20% 30% 40% 50% 60% 70%
## 0.00000 0.00000 0.00000 48.38710 70.00000 82.78556 90.00000 93.33333
## 80% 90% 95% 99% 99.5% 99.9% 100%
## 96.42857 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000The following command plots the histogram for percent methylation distribution.The figure below is the histogram and numbers on bars denote what percentage of locations are contained in that bin. Typically, percent methylation histogram should have two peaks on both ends. In any given cell, any given base are either methylated or not. Therefore, looking at many cells should yield a similar pattern where we see lots of locations with high methylation and lots of locations with low methylation.
getMethylationStats(myobj[[2]],plot=TRUE,both.strands=FALSE)
We can also plot the read coverage per base information in a similar way, again numbers on bars denote what percentage of locations are contained in that bin. Experiments that are highly suffering from PCR duplication bias will have a secondary peak towards the right hand side of the histogram.
getCoverageStats(myobj[[2]],plot=TRUE,both.strands=FALSE)
2.5 Filtering samples based on read coverage
It might be useful to filter samples based on coverage. Particularly, if our samples are suffering from PCR bias it would be useful to discard bases with very high read coverage. Furthermore, we would also like to discard bases that have low read coverage, a high enough read coverage will increase the power of the statistical tests. The code below filters a methylRawList and discards bases that have coverage below 10X and also discards the bases that have more than 99.9th percentile of coverage in each sample.
filtered.myobj=filterByCoverage(myobj,lo.count=10,lo.perc=NULL,
hi.count=NULL,hi.perc=99.9)3 Comparative analysis
3.1 Merging samples
In order to do further analysis, we will need to get the bases covered in all samples. The following function will merge all samples to one object for base-pair locations that are covered in all samples. Setting destrand=TRUE (the default is FALSE) will merge reads on both strands of a CpG dinucleotide. This provides better coverage, but only advised when looking at CpG methylation (for CpH methylation this will cause wrong results in subsequent analyses). In addition, setting destrand=TRUE will only work when operating on base-pair resolution, otherwise setting this option TRUE will have no effect. The unite() function will return a methylBase object which will be our main object for all comparative analysis. The methylBase object contains methylation information for regions/bases that are covered in all samples.
meth=unite(myobj, destrand=FALSE)## uniting...Let us take a look at the data content of methylBase object:
head(meth)## chr start end strand coverage1 numCs1 numTs1 coverage2 numCs2 numTs2
## 1 chr21 9853296 9853296 + 17 10 7 333 268 65
## 2 chr21 9853326 9853326 + 17 12 5 329 249 79
## 3 chr21 9860126 9860126 + 39 38 1 83 78 5
## 4 chr21 9906604 9906604 + 68 42 26 111 97 14
## 5 chr21 9906616 9906616 + 68 52 16 111 104 7
## 6 chr21 9906619 9906619 + 68 59 9 111 109 2
## coverage3 numCs3 numTs3 coverage4 numCs4 numTs4
## 1 18 16 2 395 341 54
## 2 16 14 2 379 284 95
## 3 83 83 0 41 40 1
## 4 23 18 5 37 33 4
## 5 23 14 9 37 27 10
## 6 22 18 4 37 29 8By default, unite function produces bases/regions covered in all samples. That requirement can be relaxed using “min.per.group” option in unite function.
# creates a methylBase object,
# where only CpGs covered with at least 1 sample per group will be returned
# there were two groups defined by the treatment vector,
# given during the creation of myobj: treatment=c(1,1,0,0)
meth.min=unite(myobj,min.per.group=1L)3.2 Sample Correlation
We can check the correlation between samples using getCorrelation. This function will either plot scatter plot and correlation coefficients or just print a correlation matrix.
getCorrelation(meth,plot=TRUE)## test1 test2 ctrl1 ctrl2
## test1 1.0000000 0.9252530 0.8767865 0.8737509
## test2 0.9252530 1.0000000 0.8791864 0.8801669
## ctrl1 0.8767865 0.8791864 1.0000000 0.9465369
## ctrl2 0.8737509 0.8801669 0.9465369 1.0000000
3.3 Clustering samples
We can cluster the samples based on the similarity of their methylation profiles. The following function will cluster the samples and draw a dendrogram.
clusterSamples(meth, dist="correlation", method="ward", plot=TRUE)## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
##
## Call:
## hclust(d = d, method = HCLUST.METHODS[hclust.method])
##
## Cluster method : ward.D
## Distance : pearson
## Number of objects: 4Setting the plot=FALSE will return a dendrogram object which can be manipulated by users or fed in to other user functions that can work with dendrograms.
hc = clusterSamples(meth, dist="correlation", method="ward", plot=FALSE)We can also do a PCA analysis on our samples. The following function will plot a scree plot for importance of components.
PCASamples(meth, screeplot=TRUE)
We can also plot PC1 and PC2 axis and a scatter plot of our samples on those axis which will reveal how they cluster.
PCASamples(meth)
3.4 Batch effects
We have implemented some rudimentary functionality for batch effect control. You can check which one of the principal components are statistically associated with the potential batch effects such as batch processing dates, age of subjects, sex of subjects using assocComp. The function gets principal components from the percent methylation matrix derived from the input methylBase object, and checks for association. The tests for association are either via Kruskal-Wallis test or Wilcoxon test for categorical attributes and correlation test for numerical attributes for samples such as age. If you are convinced that some principal components are accounting for batch effects, you can remove those principal components from your data using removeComp.
# make some batch data frame
# this is a bogus data frame
# we don't have batch information
# for the example data
sampleAnnotation=data.frame(batch_id=c("a","a","b","b"),
age=c(19,34,23,40))
as=assocComp(mBase=meth,sampleAnnotation)
as## $pcs
## PC1 PC2 PC3 PC4
## test1 -0.4978699 -0.5220504 0.68923849 -0.06737363
## test2 -0.4990924 -0.4805506 -0.71827964 0.06365693
## ctrl1 -0.5016543 0.4938800 0.08068700 0.70563101
## ctrl2 -0.5013734 0.5026102 -0.05014261 -0.70249091
##
## $vars
## [1] 92.271885 4.525328 1.870950 1.331837
##
## $association
## PC1 PC2 PC3 PC4
## batch_id 0.3333333 0.3333333 1.0000000 1.0000000
## age 0.5864358 0.6794346 0.3140251 0.3467957# construct a new object by removing the first pricipal component
# from percent methylation value matrix
newObj=removeComp(meth,comp=1)In addition to the methods described above, if you have used other ways to correct for batch effects and obtained a corrected percent methylation matrix, you can use reconstruct function to reconstruct a corrected methylBase object. Users have to supply a corrected percent methylation matrix and methylBase object (where the uncorrected percent methylation matrix obtained from) to the reconstruct function. Corrected percent methylation matrix should have the same row and column order as the original percent methylation matrix. All of these functions described in this section work on a methylBase object that does not have missing values (that means all bases in methylBase object should have coverage in all samples).
mat=percMethylation(meth)
# do some changes in the matrix
# this is just a toy example
# ideally you want to correct the matrix
# for batch effects
mat[mat==100]=80
# reconstruct the methylBase from the corrected matrix
newobj=reconstruct(mat,meth)3.5 Tiling windows analysis
For some situations, it might be desirable to summarize methylation information over tiling windows rather than doing base-pair resolution analysis. methylKit provides functionality to do such analysis. The function below tiles the genome with windows of 1000bp length and 1000bp step-size and summarizes the methylation information on those tiles. In this case, it returns a methylRawList object which can be fed into unite and calculateDiffMeth functions consecutively to get differentially methylated regions. The tilling function adds up C and T counts from each covered cytosine and returns a total C and T count for each tile.
As mentioned before, methRead sets a minimum coverage threshold of 10 reads per cytosine to ensure good quality for downstream base-pair resolution analysis. However in the case of tiling window / regional analysis one might want to set the initial per base coverage threshold to a lower value and then filter based on the number of bases (cytosines) per region. Filtering samples based on read coverage might still be appropriate to remove coverage biases.
myobj_lowCov = methRead(file.list,
sample.id=list("test1","test2","ctrl1","ctrl2"),
assembly="hg18",
treatment=c(1,1,0,0),
context="CpG",
mincov = 3
)## Received list of locations.## Reading file.
## Reading file.
## Reading file.
## Reading file.tiles = tileMethylCounts(myobj_lowCov,win.size=1000,step.size=1000,cov.bases = 10)
head(tiles[[1]],3)## chr start end strand coverage numCs numTs
## 1 chr21 9906001 9907000 * 970 604 366
## 2 chr21 10011001 10012000 * 2461 2169 292
## 3 chr21 10012001 10013000 * 4932 4175 7563.6 Finding differentially methylated bases or regions
The calculateDiffMeth() function is the main function to calculate differential methylation. Depending on the sample size per each set it will either use Fisher’s exact or logistic regression to calculate P-values. P-values will be adjusted to Q-values using SLIM method (???). If you have replicates, the function will automatically use logistic regression. You can force the calculateDiffMeth() function to use Fisher’s exact test if you pool the replicates when there is only test and control sample groups. This can be achieved with pool() function, see FAQ for more info.
In its simplest form ,where there are no covariates, the logistic regression will try to model the the log odds ratio which is based on methylation proportion of a CpG, \(\pi_i\), using the treatment vector which denotes the sample group membership for the CpGs in the model. Below, the “Treatment” variable is used to predict the log-odds ratio of methylation proportions.
\[ \text{log}\left(\dfrac{\pi_i}{1-\pi_i}\right) =\beta_0 + \beta_1 Treatment_i \]
The logistic regression model is fitted per CpG or per region and we test if treatment vector has any effect on the outcome variable or not. In other words, we are testing if \(log(\pi_i/(1-\pi_i)) = \beta_0 + \beta_1 Treatment_i\) is a “better” model than \(log(\pi_i/(1-\pi_i)) = \beta_0\).
The following code snippet tests for differential methylation. Since the example data has replicates, the logistic regression based modeling and test will be used.
myDiff=calculateDiffMeth(meth)## two groups detected:
## will calculate methylation difference as the difference of
## treatment (group: 1) - control (group: 0)After q-value calculation, we can select the differentially methylated regions/bases based on q-value and percent methylation difference cutoffs. Following bit selects the bases that have q-value<0.01 and percent methylation difference larger than 25%. If you specify type="hyper" or type="hypo" options, you will get hyper-methylated or hypo-methylated regions/bases.
# get hyper methylated bases
myDiff25p.hyper=getMethylDiff(myDiff,difference=25,qvalue=0.01,type="hyper")
#
# get hypo methylated bases
myDiff25p.hypo=getMethylDiff(myDiff,difference=25,qvalue=0.01,type="hypo")
#
#
# get all differentially methylated bases
myDiff25p=getMethylDiff(myDiff,difference=25,qvalue=0.01)We can also visualize the distribution of hypo/hyper-methylated bases/regions per chromosome using the following function. In this case, the example set includes only one chromosome. The list shows percentages of hypo/hyper methylated bases over all the covered bases in a given chromosome.
diffMethPerChr(myDiff,plot=FALSE,qvalue.cutoff=0.01, meth.cutoff=25)## $diffMeth.per.chr
## chr number.of.hypermethylated percentage.of.hypermethylated
## 1 chr21 75 7.788162
## number.of.hypomethylated percentage.of.hypomethylated
## 1 59 6.126687
##
## $diffMeth.all
## number.of.hypermethylated percentage.of.hypermethylated
## 1 75 7.788162
## number.of.hypomethylated percentage.of.hypomethylated
## 1 59 6.1266873.7 Correcting for overdispersion
Overdispersion occurs when there is more variability in the data than assumed by the distribution. In the logistic regression model, the response variable \(meth_i\) (number of methylated CpGs) is expected to have a binomial distribution: \[meth_i \sim Bin(n_i, \pi_i)\] Therefore, the methylated CpGs will have the variance \(n_i \pi_i(1-\pi_i)\) and mean \(\mu_i=n_i \pi_i\). \(n_i\) is the coverage for the CpG or a region and \(\pi_i\) is the underlying methylation proportion.
Overdispersion occurs when the variance of \(meth_i\) is greater than \(n_i\hat{\pi_i}(1-\hat{\pi_i})\), where \(\hat{\pi_i}\) is the estimated methylation proportion from the model. This can be corrected by calculating a scaling parameter \(\phi\) and adjusting the variance as \(\phi n_i \hat{\pi_i}(1-\hat{\pi_i})\). calculateDiffMeth can calculate that scaling parameter and use it in statistical tests to correct for overdispersion. The parameter is calculated as proposed by (???) as follows: \(\hat{\phi}=X^2/(N-P)\), where \(X\) is Pearson goodness-of-fit statistic, \(N\) is the number of samples, and \(P\) is the number of parameters. This scaling parameter also effects the statistical tests and if there is overdispersion correction the tests will be more stringent in general.
By default,this overdispersion correction is not applied. This can be achieved by setting overdispersion="MN". The Chisq-test is used by default only when no overdispersion correction is applied. If overdispersion correction is applied, the function automatically switches to the F-test. The Chisq-test can be manually chosen in this case as well, but the F-test only works with overdispersion correction switched on. In both cases, the procedure tests if the full model (the model where treatment is included as an explanatory variable) explains the data better than the null model (the model with no treatment, just intercept). If there is no effect based on samples being from different groups adding a treatment vector for sample groupings will be no better than not adding the treatment vector. Below, we simulate methylation data and use overdispersion correction for the logistic regression model.
sim.methylBase1<-dataSim(replicates=6,sites=1000,
treatment=c(rep(1,3),rep(0,3)),
sample.ids=c(paste0("test",1:3),paste0("ctrl",1:3))
)
my.diffMeth<-calculateDiffMeth(sim.methylBase1[1:,],
overdispersion="MN",test="Chisq",mc.cores=1)3.8 Accounting for covariates
Covariates can be included in the analysis. The function will then try to separate the influence of the covariates from the treatment effect via the logistic regression model. In this case, we will test if full model (model with treatment and covariates) is better than the model with the covariates only. If there is no effect due to the treatment (sample groups), the full model will not explain the data better than the model with covariates only. In calculateDiffMeth, this is achieved by supplying the covariates argument in the format of a data.frame. Below, we simulate methylation data and add make a data.frame for the age. The data frame can include more columns, and those columns can also be factor variables. The row order of the data.frame should match the order of samples in the methylBase object.
covariates=data.frame(age=c(30,80,34,30,80,40))
sim.methylBase<-dataSim(replicates=6,sites=1000,
treatment=c(rep(1,3),rep(0,3)),
covariates=covariates,
sample.ids=c(paste0("test",1:3),paste0("ctrl",1:3))
)
my.diffMeth3<-calculateDiffMeth(sim.methylBase,
covariates=covariates,
overdispersion="MN",test="Chisq",mc.cores=1)3.9 Finding differentially methylated bases using multiple-cores
The differential methylation calculation speed can be increased substantially by utilizing multiple-cores in a machine if available. Both Fisher’s Exact test and logistic regression based test are able to use multiple-core option.
The following piece of code will run differential methylation calculation using 2 cores.
myDiff=calculateDiffMeth(meth,mc.cores=2)4 Annotating differentially methylated bases or regions
We can annotate our differentially methylated regions/bases based on gene annotation using genomation package. In this example, we read the gene annotation from a BED file and annotate our differentially methylated regions with that information using genomation functions. Note that these functions operate on GRanges objects ,so we first coerce methylKit objects to GRanges. This annotation operation will tell us what percentage of our differentially methylated regions are on promoters/introns/exons/intergenic region. In this case we read annotation from a BED file, similar gene annotation information can be fetched using GenomicFeatures package or other packages available from Bioconductor.org.
library(genomation)## Loading required package: grid##
## Attaching package: 'genomation'## The following objects are masked from 'package:methylKit':
##
## getFeatsWithTargetsStats, getFlanks, getMembers,
## getTargetAnnotationStats, plotTargetAnnotation# read the gene BED file
gene.obj=readTranscriptFeatures(system.file("extdata", "refseq.hg18.bed.txt",
package = "methylKit"))## Reading the table...## Calculating intron coordinates...## Calculating exon coordinates...## Calculating TSS coordinates...## Calculating promoter coordinates...## Outputting the final GRangesList...#
# annotate differentially methylated CpGs with
# promoter/exon/intron using annotation data
#
annotateWithGeneParts(as(myDiff25p,"GRanges"),gene.obj)## Summary of target set annotation with genic parts## Rows in target set: 133## -----------------------## percentage of target features overlapping with annotation:## promoter exon intron intergenic
## 27.82 15.04 34.59 57.14##
## percentage of target features overlapping with annotation:## (with promoter > exon > intron precedence):## promoter exon intron intergenic
## 27.82 0.00 15.04 57.14## ## percentage of annotation boundaries with feature overlap:## promoter exon intron
## 0.29 0.03 0.17## ## summary of distances to the nearest TSS:## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5 828 45158 52034 94644 313528## Similarly, we can read the CpG island annotation and annotate our differentially methylated bases/regions with them.
# read the shores and flanking regions and name the flanks as shores
# and CpG islands as CpGi
cpg.obj=readFeatureFlank(system.file("extdata", "cpgi.hg18.bed.txt",
package = "methylKit"),
feature.flank.name=c("CpGi","shores"))## Warning: 'GenomicRangesList' is deprecated.
## Use 'GRangesList(..., compress=FALSE)' instead.
## See help("Deprecated")#
# convert methylDiff object to GRanges and annotate
diffCpGann=annotateWithFeatureFlank(as(myDiff25p,"GRanges"),
cpg.obj$CpGi,cpg.obj$shores,
feature.name="CpGi",flank.name="shores")4.1 Regional analysis
We can also summarize methylation information over a set of defined regions such as promoters or CpG islands. The function below summarizes the methylation information over a given set of promoter regions and outputs a methylRaw or methylRawList object depending on the input. We are using the output of genomation functions used above to provide the locations of promoters. For regional summary functions, we need to provide regions of interest as GRanges object.
promoters=regionCounts(myobj,gene.obj$promoters)
head(promoters[[1]])## chr start end strand coverage numCs numTs
## 1 chr21 10011791 10013791 - 7953 6662 1290
## 2 chr21 10119796 10121796 - 1725 1171 554
## 3 chr21 10119808 10121808 - 1725 1171 554
## 4 chr21 13903368 13905368 + 10 10 0
## 5 chr21 14273636 14275636 - 282 220 62
## 6 chr21 14509336 14511336 + 1058 55 10034.2 Convenience functions for annotation objects
After getting the annotation of differentially methylated regions, we can get the distance to TSS and nearest gene name using the getAssociationWithTSS function from genomation package.
diffAnn=annotateWithGeneParts(as(myDiff25p,"GRanges"),gene.obj)
# target.row is the row number in myDiff25p
head(getAssociationWithTSS(diffAnn))## target.row dist.to.feature feature.name feature.strand
## 60 1 106111 NM_199260 -
## 60.1 2 106098 NM_199260 -
## 60.2 3 106092 NM_199260 -
## 60.3 4 105919 NM_199260 -
## 60.4 5 85265 NM_199260 -
## 60.5 6 68287 NM_199260 -It is also desirable to get percentage/number of differentially methylated regions that overlap with intron/exon/promoters
getTargetAnnotationStats(diffAnn,percentage=TRUE,precedence=TRUE)## promoter exon intron intergenic
## 27.82 0.00 15.04 57.14We can also plot the percentage of differentially methylated bases overlapping with exon/intron/promoters
plotTargetAnnotation(diffAnn,precedence=TRUE,
main="differential methylation annotation")
We can also plot the CpG island annotation the same way. The plot below shows what percentage of differentially methylated bases are on CpG islands, CpG island shores and other regions.
plotTargetAnnotation(diffCpGann,col=c("green","gray","white"),
main="differential methylation annotation")
It might be also useful to get percentage of intron/exon/promoters that overlap with differentially methylated bases.
getFeatsWithTargetsStats(diffAnn,percentage=TRUE)## promoter exon intron
## 0.29 0.03 0.175 methylKit convenience functions
5.1 Coercing methylKit objects to GRanges
Most methylKit objects (methylRaw,methylBase and methylDiff), including methylDB objects (methylRawDB,methylBaseDB and methylDiffDB) can be coerced to GRanges objects from GenomicRanges package. Coercing methylKit objects to GRanges will give users additional flexibility when customizing their analyses.
class(meth)## [1] "methylBase"
## attr(,"package")
## [1] "methylKit"as(meth,"GRanges")## GRanges object with 963 ranges and 12 metadata columns:
## seqnames ranges strand | coverage1 numCs1 numTs1 coverage2
## <Rle> <IRanges> <Rle> | <integer> <integer> <integer> <integer>
## [1] chr21 9853296 + | 17 10 7 333
## [2] chr21 9853326 + | 17 12 5 329
## [3] chr21 9860126 + | 39 38 1 83
## [4] chr21 9906604 + | 68 42 26 111
## [5] chr21 9906616 + | 68 52 16 111
## ... ... ... ... . ... ... ... ...
## [959] chr21 19855690 + | 27 26 1 19
## [960] chr21 19855706 + | 27 27 0 19
## [961] chr21 19855711 + | 18 18 0 18
## [962] chr21 19943653 + | 12 12 0 32
## [963] chr21 19943695 + | 12 11 1 32
## numCs2 numTs2 coverage3 numCs3 numTs3 coverage4 numCs4
## <integer> <integer> <integer> <integer> <integer> <integer> <integer>
## [1] 268 65 18 16 2 395 341
## [2] 249 79 16 14 2 379 284
## [3] 78 5 83 83 0 41 40
## [4] 97 14 23 18 5 37 33
## [5] 104 7 23 14 9 37 27
## ... ... ... ... ... ... ... ...
## [959] 17 2 34 34 0 12 12
## [960] 19 0 34 34 0 12 11
## [961] 15 3 34 34 0 12 12
## [962] 30 2 26 25 1 24 22
## [963] 32 0 26 26 0 27 24
## numTs4
## <integer>
## [1] 54
## [2] 95
## [3] 1
## [4] 4
## [5] 10
## ... ...
## [959] 0
## [960] 1
## [961] 0
## [962] 2
## [963] 3
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsclass(myDiff)## [1] "methylDiff"
## attr(,"package")
## [1] "methylKit"as(myDiff,"GRanges")## GRanges object with 963 ranges and 3 metadata columns:
## seqnames ranges strand | pvalue qvalue meth.diff
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric>
## [1] chr21 9853296 + | 0.00990814 0.0215658 -7.012107
## [2] chr21 9853326 + | 0.94735455 0.5921730 0.209136
## [3] chr21 9860126 + | 0.04160449 0.0697808 -4.111581
## [4] chr21 9906604 + | 0.21065168 0.2594531 -7.346369
## [5] chr21 9906616 + | 0.00157650 0.0043222 18.817505
## ... ... ... ... . ... ... ...
## [959] chr21 19855690 + | 0.0390193 0.0660275 -6.52174
## [960] chr21 19855706 + | 0.2371789 0.2829587 2.17391
## [961] chr21 19855711 + | 0.0241291 0.0446546 -8.33333
## [962] chr21 19943653 + | 0.7528367 0.5921730 1.45455
## [963] chr21 19943695 + | 0.3904234 0.3960455 3.38765
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths5.2 Converting methylKit objects to methylDB objects and vice versa
methylDB objects (methylRawDB, methylBaseDB and methylDiffDB) can be coerced to normal methylKit objects. This might speed up the analysis if sufficient computing resources are available. This can be done via “as()” function.
class(myobjDB[[1]])## [1] "methylRawDB"
## attr(,"package")
## [1] "methylKit"as(myobjDB[[1]],"methylRaw")You can also convert methylDB objects to their in-memory equivalents. Since that requires an additional parameter (the directory where the files will be located), we have a different function, named makeMethylDB to achieve this goal. Below, we convert a methylBase object to methylBaseDB and saving it at “exMethylDB” directory.
data(methylKit)
objDB=makeMethylDB(methylBase.obj,"exMethylDB")5.3 Loading methylDB objects from tabix files
Since version 1.13.1 of methylKit the underlying tabix file of methylDB objects (methylRawDB, methylBaseDB and methylDiffDB) include a header which stores the corresponding metadata of the object. Thus you can recreate the object with just the tabix file, which allows easy sharing of methylDB objects accross sessions or users.
data(methylKit)
baseDB.obj <- makeMethylDB(methylBase.obj,"my/path")## creating directory my/path ...## compressing the file with bgzip...## making tabix index...## flatfile located at: /tmp/RtmpOhDUrw/Rbuild3bfc992febc/methylKit/vignettes/my/path/methylBase_5d4b615170b3.txt.bgzmydbpath <- getDBPath(baseDB.obj)
rm(baseDB.obj)
methylKit:::checkTabixHeader(mydbpath)## $dbtype
## [1] "tabix"
##
## $sample.ids
## [1] "test1" "test2" "ctrl1" "ctrl2"
##
## $assembly
## [1] "hg18"
##
## $context
## [1] "CpG"
##
## $resolution
## [1] "base"
##
## $coverage.index
## [1] 5 8 11 14
##
## $numCs.index
## [1] 6 9 12 15
##
## $numTs.index
## [1] 7 10 13 16
##
## $treatment
## [1] 1 1 0 0
##
## $destranded
## [1] FALSE
##
## $num.records
## [1] 963
##
## $dbtype
## [1] "tabix"
##
## $class
## [1] "methylBase"readMethylDB(mydbpath)## methylBaseDB object with 963 rows
## --------------
## chr start end strand coverage1 numCs1 numTs1 coverage2 numCs2 numTs2
## 1 chr21 9853296 9853296 + 17 10 7 333 268 65
## 2 chr21 9853326 9853326 + 17 12 5 329 249 79
## 3 chr21 9860126 9860126 + 39 38 1 83 78 5
## 4 chr21 9906604 9906604 + 68 42 26 111 97 14
## 5 chr21 9906616 9906616 + 68 52 16 111 104 7
## 6 chr21 9906619 9906619 + 68 59 9 111 109 2
## coverage3 numCs3 numTs3 coverage4 numCs4 numTs4
## 1 18 16 2 395 341 54
## 2 16 14 2 379 284 95
## 3 83 83 0 41 40 1
## 4 23 18 5 37 33 4
## 5 23 14 9 37 27 10
## 6 22 18 4 37 29 8
## --------------
## sample.ids: test1 test2 ctrl1 ctrl2
## destranded FALSE
## assembly: hg18
## context: CpG
## treament: 1 1 0 0
## resolution: base
## dbtype: tabix5.4 Selection: subsetting methylKit Objects
We can also select rows from methylRaw, methylBase and methylDiff objects and methylDB pendants with select function. An appropriate methylKit object will be returned as a result of select function. Or you can use the '[' notation to subset the methylKit objects.
select(meth,1:5) # get first 10 rows of a methylBase object## chr start end strand coverage1 numCs1 numTs1 coverage2 numCs2 numTs2
## 1 chr21 9853296 9853296 + 17 10 7 333 268 65
## 2 chr21 9853326 9853326 + 17 12 5 329 249 79
## 3 chr21 9860126 9860126 + 39 38 1 83 78 5
## 4 chr21 9906604 9906604 + 68 42 26 111 97 14
## 5 chr21 9906616 9906616 + 68 52 16 111 104 7
## coverage3 numCs3 numTs3 coverage4 numCs4 numTs4
## 1 18 16 2 395 341 54
## 2 16 14 2 379 284 95
## 3 83 83 0 41 40 1
## 4 23 18 5 37 33 4
## 5 23 14 9 37 27 10myDiff[21:25,] # get 5 rows of a methylDiff object## chr start end strand pvalue qvalue meth.diff
## 21 chr21 9913543 9913543 + 1.254379e-02 2.632641e-02 -13.343109
## 22 chr21 9913575 9913575 + 2.755448e-01 3.161628e-01 -5.442623
## 23 chr21 9927527 9927527 + 1.120126e-07 9.257475e-07 -46.109840
## 24 chr21 9944505 9944505 + 7.907909e-20 7.515975e-18 -51.017943
## 25 chr21 9944663 9944663 - 1.790779e-05 7.678302e-05 -28.099911Important: Using select or '[' on methylDB objects will return its normal methylKit pendant, to avoid overhead of database operations.
5.4.1 selectByOverlap
We can select rows from any methylKit object, that lie inside the ranges of a GRanges object from GenomicRanges package with selectByOverlap function. An appropriate methylKit object will be returned as a result of selectByOverlap function.
library(GenomicRanges)
my.win=GRanges(seqnames="chr21",
ranges=IRanges(start=seq(from=9764513,by=10000,length.out=20),width=5000) )
# selects the records that lie inside the regions
selectByOverlap(myobj[[1]],my.win)Important: Using selectByOverlap on methylDB objects will return its normal methylKit pendant, to avoid overhead of database operations.
5.5 reorganize(): reorganizing samples and treatment vector within methylKit objects
The methylBase and methylRawList, as well as methylDB pendants can be reorganized by reorganize function. The function can subset the objects based on provided sample ids, it also creates a new treatment vector determining which samples belong to which group. Order of sample ids should match the treatment vector order.
# creates a new methylRawList object
myobj2=reorganize(myobj,sample.ids=c("test1","ctrl2"),treatment=c(1,0) )
# creates a new methylBase object
meth2 =reorganize(meth,sample.ids=c("test1","ctrl2"),treatment=c(1,0) )5.6 percMethylation(): Getting percent methylation matrix from methylBase objects
Percent methylation values can be extracted from methylBase object by using percMethylation function.
# creates a matrix containing percent methylation values
perc.meth=percMethylation(meth)5.7 methSeg(): segmentation of methylation or differential methylation profiles
Methylation or differential methylation profiles can be segmented to sections that contain similar CpGs with respect to their methylation profiles. This kind of segmentation could help us find interesting regions. For example, segmentation analysis will usually reveal high or low methylated regions, where low methylated regions could be interesting for gene regulation. The algorithm first finds segments that have CpGs with similar methylation levels, then those segments are classified to segment groups based on their mean methylation levels. This enables us to group segments with similar methylation levels to the same class.
See more at http://zvfak.blogspot.de/2015/06/segmentation-of-methylation-profiles.html
download.file("https://raw.githubusercontent.com/BIMSBbioinfo/compgen2018/master/day3_diffMeth/data/H1.chr21.chr22.rds",
destfile="H1.chr21.chr22.rds",method="curl")
mbw=readRDS("H1.chr21.chr22.rds")
# it finds the optimal number of componets as 6
res=methSeg(mbw,diagnostic.plot=TRUE,maxInt=100,minSeg=10)
# however the BIC stabilizes after 4, we can also try 4 componets
res=methSeg(mbw,diagnostic.plot=TRUE,maxInt=100,minSeg=10,G=1:4)
# get segments to BED file
methSeg2bed(res,filename="H1.chr21.chr22.trial.seg.bed")6 Frequently Asked Questions
Detailed answers to some of the frequently asked questions and various HOW-TO’s can be found at http://zvfak.blogspot.com/search/label/methylKit. In addition, http://code.google.com/p/methylkit/ has online documentation and links to tutorials and other related material. You can also check methylKit Q&A forum for answers https://groups.google.com/forum/#;!forum/methylkit_discussion.
Apart from those here are some of the frequently asked questions.
6.0.1 How can I select certain regions/bases from methylRaw or methylBase objects ?
See ?select or help("[", package = "methylKit")
6.0.2 How can I find if my regions of interest overlap with
exon/intron/promoter/CpG island etc.?
Currently, we will be able to tell you if your regions/bases overlap with the genomic features or not. See ?getMembers.
6.0.3 How can I find the nearest TSS associated with my CpGs ?
See ?genomation::getAssociationWithTSS
6.0.4 How do you define promoters and CpG island shores ?
Promoters are defined by options at genomation::readTranscriptFeatures function. The default option is to take -1000,+1000bp around the TSS and you can change that. Same goes for CpG islands when reading them in via genomation::readFeatureFlank function. Default is to take 2000bp flanking regions on each side of the CpG island as shores. But you can change that as well.
6.0.5 What does Bismark SAM output look like, where can I get more info ?
Check the Bismark (???) website and there are also example files that ship with the package. Look at their formats and try to run different variations of processBismarkAln() command on the example files.
6.0.6 How can I reorder or remove samples at/from methylRawList or methylBase
objects ?
See ?reorganize
6.0.7 Should I normalize my data ?
methylKit comes with a simple normalizeCoverage() function to normalize read coverage distributions between samples. Ideally, you should first filter bases with extreme coverage to account for PCR bias using filterByCoverage() function, then run normalizeCoverage() function to normalize coverage between samples. These two functions will help reduce the bias in the statistical tests that might occur due to systematic over-sampling of reads in certain samples.
6.0.8 How can I force methylKit to use Fisher’s exact test ?
methylKit decides which test to use based on number of samples per group. In order to use Fisher’s exact there must be one sample in each of the test and control groups. So if you have multiple samples for group, the package will employ Logistic Regression based test. However, you can use pool() function to pool samples in each group so that you have one representative sample per group. pool() function will sum up number of Cs and Ts in each group. We recommend using filterByCoverage() and normalizeCoverage() functions prior to using pool(). See ?pool
6.0.9 Can use data from other aligners than Bismark in methylKit ?
Yes, you can. methylKit can read any generic methylation percentage/ratio file as long as that text file contains columns for chromosome, start, end, strand, coverage and number of methylated cytosines. However, methylKit can only process SAM files from Bismark. For other aligners, you need to get a text file containing the minimal information described above. Some aligners will come with scripts or built-in tools to provide such files. See http://zvfak.blogspot.com/2012/10/how-to-read-bsmap-methylation-ratio.html for how to read methylation ratio files from BSMAP (???) aligner.
6.0.10 Can I transform an methylKit object into an methylDB object ?
Yes, you can. Many functions of the analysis workflow provide an save.db argument, which allows you to save the output as methylDB object. For example see ?unite and also check the ... argument section for further details.
You can also use the makeMethylDB() function to export your in-memory object to flat-file database.
6.0.12 Where do I find the flatfile database underlying a methylDB?
You can easily find the underlying flatfile database (aka tabix file) using the getDBPath() function which prints the absolute location. Please note that starting is the generated when you decide to set a db.
6.0.13 Why does my methylBaseDB flatfile database has a different name now ?
In prior version the filename was just generated by comining sample-IDs, but this lead to unexpected errors. Starting from version 1.3.2 of methylKit we changed the filename pattern of the methylBaseDB and methylDiffDB database files to “methylBase_suffix.txt.bgz”/“methylDiff_suffix.txt.bgz”, where suffix is either a self-defined string given by the suffix argument or a random-string.
6.0.14 How can I make a bigwig file from methylKit result?
You can use the rtacklayer package, it should be able to convert GRanges objects to bigWig.
6.0.15 My data comes from MIRA-seq, can I use methylKit to perform the differential
methylation analysis and its annotation?
The package methylKit is designed for analysing methylation data from bisulfite sequencing (such as WGBS or RRBS) and as such not designed for affinity based method (like MIRA-Seq, MeDIP), wich produce other type of signal, more like that of ChiP-Seq. The Developers of MIRA-Seq protocol suggest the MEDIPS package to analyse their data.
6.0.16 My data comes from Methylation arrays, can I use methylKit to analyse my data ?
The package methylKit is designed for analysing methylation data from bisulfite sequencing (such as WGBS or RRBS) and as such does not provide any preprocessing methods required for array based methods (like Illumina Methylation arrays (27K, 450k or EPIC (850k))), please check Bioconductor (https://bioconductor.org/packages/release/BiocViews.html#___MethylationArray) for more suitable package to perform these steps. You could theoretically use methylKit to perform downstream analysis but this would require constructing a table that mimics our expected count based format and is not officially supported.
6.0.17 How can I analyze data generated from a local alignment ?
You cannot use the function processBismarkAln() to extract methylation calls, but you have to use methylDackel or Bismark methylation extractor to generate input files for methylKit.
The reason why we were not dealing with this directly is described here: https://sequencing.qcfail.com/articles/soft-clipping
6.0.18 How can I analyze data generated from a spliced alignment ?
We currently do not fully support spliced alignments generate with Bismarks’s HISAT2 mode, but you can extract methylation calls with methylDackel or Bismark methylation extractor to generate input files for methylKit.
6.0.19 Why does the regionCount function not keep the input region order?
The given regions (Granges/GrangesList object) will be orderd based on chromosome and position before searching for overlaps, so the resulting methylKit object might have a different ording than expected. We are doing this is to ensure that resulting output is consistent for in-memory and database based objects, as database based objects always have to be sorted to enable tabix indexing and providing fast random access.
If you to still want get a custom ordering of the output regions you can order the single regions in any object by providing your indices to the select or extract functions.
## methylDiff object sorted by chromosome and position
myDiff## chr start end strand pvalue qvalue meth.diff
## 1 chr21 9853296 9853296 + 9.908142e-03 2.156581e-02 -7.01210654
## 2 chr21 9853326 9853326 + 9.473546e-01 5.921730e-01 0.20913594
## 3 chr21 9860126 9860126 + 4.160449e-02 6.978084e-02 -4.11158117
## 4 chr21 9906604 9906604 + 2.106517e-01 2.594531e-01 -7.34636872
## 5 chr21 9906616 9906616 + 1.576498e-03 4.322201e-03 18.81750466
## 6 chr21 9906619 9906619 + 2.824594e-03 7.321638e-03 14.19373165
## 7 chr21 9906634 9906634 + 3.432652e-02 5.926876e-02 13.88888889
## 8 chr21 9906640 9906640 + 6.469598e-04 1.972925e-03 11.17318436
## 9 chr21 9906644 9906644 + 7.711669e-01 5.921730e-01 -1.40550239
## 10 chr21 9906676 9906676 + 2.399584e-01 2.850819e-01 -4.33048433
## 11 chr21 9906681 9906681 + 2.324256e-08 2.172846e-07 44.26807760
## 12 chr21 9906694 9906694 + 2.983628e-04 1.006776e-03 28.85090218
## 13 chr21 9906700 9906700 + 8.405880e-06 4.097059e-05 40.28016009
## 14 chr21 9906714 9906714 + 2.345839e-01 2.804495e-01 -9.88142292
## 15 chr21 9906846 9906846 + 1.485231e-01 1.997574e-01 6.28992629
## 16 chr21 9906864 9906864 + 1.555091e-01 2.071703e-01 13.33333333
## 17 chr21 9906869 9906869 + 7.859689e-04 2.334420e-03 18.51851852
## 18 chr21 9906873 9906873 + 4.976364e-06 2.603518e-05 47.62876084
## 19 chr21 9906884 9906884 + 6.770975e-01 5.921730e-01 4.15584416
## 20 chr21 9906889 9906889 + 1.121225e-02 2.412804e-02 11.76470588
## 21 chr21 9913543 9913543 + 1.254379e-02 2.632641e-02 -13.34310850
## 22 chr21 9913575 9913575 + 2.755448e-01 3.161628e-01 -5.44262295
## 23 chr21 9927527 9927527 + 1.120126e-07 9.257475e-07 -46.10983982
## 24 chr21 9944505 9944505 + 7.907909e-20 7.515975e-18 -51.01794341
## 25 chr21 9944663 9944663 - 1.790779e-05 7.678302e-05 -28.09991079
## 26 chr21 9959407 9959407 + 4.752177e-09 4.839266e-08 -36.31239936
## 27 chr21 9959541 9959541 - 4.733460e-07 3.145107e-06 -33.55957768
## 28 chr21 9959569 9959569 - 1.867858e-02 3.702161e-02 -10.62298337
## 29 chr21 9959577 9959577 - 5.170987e-01 4.922906e-01 -2.08488459
## 30 chr21 9959644 9959644 - 6.406095e-06 3.291132e-05 -30.96045198
## 31 chr21 9959650 9959650 - 1.521958e-05 6.575118e-05 -28.31442751
## 32 chr21 9967634 9967634 - 3.081873e-04 1.027764e-03 -25.86255790
## 33 chr21 10011833 10011833 + 2.453779e-04 8.543092e-04 10.59027778
## 34 chr21 10011841 10011841 + 1.907779e-14 6.057727e-13 46.67050470
## 35 chr21 10011855 10011855 + 3.292371e-10 4.579308e-09 22.64150943
## 36 chr21 10011858 10011858 + 7.585884e-01 5.921730e-01 2.04081633
## 37 chr21 10011861 10011861 + 8.445194e-09 8.162676e-08 41.19746233
## 38 chr21 10011872 10011872 + 3.821215e-04 1.238123e-03 16.47664211
## 39 chr21 10011876 10011876 + 4.779984e-05 1.933224e-04 24.36576841
## 40 chr21 10011878 10011878 + 9.176517e-05 3.488683e-04 24.69387755
## 41 chr21 10011925 10011925 - 2.456880e-04 8.543092e-04 24.09525188
## 42 chr21 10011938 10011938 - 1.912329e-02 3.773460e-02 8.53566959
## 43 chr21 10011954 10011954 - 5.680842e-10 7.042547e-09 28.89423077
## 44 chr21 10011964 10011964 - 4.537417e-13 9.583406e-12 43.21110590
## 45 chr21 10011974 10011974 - 5.034449e-05 2.021802e-04 23.76991565
## 46 chr21 10011987 10011987 + 3.383682e-02 5.882888e-02 8.03960396
## 47 chr21 10011990 10011990 + 6.044847e-05 2.377345e-04 26.68552570
## 48 chr21 10012008 10012008 + 9.083844e-01 5.921730e-01 0.88126160
## 49 chr21 10012030 10012030 + 8.551484e-02 1.256854e-01 11.85605801
## 50 chr21 10012069 10012069 + 7.376551e-10 8.826212e-09 30.81481481
## 51 chr21 10012075 10012075 + 2.296300e-05 9.628634e-05 17.29629630
## 52 chr21 10012079 10012079 + 1.325366e-07 1.049731e-06 26.61691542
## 53 chr21 10012089 10012089 + 6.797159e-02 1.047612e-01 9.56442278
## 54 chr21 10012095 10012095 + 9.125016e-02 1.324085e-01 5.72646969
## 55 chr21 10012101 10012101 + 8.651864e-16 4.111529e-14 39.80782430
## 56 chr21 10012696 10012696 + 2.253460e-03 6.033165e-03 9.68498168
## 57 chr21 10012699 10012699 + 1.782895e-09 1.955228e-08 44.70329670
## 58 chr21 10012876 10012876 + 4.251160e-01 4.224084e-01 3.88751034
## 59 chr21 10012881 10012881 + 1.000000e+00 5.921730e-01 0.00000000
## 60 chr21 10012883 10012883 + 4.287026e-01 4.251706e-01 3.75000000
## 61 chr21 10012887 10012887 + 1.645398e-02 3.315580e-02 20.80792683
## 62 chr21 10012891 10012891 + 8.591231e-01 5.921730e-01 0.68597561
## 63 chr21 10012894 10012894 + 3.682649e-01 3.811392e-01 8.73328088
## 64 chr21 10012934 10012934 - 1.794638e-07 1.346599e-06 37.08791209
## 65 chr21 10012938 10012938 - 1.767536e-04 6.379491e-04 21.01449275
## 66 chr21 10012940 10012940 - 1.364114e-04 5.117783e-04 18.81942241
## 67 chr21 10012942 10012942 - 1.652853e-13 4.098088e-12 47.61555393
## 68 chr21 10012944 10012944 - 1.238799e-07 1.009201e-06 36.00782779
## 69 chr21 10012947 10012947 - 9.538162e-01 5.921730e-01 0.18552876
## 70 chr21 10022131 10022131 - 9.421034e-01 5.921730e-01 -0.69686411
## 71 chr21 10022157 10022157 - 4.316338e-03 1.065561e-02 -26.10275039
## 72 chr21 10022165 10022165 - 6.042468e-02 9.571650e-02 13.12921640
## 73 chr21 10065022 10065022 + 2.191397e-01 2.670239e-01 3.47945205
## 74 chr21 10065061 10065061 + 1.632724e-01 2.145349e-01 -10.58855886
## 75 chr21 10077699 10077699 + 2.570414e-15 1.127547e-13 -43.48105331
## 76 chr21 10077705 10077705 + 1.331087e-06 7.745606e-06 -27.59568018
## 77 chr21 10077904 10077904 - 5.831807e-01 5.329586e-01 -5.20581114
## 78 chr21 10086785 10086785 + 6.070256e-03 1.395823e-02 -17.61615044
## 79 chr21 10087014 10087014 - 3.910017e-01 3.960455e-01 -11.90476190
## 80 chr21 10099942 10099942 + 8.117156e-01 5.921730e-01 1.79211470
## 81 chr21 10099950 10099950 + 3.907632e-03 9.748659e-03 -25.26881720
## 82 chr21 10114305 10114305 - 6.285214e-04 1.937418e-03 -28.60855658
## 83 chr21 10114343 10114343 - 3.318919e-01 3.632736e-01 8.03921569
## 84 chr21 10114345 10114345 - 8.064166e-02 1.191371e-01 14.65116279
## 85 chr21 10114351 10114351 - 2.484244e-07 1.770840e-06 -27.17054264
## 86 chr21 10120564 10120564 - 9.001105e-01 5.921730e-01 1.06227106
## 87 chr21 10120584 10120584 - 9.451814e-02 1.361115e-01 -13.39506173
## 88 chr21 10120599 10120599 + 1.808342e-01 2.307002e-01 -3.78151261
## 89 chr21 10120600 10120600 - 4.624398e-02 7.577935e-02 -12.07317073
## 90 chr21 10120603 10120603 + 3.438372e-01 3.692608e-01 -4.26470588
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## 192 chr21 10186541 10186541 - 1.929664e-22 3.668051e-20 -52.06168831
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## 195 chr21 10190516 10190516 - 5.857299e-07 3.839309e-06 -13.89785068
## 196 chr21 10190541 10190541 + 4.592617e-03 1.109745e-02 14.43001443
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## 201 chr21 10190611 10190611 - 4.464488e-02 7.358182e-02 -3.12500000
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## 205 chr21 10190630 10190630 - 2.009683e-02 3.906031e-02 -8.25242718
## 206 chr21 10190633 10190633 + 6.036893e-03 1.393771e-02 21.36899626
## 207 chr21 10190658 10190658 + 1.091368e-01 1.532923e-01 8.89824561
## 208 chr21 10190669 10190669 + 1.592820e-04 5.898203e-04 21.84873950
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## 213 chr21 10190924 10190924 + 2.587702e-02 4.684666e-02 8.49077823
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## 217 chr21 10190937 10190937 - 1.416968e-01 1.914795e-01 -3.41264119
## 218 chr21 10190944 10190944 - 5.213333e-03 1.238737e-02 -5.01649414
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## 221 chr21 10191408 10191408 - 4.743064e-07 3.145107e-06 -28.74762513
## 222 chr21 10191430 10191430 - 1.245688e-14 4.735796e-13 -43.97304236
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## 225 chr21 10191773 10191773 + 8.182003e-01 5.921730e-01 2.09176788
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## 229 chr21 10191824 10191824 - 6.714069e-01 5.921730e-01 -1.71324183
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## 231 chr21 10191838 10191838 - 3.154906e-01 3.513374e-01 3.42983388
## 232 chr21 10191851 10191851 - 3.957467e-05 1.611997e-04 -13.06577129
## 233 chr21 10191878 10191878 + 5.461516e-04 1.692662e-03 10.53302979
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## 235 chr21 10191900 10191900 - 1.431176e-03 4.020424e-03 -10.97592484
## 236 chr21 10191906 10191906 + 4.685113e-02 7.633557e-02 10.07069524
## 237 chr21 10191907 10191907 - 1.046243e-01 1.484162e-01 7.92335116
## 238 chr21 10191923 10191923 + 8.574910e-04 2.494873e-03 16.57537937
## 239 chr21 10191924 10191924 - 3.301331e-02 5.792291e-02 10.06705602
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## 243 chr21 10192322 10192322 + 2.089803e-02 3.990415e-02 -9.34459805
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## 245 chr21 13282440 13282440 + 1.696739e-10 2.418967e-09 -25.57837487
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## 251 chr21 13306339 13306339 - 1.847643e-01 2.341426e-01 -2.94117647
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## 255 chr21 13664889 13664889 - 6.902971e-36 3.936507e-33 -34.91195089
## 256 chr21 13666397 13666397 - 6.433579e-07 4.122281e-06 -15.58076070
## 257 chr21 13666553 13666553 - 8.703844e-07 5.337072e-06 -30.97841323
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## 259 chr21 13673740 13673740 + 8.005441e-01 5.921730e-01 0.90264346
## 260 chr21 13673754 13673754 + 3.184396e-04 1.055780e-03 33.91360413
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## 263 chr21 13958077 13958077 - 5.453167e-02 8.735217e-02 -11.46245059
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## 265 chr21 13958094 13958094 - 3.577641e-03 9.067533e-03 -22.64392324
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## 267 chr21 13958266 13958266 - 3.540372e-13 8.213146e-12 -50.98220904
## 268 chr21 13989277 13989277 + 3.721873e-01 3.837525e-01 2.62626263
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## 270 chr21 13989284 13989284 + 5.959555e-01 5.411642e-01 2.46687054
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## 272 chr21 13989320 13989320 + 6.422966e-01 5.750043e-01 -1.95261079
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## 274 chr21 13989387 13989387 - 1.872218e-01 2.362071e-01 9.82456140
## 275 chr21 13989431 13989431 + 1.926153e-01 2.419412e-01 5.06379585
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## 278 chr21 13989449 13989449 - 3.871456e-01 3.941121e-01 5.19480519
## 279 chr21 13989451 13989451 + 3.785720e-01 3.868915e-01 7.83972125
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## 284 chr21 13990130 13990130 + 1.164282e-03 3.336415e-03 -10.25641026
## 285 chr21 13990159 13990159 + 6.725782e-01 5.921730e-01 1.41700405
## 286 chr21 13990171 13990171 + 1.671651e-01 2.188217e-01 -3.81241565
## 287 chr21 13990874 13990874 - 9.090313e-02 1.322415e-01 14.14835165
## 288 chr21 13991068 13991068 + 2.043710e-05 8.632973e-05 37.05882353
## 289 chr21 13991085 13991085 + 4.322154e-07 2.934241e-06 50.36764706
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## 291 chr21 13991229 13991229 - 2.811169e-01 3.212634e-01 -9.73118280
## 292 chr21 13991248 13991248 - 1.396415e-02 2.885230e-02 14.04761905
## 293 chr21 13998967 13998967 + 2.458575e-01 2.896763e-01 11.32075472
## 294 chr21 13998987 13998987 + 4.908427e-10 6.220205e-09 56.69934641
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## 298 chr21 13999023 13999023 - 5.482443e-11 8.227454e-10 50.03030303
## 299 chr21 13999031 13999031 - 3.914763e-03 9.748659e-03 24.79069767
## 300 chr21 13999039 13999039 - 4.315382e-02 7.153782e-02 14.80986334
## 301 chr21 13999041 13999041 - 1.293780e-01 1.781908e-01 -12.55131965
## 302 chr21 13999059 13999059 - 6.595164e-05 2.576011e-04 31.16883117
## 303 chr21 13999082 13999082 + 9.362046e-12 1.483007e-10 55.57603687
## 304 chr21 13999089 13999089 + 7.429177e-10 8.826212e-09 56.52482270
## 305 chr21 13999095 13999095 + 2.136550e-06 1.206331e-05 33.74513841
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## 307 chr21 13999107 13999107 + 6.254835e-09 6.149825e-08 53.94646534
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## 313 chr21 13999168 13999168 - 5.645511e-01 5.209424e-01 4.42725124
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## 320 chr21 13999239 13999239 - 2.943129e-09 3.166711e-08 55.01474926
## 321 chr21 13999247 13999247 - 6.838482e-08 5.820494e-07 50.68965517
## 322 chr21 13999249 13999249 - 3.494649e-10 4.744923e-09 56.89655172
## 323 chr21 13999252 13999252 - 1.185429e-05 5.407178e-05 28.19514430
## 324 chr21 13999256 13999256 - 1.119749e-07 9.257475e-07 41.49178255
## 325 chr21 13999263 13999263 - 7.840586e-10 9.124884e-09 39.97175141
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## 327 chr21 13999547 13999547 + 6.135931e-03 1.405258e-02 22.97154900
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## 329 chr21 13999561 13999561 + 7.701026e-03 1.702173e-02 26.64233577
## 330 chr21 13999562 13999562 - 3.117343e-03 7.969354e-03 25.53956835
## 331 chr21 13999566 13999566 + 1.857595e-06 1.059317e-05 38.44444444
## 332 chr21 13999567 13999567 - 3.062075e-07 2.103840e-06 28.65655471
## 333 chr21 13999568 13999568 + 3.058702e-06 1.693460e-05 42.57854821
## 334 chr21 13999569 13999569 - 3.219523e-05 1.321723e-04 22.18309859
## 335 chr21 13999574 13999574 + 6.832975e-07 4.329545e-06 44.42586399
## 336 chr21 13999575 13999575 - 1.263860e-05 5.630720e-05 32.65068265
## 337 chr21 13999588 13999588 - 3.776379e-01 3.866330e-01 3.96825397
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## 339 chr21 13999971 13999971 + 3.373566e-02 5.882888e-02 15.83229036
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## 342 chr21 14018027 14018027 + 8.110882e-04 2.384192e-03 23.69925672
## 343 chr21 14018046 14018046 + 7.013869e-03 1.576451e-02 26.16421569
## 344 chr21 14018050 14018050 + 1.547250e-12 3.042547e-11 65.58033162
## 345 chr21 14018053 14018053 + 1.951332e-02 3.823958e-02 23.45238095
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## 347 chr21 14018224 14018224 + 4.334688e-01 4.291512e-01 -4.76190476
## 348 chr21 14018261 14018261 + 1.891295e-05 8.048769e-05 41.54704944
## 349 chr21 14018268 14018268 + 2.005259e-02 3.906031e-02 22.75432900
## 350 chr21 14057234 14057234 + 3.973326e-10 5.269394e-09 -44.28763441
## 351 chr21 14057892 14057892 - 5.911215e-03 1.375896e-02 -14.32803663
## 352 chr21 14057932 14057932 - 1.384738e-03 3.909230e-03 -12.50000000
## 353 chr21 14071266 14071266 + 3.747492e-02 6.398367e-02 -9.52380952
## 354 chr21 14071286 14071286 + 5.193290e-01 4.923835e-01 -7.51879699
## 355 chr21 14120949 14120949 + 7.869016e-01 5.921730e-01 1.83901919
## 356 chr21 14121557 14121557 - 1.065049e-01 1.501868e-01 -3.22580645
## 357 chr21 14121571 14121571 - 9.784953e-05 3.695360e-04 -17.74193548
## 358 chr21 14229195 14229195 - 6.733777e-02 1.040656e-01 -7.40740741
## 359 chr21 14229198 14229198 - 8.002022e-03 1.755098e-02 -15.09433962
## 360 chr21 14229209 14229209 - 8.653371e-02 1.268559e-01 8.86243386
## 361 chr21 14229227 14229227 - 2.858745e-02 5.126526e-02 25.26455026
## 362 chr21 14229238 14229238 - 6.518693e-02 1.024493e-01 -7.54716981
## 363 chr21 14229946 14229946 - 2.329051e-01 2.790275e-01 -2.43902439
## 364 chr21 14229948 14229948 - 2.420052e-01 2.869158e-01 7.02671312
## 365 chr21 14230187 14230187 - 3.397112e-01 3.675988e-01 -7.12074303
## 366 chr21 14230216 14230216 - 3.978291e-04 1.281735e-03 -19.60784314
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## 369 chr21 14230660 14230660 - 4.090739e-04 1.310559e-03 -11.42857143
## 370 chr21 14230665 14230665 - 8.308578e-06 4.097059e-05 -21.90635452
## 371 chr21 14230669 14230669 - 6.359469e-04 1.949767e-03 -16.30893414
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## 373 chr21 14275564 14275564 - 5.888965e-03 1.375896e-02 -17.14285714
## 374 chr21 14275582 14275582 - 4.091694e-01 4.113649e-01 -0.96153846
## 375 chr21 14280366 14280366 - 5.726599e-03 1.343895e-02 -12.89738431
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## 381 chr21 14348846 14348846 - 3.616045e-01 3.786809e-01 -3.86904762
## 382 chr21 14348848 14348848 + 1.011837e-01 1.446147e-01 -4.16666667
## 383 chr21 14348849 14348849 - 3.351927e-01 3.654835e-01 6.28571429
## 384 chr21 14348893 14348893 - 2.123333e-01 2.609607e-01 2.32558140
## 385 chr21 14350317 14350317 + 2.128786e-02 4.006492e-02 7.69230769
## 386 chr21 14352384 14352384 + 7.202037e-02 1.097147e-01 -2.67857143
## 387 chr21 14357926 14357926 + 3.180191e-01 3.521444e-01 -1.84746877
## 388 chr21 14357942 14357942 + 2.106857e-02 3.999918e-02 5.99206349
## 389 chr21 14357945 14357945 + 2.578184e-01 2.997817e-01 -3.29822616
## 390 chr21 14357956 14357956 + 3.382719e-01 3.667373e-01 -0.79365079
## 391 chr21 14357962 14357962 + 9.925507e-01 5.921730e-01 -0.03424658
## 392 chr21 14358042 14358042 - 2.717090e-04 9.334067e-04 14.53012343
## 393 chr21 14358047 14358047 - 7.214150e-01 5.921730e-01 1.30317187
## 394 chr21 14358078 14358078 - 7.504092e-01 5.921730e-01 1.15151515
## 395 chr21 14358155 14358155 - 7.577618e-01 5.921730e-01 0.95108696
## 396 chr21 14358179 14358179 - 2.018062e-03 5.454149e-03 27.05696203
## 397 chr21 14358181 14358181 - 9.875979e-01 5.921730e-01 -0.05737235
## 398 chr21 14358186 14358186 - 3.203501e-02 5.655842e-02 8.65171650
## 399 chr21 14358196 14358196 - 5.197866e-01 4.923835e-01 -2.59740260
## 400 chr21 14358203 14358203 - 1.702171e-01 2.210961e-01 -2.38095238
## 401 chr21 14358487 14358487 + 6.611117e-02 1.030075e-01 -4.09836066
## 402 chr21 14358528 14358528 + 1.385449e-01 1.876651e-01 -5.25806452
## 403 chr21 14358534 14358534 + 1.181382e-02 2.504454e-02 -7.31707317
## 404 chr21 14358577 14358577 - 1.296756e-01 1.781908e-01 -6.25000000
## 405 chr21 14358585 14358585 - 5.374421e-02 8.633327e-02 11.68831169
## 406 chr21 14358592 14358592 - 1.195513e-02 2.525023e-02 9.52380952
## 407 chr21 14358604 14358604 + 8.090667e-01 5.921730e-01 0.44398907
## 408 chr21 14358612 14358612 + 7.422303e-12 1.244901e-10 43.46598203
## 409 chr21 14358635 14358635 + 1.616505e-01 2.128943e-01 4.30291053
## 410 chr21 14358648 14358648 + 4.781315e-01 4.613546e-01 1.61341348
## 411 chr21 14358651 14358651 + 1.372691e-01 1.863796e-01 -1.73913043
## 412 chr21 14364879 14364879 + 9.550781e-02 1.371903e-01 4.06329114
## 413 chr21 14364882 14364882 + 1.000000e+00 5.921730e-01 0.00000000
## 414 chr21 14364894 14364894 + 2.014605e-01 2.519417e-01 1.26582278
## 415 chr21 14364896 14364896 + 2.796340e-01 3.202105e-01 -1.00000000
## 416 chr21 14364917 14364917 + 2.458118e-02 4.521848e-02 7.81833860
## 417 chr21 14364979 14364979 - 7.522225e-01 5.921730e-01 -1.55172414
## 418 chr21 14364981 14364981 - 7.090401e-01 5.921730e-01 -1.77419355
## 419 chr21 14364995 14364995 - 6.560105e-02 1.024927e-01 6.45161290
## 420 chr21 14365015 14365015 - 2.749261e-01 3.160888e-01 -2.43902439
## 421 chr21 14365019 14365019 - 1.208236e-01 1.676428e-01 -4.87804878
## 422 chr21 14365025 14365025 - 2.529706e-02 4.608936e-02 9.09090909
## 423 chr21 14365260 14365260 - 1.691453e-02 3.396383e-02 -7.69230769
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## 427 chr21 14365683 14365683 + 4.762575e-01 4.603252e-01 3.65990991
## 428 chr21 14365688 14365688 + 5.813392e-01 5.325862e-01 2.27338968
## 429 chr21 14365696 14365696 + 1.125555e-02 2.413015e-02 -8.82352941
## 430 chr21 14365748 14365748 - 4.621706e-01 4.512990e-01 2.30508475
## 431 chr21 14365755 14365755 - 7.393767e-02 1.109576e-01 8.72131148
## 432 chr21 14365777 14365777 - 4.569488e-01 4.469654e-01 2.23325062
## 433 chr21 14365791 14365791 - 2.559650e-01 2.991133e-01 4.82539683
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## 435 chr21 14366216 14366216 - 5.796656e-01 5.323054e-01 -2.32379980
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## 437 chr21 14366254 14366254 - 7.278906e-03 1.621454e-02 -7.14285714
## 438 chr21 14366580 14366580 + 1.032561e-01 1.472078e-01 -4.41176471
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## 440 chr21 14366605 14366605 + 3.637008e-01 3.786809e-01 -1.40845070
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## 442 chr21 14366625 14366625 + 1.000000e+00 5.921730e-01 0.00000000
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## 444 chr21 14366972 14366972 + 1.000000e+00 5.921730e-01 0.00000000
## 445 chr21 14367008 14367008 + 8.043413e-02 1.191371e-01 8.62068966
## 446 chr21 14367010 14367010 + 4.815606e-01 4.638784e-01 3.44827586
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## 449 chr21 14367045 14367045 - 2.077689e-01 2.576874e-01 -6.43382353
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## 453 chr21 14367207 14367207 + 2.823386e-01 3.220144e-01 -9.94152047
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## 455 chr21 14367220 14367220 + 4.228510e-01 4.215667e-01 -7.38045738
## 456 chr21 14367279 14367279 - 2.841869e-04 9.665835e-04 35.86956522
## 457 chr21 14367498 14367498 - 1.368987e-01 1.863203e-01 8.98165718
## 458 chr21 14367515 14367515 - 1.574286e-01 2.086719e-01 -3.63636364
## 459 chr21 14367517 14367517 - 3.306745e-01 3.626372e-01 3.86243386
## 460 chr21 14367534 14367534 - 1.000000e+00 5.921730e-01 0.00000000
## 461 chr21 14367621 14367621 - 4.837883e-01 4.652385e-01 -3.79176061
## 462 chr21 14367628 14367628 - 3.805528e-03 9.602434e-03 -7.14285714
## 463 chr21 14367702 14367702 - 1.426824e-01 1.923557e-01 -4.34782609
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## 471 chr21 14367979 14367979 - 2.298593e-01 2.759583e-01 2.22222222
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## 474 chr21 14368438 14368438 + 1.922439e-01 2.419412e-01 -7.87247885
## 475 chr21 14368445 14368445 + 6.684812e-01 5.919407e-01 -2.27629513
## 476 chr21 14368475 14368475 - 3.429142e-01 3.690515e-01 -2.43902439
## 477 chr21 14368481 14368481 - 9.062529e-01 5.921730e-01 -0.65217391
## 478 chr21 14368490 14368490 - 1.776386e-01 2.271315e-01 -4.87804878
## 479 chr21 14370025 14370025 + 4.615926e-02 7.577935e-02 9.82905983
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## 481 chr21 14370153 14370153 - 6.026360e-03 1.393771e-02 -5.81395349
## 482 chr21 14370169 14370169 - 6.955248e-03 1.573936e-02 6.77853404
## 483 chr21 14370181 14370181 - 3.554025e-08 3.217028e-07 13.92554706
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## 487 chr21 14370318 14370318 + 3.462032e-01 3.704066e-01 -4.83516484
## 488 chr21 14370352 14370352 + 2.215205e-01 2.687763e-01 3.12500000
## 489 chr21 14370353 14370353 - 2.940447e-01 3.320449e-01 4.01937046
## 490 chr21 14370359 14370359 + 2.658902e-01 3.075603e-01 6.23376623
## 491 chr21 14370360 14370360 - 3.360973e-01 3.657705e-01 -1.66666667
## 492 chr21 14370376 14370376 - 1.000000e+00 5.921730e-01 0.00000000
## 493 chr21 14370415 14370415 + 1.000000e+00 5.921730e-01 0.00000000
## 494 chr21 14371668 14371668 + 6.853396e-04 2.067850e-03 -19.04761905
## 495 chr21 14371678 14371678 + 3.537950e-02 6.076990e-02 7.50000000
## 496 chr21 14371698 14371698 + 9.409132e-01 5.921730e-01 0.34883721
## 497 chr21 14374515 14374515 + 1.170430e-02 2.490494e-02 9.55045341
## 498 chr21 14374542 14374542 + 7.297200e-02 1.103798e-01 5.94449047
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## 500 chr21 14374686 14374686 + 2.518555e-01 2.949153e-01 -1.58730159
## 501 chr21 14374689 14374689 + 1.944625e-01 2.437247e-01 5.44608648
## 502 chr21 14374698 14374698 + 1.673024e-01 2.188217e-01 7.58347482
## 503 chr21 14376417 14376417 - 3.187248e-01 3.522419e-01 0.42553191
## 504 chr21 14376428 14376428 - 3.077141e-01 3.447502e-01 0.52356021
## 505 chr21 14376609 14376609 - 1.732999e-01 2.233472e-01 -9.72850679
## 506 chr21 14376611 14376611 - 6.160904e-01 5.559072e-01 -2.72410091
## 507 chr21 14376644 14376644 - 2.438041e-01 2.884489e-01 -3.03030303
## 508 chr21 14377513 14377513 + 5.593419e-01 5.176294e-01 2.81524927
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## 510 chr21 14377533 14377533 - 7.113891e-01 5.921730e-01 2.77777778
## 511 chr21 14377537 14377537 + 5.489270e-02 8.768418e-02 -7.27272727
## 512 chr21 14377538 14377538 - 1.840774e-01 2.337917e-01 7.71324864
## 513 chr21 14377576 14377576 - 7.186383e-01 5.921730e-01 -1.81488203
## 514 chr21 14377582 14377582 - 8.463515e-01 5.921730e-01 0.81669691
## 515 chr21 14378318 14378318 + 3.817314e-01 3.894225e-01 -1.92307692
## 516 chr21 14378325 14378325 + 3.059253e-01 3.434208e-01 6.94444444
## 517 chr21 14379889 14379889 - 3.889199e-02 6.600788e-02 -5.47945205
## 518 chr21 14379907 14379907 - 7.289845e-02 1.103798e-01 -4.16666667
## 519 chr21 14510373 14510373 + 2.766860e-02 4.977403e-02 6.44920123
## 520 chr21 14510402 14510402 + 7.563307e-03 1.678238e-02 4.16666667
## 521 chr21 14510411 14510411 + 6.909918e-02 1.056426e-01 1.90114068
## 522 chr21 14510480 14510480 - 3.621756e-01 3.786809e-01 1.16279070
## 523 chr21 14510496 14510496 - 1.361778e-02 2.834201e-02 7.95454545
## 524 chr21 14510499 14510499 - 2.091250e-02 3.990415e-02 6.89655172
## 525 chr21 14510507 14510507 - 1.317040e-02 2.751130e-02 8.04597701
## 526 chr21 14510514 14510514 - 3.537787e-01 3.756923e-01 1.13636364
## 527 chr21 14510517 14510517 - 1.542478e-03 4.290819e-03 12.64367816
## 528 chr21 14510522 14510522 - 6.904584e-02 1.056426e-01 9.38104449
## 529 chr21 14568171 14568171 + 3.100519e-06 1.700106e-05 36.18421053
## 530 chr21 14568176 14568176 + 2.391222e-06 1.336887e-05 36.90476190
## 531 chr21 14568183 14568183 + 8.860660e-07 5.375429e-06 36.83181226
## 532 chr21 14568196 14568196 + 6.513790e-06 3.316581e-05 30.75862069
## 533 chr21 14568204 14568204 + 3.983973e-06 2.143312e-05 35.48850575
## 534 chr21 14568220 14568220 + 3.130374e-03 7.969354e-03 18.80701754
## 535 chr21 14677252 14677252 - 1.000000e+00 5.921730e-01 0.00000000
## 536 chr21 14677256 14677256 - 1.000000e+00 5.921730e-01 0.00000000
## 537 chr21 14677277 14677277 - 1.000000e+00 5.921730e-01 0.00000000
## 538 chr21 14677504 14677504 - 1.000000e+00 5.921730e-01 0.00000000
## 539 chr21 14677516 14677516 - 1.000000e+00 5.921730e-01 0.00000000
## 540 chr21 14677535 14677535 - 1.000000e+00 5.921730e-01 0.00000000
## 541 chr21 14677540 14677540 - 1.000000e+00 5.921730e-01 0.00000000
## 542 chr21 14677544 14677544 - 1.000000e+00 5.921730e-01 0.00000000
## 543 chr21 15358331 15358331 + 5.855625e-01 5.342791e-01 -1.21951220
## 544 chr21 15358368 15358368 + 1.000000e+00 5.921730e-01 0.00000000
## 545 chr21 15358374 15358374 + 1.000000e+00 5.921730e-01 0.00000000
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## 547 chr21 15358643 15358643 + 1.000000e+00 5.921730e-01 0.00000000
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## 549 chr21 15358658 15358658 + 1.000000e+00 5.921730e-01 0.00000000
## 550 chr21 15358659 15358659 - 1.000000e+00 5.921730e-01 0.00000000
## 551 chr21 15358661 15358661 + 1.000000e+00 5.921730e-01 0.00000000
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## 554 chr21 15358689 15358689 - 1.000000e+00 5.921730e-01 0.00000000
## 555 chr21 15358693 15358693 - 1.000000e+00 5.921730e-01 0.00000000
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## 578 chr21 15358896 15358896 + 5.278257e-01 4.958802e-01 0.75757576
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## 615 chr21 15447185 15447185 - 8.791070e-21 1.253305e-18 -64.52887538
## 616 chr21 15583554 15583554 - 6.521396e-02 1.024493e-01 3.50877193
## 617 chr21 15583576 15583576 - 4.365760e-03 1.068511e-02 -9.21052632
## 618 chr21 15583585 15583585 - 2.864089e-01 3.260045e-01 -1.33333333
## 619 chr21 15583673 15583673 - 5.278102e-04 1.644756e-03 -37.46081505
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## 623 chr21 15584560 15584560 - 5.387495e-01 5.020077e-01 -4.04483431
## 624 chr21 15584596 15584596 - 6.553698e-02 1.024927e-01 -9.90516333
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## 683 chr21 16024428 16024428 - 1.577126e-01 2.086719e-01 2.32558140
## 684 chr21 16024431 16024431 + 1.000000e+00 5.921730e-01 0.00000000
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## 714 chr21 16713387 16713387 + 5.350870e-01 5.005928e-01 -5.74162679
## 715 chr21 16713421 16713421 + 8.859389e-01 5.921730e-01 0.95238095
## 716 chr21 16713423 16713423 + 2.135037e-01 2.612729e-01 8.38095238
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## 718 chr21 16869844 16869844 + 5.871720e-01 5.348918e-01 -2.04761905
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## 720 chr21 16869890 16869890 + 3.492257e-09 3.687970e-08 -34.69597070
## 721 chr21 17043261 17043261 + 2.282202e-01 2.751490e-01 2.63157895
## 722 chr21 17043269 17043269 + 2.456291e-01 2.896763e-01 -2.50000000
## 723 chr21 17385151 17385151 - 2.021349e-01 2.522319e-01 1.67339925
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## 725 chr21 17419551 17419551 - 4.885317e-10 6.220205e-09 -41.89482834
## 726 chr21 17719707 17719707 - 3.557065e-01 3.770374e-01 -3.53271778
## 727 chr21 17719743 17719743 - 2.023747e-02 3.912092e-02 -7.40740741
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## 730 chr21 17731709 17731709 + 4.978378e-03 1.192850e-02 -21.46718147
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## 732 chr21 17803088 17803088 - 2.701418e-12 4.969413e-11 -56.96202532
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## 760 chr21 17832330 17832330 - 5.461083e-01 5.072071e-01 3.24324324
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## 784 chr21 17906722 17906722 - 3.605992e-01 3.786809e-01 0.68027211
## 785 chr21 17906725 17906725 - 3.605992e-01 3.786809e-01 0.68027211
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## 802 chr21 17907011 17907011 + 1.000000e+00 5.921730e-01 0.00000000
## 803 chr21 17907012 17907012 - 5.600531e-01 5.176294e-01 0.59523810
## 804 chr21 17907014 17907014 + 1.000000e+00 5.921730e-01 0.00000000
## 805 chr21 17907016 17907016 + 1.000000e+00 5.921730e-01 0.00000000
## 806 chr21 17907020 17907020 + 1.000000e+00 5.921730e-01 0.00000000
## 807 chr21 17907027 17907027 + 1.000000e+00 5.921730e-01 0.00000000
## 808 chr21 17907029 17907029 + 1.000000e+00 5.921730e-01 0.00000000
## 809 chr21 17907032 17907032 + 1.000000e+00 5.921730e-01 0.00000000
## 810 chr21 17907035 17907035 + 1.000000e+00 5.921730e-01 0.00000000
## 811 chr21 17907039 17907039 + 1.000000e+00 5.921730e-01 0.00000000
## 812 chr21 17907047 17907047 + 3.454141e-01 3.702571e-01 0.83333333
## 813 chr21 17907049 17907049 + 1.000000e+00 5.921730e-01 0.00000000
## 814 chr21 17907051 17907051 + 1.000000e+00 5.921730e-01 0.00000000
## 815 chr21 17907087 17907087 - 1.000000e+00 5.921730e-01 0.00000000
## 816 chr21 17907095 17907095 - 1.000000e+00 5.921730e-01 0.00000000
## 817 chr21 17907108 17907108 - 1.000000e+00 5.921730e-01 0.00000000
## 818 chr21 17907111 17907111 - 1.000000e+00 5.921730e-01 0.00000000
## 819 chr21 17907113 17907113 - 1.000000e+00 5.921730e-01 0.00000000
## 820 chr21 17907124 17907124 - 1.000000e+00 5.921730e-01 0.00000000
## 821 chr21 17907126 17907126 - 1.000000e+00 5.921730e-01 0.00000000
## 822 chr21 17907131 17907131 - 3.569557e-01 3.771534e-01 1.03092784
## 823 chr21 17907306 17907306 - 1.000000e+00 5.921730e-01 0.00000000
## 824 chr21 17907310 17907310 - 4.491272e-01 4.415870e-01 1.00000000
## 825 chr21 17907312 17907312 - 1.000000e+00 5.921730e-01 0.00000000
## 826 chr21 17907315 17907315 - 1.000000e+00 5.921730e-01 0.00000000
## 827 chr21 17907318 17907318 - 1.000000e+00 5.921730e-01 0.00000000
## 828 chr21 17907320 17907320 - 1.000000e+00 5.921730e-01 0.00000000
## 829 chr21 17907325 17907325 - 1.000000e+00 5.921730e-01 0.00000000
## 830 chr21 17907333 17907333 - 1.000000e+00 5.921730e-01 0.00000000
## 831 chr21 17907335 17907335 - 1.000000e+00 5.921730e-01 0.00000000
## 832 chr21 17907349 17907349 - 1.000000e+00 5.921730e-01 0.00000000
## 833 chr21 17907387 17907387 + 2.419634e-02 4.465459e-02 -4.76190476
## 834 chr21 17907391 17907391 + 2.419634e-02 4.465459e-02 -4.76190476
## 835 chr21 17907505 17907505 - 1.222150e-03 3.484732e-03 -16.70834559
## 836 chr21 17907513 17907513 - 2.300612e-03 6.130621e-03 -14.87809515
## 837 chr21 17907540 17907540 - 1.690179e-04 6.139147e-04 -9.68017162
## 838 chr21 17907732 17907732 - 1.337656e-17 8.475726e-16 -73.62016515
## 839 chr21 17907754 17907754 - 1.869255e-07 1.366623e-06 -46.81706500
## 840 chr21 17983314 17983314 + 1.018308e-03 2.932845e-03 -28.98206751
## 841 chr21 17988383 17988383 + 3.155696e-02 5.606154e-02 13.50000000
## 842 chr21 17988393 17988393 + 6.781834e-01 5.921730e-01 3.00000000
## 843 chr21 17988405 17988405 + 4.578918e-03 1.109745e-02 -29.79487179
## 844 chr21 18038986 18038986 + 9.050132e-01 5.921730e-01 -0.35560547
## 845 chr21 18038990 18038990 + 1.728995e-02 3.459584e-02 8.79655624
## 846 chr21 18038995 18038995 + 5.663180e-01 5.213032e-01 2.50712251
## 847 chr21 18039018 18039018 + 1.537521e-01 2.063036e-01 8.79142300
## 848 chr21 18052003 18052003 - 1.858408e-01 2.349846e-01 -8.96296296
## 849 chr21 18052040 18052040 + 1.765646e-01 2.262656e-01 -6.37007078
## 850 chr21 18052041 18052041 - 3.732411e-01 3.837525e-01 -1.78571429
## 851 chr21 18052058 18052058 + 9.492917e-01 5.921730e-01 0.19792182
## 852 chr21 18052081 18052081 + 6.373381e-01 5.714624e-01 -1.84782609
## 853 chr21 18052172 18052172 - 2.350712e-02 4.395157e-02 -5.26315789
## 854 chr21 18052188 18052188 - 7.317306e-01 5.921730e-01 -1.74288179
## 855 chr21 18052194 18052194 - 6.942585e-01 5.921730e-01 -1.47707980
## 856 chr21 18113293 18113293 + 6.657986e-02 1.034551e-01 5.55555556
## 857 chr21 18113300 18113300 + 4.165108e-01 4.167027e-01 1.11111111
## 858 chr21 18113303 18113303 + 1.000000e+00 5.921730e-01 0.00000000
## 859 chr21 18113312 18113312 + 1.000000e+00 5.921730e-01 0.00000000
## 860 chr21 18113314 18113314 + 1.000000e+00 5.921730e-01 0.00000000
## 861 chr21 18113316 18113316 + 1.000000e+00 5.921730e-01 0.00000000
## 862 chr21 18113320 18113320 + 1.000000e+00 5.921730e-01 0.00000000
## 863 chr21 18113334 18113334 + 1.000000e+00 5.921730e-01 0.00000000
## 864 chr21 18113336 18113336 + 1.000000e+00 5.921730e-01 0.00000000
## 865 chr21 18113602 18113602 + 1.000000e+00 5.921730e-01 0.00000000
## 866 chr21 18113613 18113613 + 1.000000e+00 5.921730e-01 0.00000000
## 867 chr21 18113624 18113624 + 1.000000e+00 5.921730e-01 0.00000000
## 868 chr21 18113625 18113625 - 1.000000e+00 5.921730e-01 0.00000000
## 869 chr21 18113638 18113638 + 1.000000e+00 5.921730e-01 0.00000000
## 870 chr21 18113639 18113639 - 1.000000e+00 5.921730e-01 0.00000000
## 871 chr21 18113642 18113642 + 1.000000e+00 5.921730e-01 0.00000000
## 872 chr21 18113643 18113643 - 1.000000e+00 5.921730e-01 0.00000000
## 873 chr21 18113644 18113644 + 1.000000e+00 5.921730e-01 0.00000000
## 874 chr21 18113645 18113645 - 1.000000e+00 5.921730e-01 0.00000000
## 875 chr21 18113657 18113657 - 1.000000e+00 5.921730e-01 0.00000000
## 876 chr21 18113659 18113659 - 4.886587e-01 4.683425e-01 0.79365079
## 877 chr21 18113663 18113663 - 1.000000e+00 5.921730e-01 0.00000000
## 878 chr21 18113665 18113665 + 1.000000e+00 5.921730e-01 0.00000000
## 879 chr21 18113666 18113666 - 7.891250e-02 1.171897e-01 -2.85714286
## 880 chr21 18113681 18113681 + 1.000000e+00 5.921730e-01 0.00000000
## 881 chr21 18113689 18113689 + 1.000000e+00 5.921730e-01 0.00000000
## 882 chr21 18113700 18113700 + 1.000000e+00 5.921730e-01 0.00000000
## 883 chr21 18113703 18113703 + 1.000000e+00 5.921730e-01 0.00000000
## 884 chr21 18113704 18113704 - 1.000000e+00 5.921730e-01 0.00000000
## 885 chr21 18113706 18113706 + 1.000000e+00 5.921730e-01 0.00000000
## 886 chr21 18113707 18113707 - 1.000000e+00 5.921730e-01 0.00000000
## 887 chr21 18113714 18113714 + 1.000000e+00 5.921730e-01 0.00000000
## 888 chr21 18113715 18113715 - 1.000000e+00 5.921730e-01 0.00000000
## 889 chr21 18113720 18113720 - 1.000000e+00 5.921730e-01 0.00000000
## 890 chr21 18113738 18113738 - 1.000000e+00 5.921730e-01 0.00000000
## 891 chr21 18113751 18113751 - 3.469745e-01 3.705367e-01 1.00502513
## 892 chr21 18113817 18113817 - 1.000000e+00 5.921730e-01 0.00000000
## 893 chr21 18113823 18113823 - 1.000000e+00 5.921730e-01 0.00000000
## 894 chr21 18113863 18113863 - 1.000000e+00 5.921730e-01 0.00000000
## 895 chr21 18178551 18178551 - 1.839857e-02 3.668537e-02 -9.09090909
## 896 chr21 18178575 18178575 - 2.956505e-03 7.628888e-03 -14.03508772
## 897 chr21 18178579 18178579 - 3.440162e-02 5.926876e-02 -18.42105263
## 898 chr21 18178587 18178587 - 2.013762e-02 3.906031e-02 -8.77192982
## 899 chr21 18178591 18178591 - 7.391893e-02 1.109576e-01 -5.26315789
## 900 chr21 18292976 18292976 - 1.027205e-08 9.762948e-08 -40.54267650
## 901 chr21 18491657 18491657 + 2.466786e-03 6.512575e-03 -14.28571429
## 902 chr21 18491661 18491661 + 6.859092e-02 1.054308e-01 -11.78861789
## 903 chr21 18498731 18498731 - 8.660041e-03 1.892145e-02 -18.61471861
## 904 chr21 18538970 18538970 + 2.103788e-01 2.594531e-01 2.10084034
## 905 chr21 18538978 18538978 + 2.103788e-01 2.594531e-01 2.10084034
## 906 chr21 18538983 18538983 + 1.679340e-01 2.191453e-01 2.54237288
## 907 chr21 18538998 18538998 + 1.059695e-05 4.913044e-05 23.30508475
## 908 chr21 18539009 18539009 + 6.061920e-07 3.928280e-06 29.09090909
## 909 chr21 18539010 18539010 - 3.016434e-01 3.399524e-01 8.37104072
## 910 chr21 18539016 18539016 + 7.026043e-07 4.355098e-06 31.30841121
## 911 chr21 18539017 18539017 - 2.087395e-04 7.439771e-04 20.48192771
## 912 chr21 18539048 18539048 - 3.973349e-06 2.143312e-05 36.04729730
## 913 chr21 18539053 18539053 - 8.800401e-06 4.253000e-05 28.39506173
## 914 chr21 18539208 18539208 - 4.901774e-13 9.983208e-12 46.48936170
## 915 chr21 18539218 18539218 - 1.677821e-16 8.698170e-15 49.25170068
## 916 chr21 18539220 18539220 - 3.745066e-19 3.050958e-17 47.11538462
## 917 chr21 18539222 18539222 - 5.086158e-18 3.625557e-16 43.80952381
## 918 chr21 18539231 18539231 - 1.478624e-16 8.432039e-15 49.35765421
## 919 chr21 18539233 18539233 - 9.329989e-14 2.682511e-12 34.28571429
## 920 chr21 18539240 18539240 - 5.325912e-12 9.203541e-11 30.39215686
## 921 chr21 18539249 18539249 + 2.623125e-07 1.846753e-06 44.28571429
## 922 chr21 18539250 18539250 - 2.586401e-20 2.949855e-18 49.01960784
## 923 chr21 18539253 18539253 + 1.975059e-12 3.754341e-11 58.33333333
## 924 chr21 18539259 18539259 + 3.038366e-04 1.019216e-03 25.90497738
## 925 chr21 18539264 18539264 + 2.560907e-03 6.699034e-03 22.06349206
## 926 chr21 18539266 18539266 + 4.710879e-12 8.395119e-11 57.00934579
## 927 chr21 18539276 18539276 + 7.959508e-01 5.921730e-01 1.61616162
## 928 chr21 18539290 18539290 + 7.723273e-01 5.921730e-01 -0.83603020
## 929 chr21 18539294 18539294 + 5.912685e-02 9.392154e-02 10.63907044
## 930 chr21 18539804 18539804 + 4.520820e-04 1.434870e-03 11.49425287
## 931 chr21 18539838 18539838 + 1.342245e-01 1.831177e-01 9.72222222
## 932 chr21 18539933 18539933 - 1.384091e-13 3.587706e-12 55.24475524
## 933 chr21 18539960 18539960 - 1.202084e-06 7.067049e-06 34.23005566
## 934 chr21 18539970 18539970 - 1.214328e-01 1.680791e-01 3.59712230
## 935 chr21 18539976 18539976 - 2.118279e-02 3.999918e-02 15.47619048
## 936 chr21 18576141 18576141 + 2.618312e-05 1.089872e-04 -21.17647059
## 937 chr21 18576145 18576145 + 8.263422e-01 5.921730e-01 -1.26646403
## 938 chr21 18576157 18576157 + 6.446281e-01 5.761870e-01 -1.96581197
## 939 chr21 18576178 18576178 + 2.150474e-01 2.625985e-01 -8.28440965
## 940 chr21 18582970 18582970 + 4.571593e-09 4.740016e-08 -54.83870968
## 941 chr21 18582975 18582975 + 3.221664e-05 1.321723e-04 -43.71816638
## 942 chr21 18615698 18615698 - 9.029494e-06 4.276555e-05 -32.15350224
## 943 chr21 19390691 19390691 - 2.111546e-04 7.479105e-04 -15.76740285
## 944 chr21 19390694 19390694 - 8.557371e-04 2.494873e-03 -16.81576740
## 945 chr21 19539486 19539486 - 4.364576e-03 1.068511e-02 -10.41666667
## 946 chr21 19539511 19539511 - 7.142144e-04 2.143630e-03 -19.33566434
## 947 chr21 19539528 19539528 - 5.294717e-02 8.529320e-02 10.62388592
## 948 chr21 19579289 19579289 + 3.311258e-02 5.792291e-02 -18.21120690
## 949 chr21 19626227 19626227 - 3.262752e-02 5.742671e-02 -5.97014925
## 950 chr21 19626384 19626384 - 7.161813e-01 5.921730e-01 -1.21212121
## 951 chr21 19739452 19739452 + 7.969542e-01 5.921730e-01 1.53452685
## 952 chr21 19788073 19788073 - 4.595569e-04 1.447890e-03 23.72372372
## 953 chr21 19788076 19788076 - 1.136610e-01 1.584759e-01 -4.10958904
## 954 chr21 19788089 19788089 - 3.638140e-01 3.786809e-01 -1.36986301
## 955 chr21 19788093 19788093 - 1.333692e-01 1.823872e-01 2.77777778
## 956 chr21 19788113 19788113 - 2.682456e-03 6.984952e-03 10.52631579
## 957 chr21 19788117 19788117 - 3.639211e-01 3.786809e-01 -1.33333333
## 958 chr21 19788122 19788122 - 6.707718e-02 1.039446e-01 -5.33333333
## 959 chr21 19855690 19855690 + 3.901933e-02 6.602749e-02 -6.52173913
## 960 chr21 19855706 19855706 + 2.371789e-01 2.829587e-01 2.17391304
## 961 chr21 19855711 19855711 + 2.412907e-02 4.465459e-02 -8.33333333
## 962 chr21 19943653 19943653 + 7.528367e-01 5.921730e-01 1.45454545
## 963 chr21 19943695 19943695 + 3.904234e-01 3.960455e-01 3.38765009## can be ordered by decreasing absolute methylation difference
myDiff[order(-abs(myDiff$meth.diff))]## chr start end strand pvalue qvalue meth.diff
## 838 chr21 17907732 17907732 - 1.337656e-17 8.475726e-16 -73.62016515
## 344 chr21 14018050 14018050 + 1.547250e-12 3.042547e-11 65.58033162
## 615 chr21 15447185 15447185 - 8.791070e-21 1.253305e-18 -64.52887538
## 731 chr21 17803059 17803059 - 8.819420e-12 1.436967e-10 -63.35526316
## 923 chr21 18539253 18539253 + 1.975059e-12 3.754341e-11 58.33333333
## 297 chr21 13999012 13999012 - 3.600598e-13 8.213146e-12 57.80317244
## 926 chr21 18539266 18539266 + 4.710879e-12 8.395119e-11 57.00934579
## 732 chr21 17803088 17803088 - 2.701418e-12 4.969413e-11 -56.96202532
## 322 chr21 13999249 13999249 - 3.494649e-10 4.744923e-09 56.89655172
## 294 chr21 13998987 13998987 + 4.908427e-10 6.220205e-09 56.69934641
## 304 chr21 13999089 13999089 + 7.429177e-10 8.826212e-09 56.52482270
## 303 chr21 13999082 13999082 + 9.362046e-12 1.483007e-10 55.57603687
## 932 chr21 18539933 18539933 - 1.384091e-13 3.587706e-12 55.24475524
## 320 chr21 13999239 13999239 - 2.943129e-09 3.166711e-08 55.01474926
## 940 chr21 18582970 18582970 + 4.571593e-09 4.740016e-08 -54.83870968
## 307 chr21 13999107 13999107 + 6.254835e-09 6.149825e-08 53.94646534
## 717 chr21 16849374 16849374 + 1.619082e-07 1.247705e-06 -52.14285714
## 192 chr21 10186541 10186541 - 1.929664e-22 3.668051e-20 -52.06168831
## 24 chr21 9944505 9944505 + 7.907909e-20 7.515975e-18 -51.01794341
## 267 chr21 13958266 13958266 - 3.540372e-13 8.213146e-12 -50.98220904
## 321 chr21 13999247 13999247 - 6.838482e-08 5.820494e-07 50.68965517
## 289 chr21 13991085 13991085 + 4.322154e-07 2.934241e-06 50.36764706
## 298 chr21 13999023 13999023 - 5.482443e-11 8.227454e-10 50.03030303
## 918 chr21 18539231 18539231 - 1.478624e-16 8.432039e-15 49.35765421
## 915 chr21 18539218 18539218 - 1.677821e-16 8.698170e-15 49.25170068
## 922 chr21 18539250 18539250 - 2.586401e-20 2.949855e-18 49.01960784
## 18 chr21 9906873 9906873 + 4.976364e-06 2.603518e-05 47.62876084
## 67 chr21 10012942 10012942 - 1.652853e-13 4.098088e-12 47.61555393
## 916 chr21 18539220 18539220 - 3.745066e-19 3.050958e-17 47.11538462
## 839 chr21 17907754 17907754 - 1.869255e-07 1.366623e-06 -46.81706500
## 34 chr21 10011841 10011841 + 1.907779e-14 6.057727e-13 46.67050470
## 185 chr21 10156521 10156521 - 9.740855e-07 5.847206e-06 -46.49122807
## 914 chr21 18539208 18539208 - 4.901774e-13 9.983208e-12 46.48936170
## 23 chr21 9927527 9927527 + 1.120126e-07 9.257475e-07 -46.10983982
## 367 chr21 14230284 14230284 - 3.435314e-08 3.159728e-07 -45.82256675
## 710 chr21 16454615 16454615 + 6.005128e-09 6.007895e-08 -45.65217391
## 57 chr21 10012699 10012699 + 1.782895e-09 1.955228e-08 44.70329670
## 317 chr21 13999210 13999210 + 1.793080e-07 1.346599e-06 44.63434022
## 335 chr21 13999574 13999574 + 6.832975e-07 4.329545e-06 44.42586399
## 350 chr21 14057234 14057234 + 3.973326e-10 5.269394e-09 -44.28763441
## 921 chr21 18539249 18539249 + 2.623125e-07 1.846753e-06 44.28571429
## 11 chr21 9906681 9906681 + 2.324256e-08 2.172846e-07 44.26807760
## 222 chr21 10191430 10191430 - 1.245688e-14 4.735796e-13 -43.97304236
## 917 chr21 18539222 18539222 - 5.086158e-18 3.625557e-16 43.80952381
## 941 chr21 18582975 18582975 + 3.221664e-05 1.321723e-04 -43.71816638
## 75 chr21 10077699 10077699 + 2.570414e-15 1.127547e-13 -43.48105331
## 408 chr21 14358612 14358612 + 7.422303e-12 1.244901e-10 43.46598203
## 44 chr21 10011964 10011964 - 4.537417e-13 9.583406e-12 43.21110590
## 308 chr21 13999114 13999114 + 4.732497e-06 2.498857e-05 42.81250000
## 333 chr21 13999568 13999568 + 3.058702e-06 1.693460e-05 42.57854821
## 198 chr21 10190550 10190550 + 6.928265e-07 4.341682e-06 42.34496124
## 725 chr21 17419551 17419551 - 4.885317e-10 6.220205e-09 -41.89482834
## 348 chr21 14018261 14018261 + 1.891295e-05 8.048769e-05 41.54704944
## 324 chr21 13999256 13999256 - 1.119749e-07 9.257475e-07 41.49178255
## 37 chr21 10011861 10011861 + 8.445194e-09 8.162676e-08 41.19746233
## 295 chr21 13999005 13999005 + 2.887850e-07 2.008333e-06 40.95238095
## 900 chr21 18292976 18292976 - 1.027205e-08 9.762948e-08 -40.54267650
## 13 chr21 9906700 9906700 + 8.405880e-06 4.097059e-05 40.28016009
## 325 chr21 13999263 13999263 - 7.840586e-10 9.124884e-09 39.97175141
## 55 chr21 10012101 10012101 + 8.651864e-16 4.111529e-14 39.80782430
## 162 chr21 10145439 10145439 + 1.581233e-07 1.235230e-06 -39.64085297
## 341 chr21 14000092 14000092 + 1.219670e-05 5.476630e-05 -39.03903904
## 331 chr21 13999566 13999566 + 1.857595e-06 1.059317e-05 38.44444444
## 290 chr21 13991220 13991220 - 1.490967e-05 6.490401e-05 38.41269841
## 619 chr21 15583673 15583673 - 5.278102e-04 1.644756e-03 -37.46081505
## 254 chr21 13664888 13664888 + 1.308182e-07 1.049731e-06 -37.19485772
## 64 chr21 10012934 10012934 - 1.794638e-07 1.346599e-06 37.08791209
## 288 chr21 13991068 13991068 + 2.043710e-05 8.632973e-05 37.05882353
## 530 chr21 14568176 14568176 + 2.391222e-06 1.336887e-05 36.90476190
## 531 chr21 14568183 14568183 + 8.860660e-07 5.375429e-06 36.83181226
## 184 chr21 10156520 10156520 + 1.472591e-14 5.248521e-13 -36.63312777
## 191 chr21 10186519 10186519 - 1.264150e-13 3.432845e-12 -36.52439484
## 26 chr21 9959407 9959407 + 4.752177e-09 4.839266e-08 -36.31239936
## 210 chr21 10190881 10190881 + 4.580419e-15 1.865744e-13 -36.29659426
## 529 chr21 14568171 14568171 + 3.100519e-06 1.700106e-05 36.18421053
## 242 chr21 10192318 10192318 + 9.407983e-14 2.682511e-12 -36.12903226
## 912 chr21 18539048 18539048 - 3.973349e-06 2.143312e-05 36.04729730
## 68 chr21 10012944 10012944 - 1.238799e-07 1.009201e-06 36.00782779
## 620 chr21 15583716 15583716 - 5.263700e-05 2.099085e-04 -36.00000000
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## 296 chr21 13999011 13999011 + 1.451500e-05 6.416558e-05 35.33692722
## 255 chr21 13664889 13664889 - 6.902971e-36 3.936507e-33 -34.91195089
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## 720 chr21 16869890 16869890 + 3.492257e-09 3.687970e-08 -34.69597070
## 919 chr21 18539233 18539233 - 9.329989e-14 2.682511e-12 34.28571429
## 933 chr21 18539960 18539960 - 1.202084e-06 7.067049e-06 34.23005566
## 260 chr21 13673754 13673754 + 3.184396e-04 1.055780e-03 33.91360413
## 305 chr21 13999095 13999095 + 2.136550e-06 1.206331e-05 33.74513841
## 27 chr21 9959541 9959541 - 4.733460e-07 3.145107e-06 -33.55957768
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## 311 chr21 13999142 13999142 - 1.684720e-04 6.139147e-04 32.71048110
## 336 chr21 13999575 13999575 - 1.263860e-05 5.630720e-05 32.65068265
## 942 chr21 18615698 18615698 - 9.029494e-06 4.276555e-05 -32.15350224
## 910 chr21 18539016 18539016 + 7.026043e-07 4.355098e-06 31.30841121
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## 30 chr21 9959644 9959644 - 6.406095e-06 3.291132e-05 -30.96045198
## 50 chr21 10012069 10012069 + 7.376551e-10 8.826212e-09 30.81481481
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## 920 chr21 18539240 18539240 - 5.325912e-12 9.203541e-11 30.39215686
## 711 chr21 16454616 16454616 - 3.869785e-03 9.721560e-03 -30.00000000
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## 241 chr21 10192279 10192279 + 1.598774e-09 1.787688e-08 -29.40588663
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## 228 chr21 10191819 10191819 - 1.912085e-14 6.057727e-13 -29.03885481
## 840 chr21 17983314 17983314 + 1.018308e-03 2.932845e-03 -28.98206751
## 43 chr21 10011954 10011954 - 5.680842e-10 7.042547e-09 28.89423077
## 12 chr21 9906694 9906694 + 2.983628e-04 1.006776e-03 28.85090218
## 221 chr21 10191408 10191408 - 4.743064e-07 3.145107e-06 -28.74762513
## 332 chr21 13999567 13999567 - 3.062075e-07 2.103840e-06 28.65655471
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## 729 chr21 17731701 17731701 + 1.567245e-03 4.317592e-03 -28.49624060
## 913 chr21 18539053 18539053 - 8.800401e-06 4.253000e-05 28.39506173
## 31 chr21 9959650 9959650 - 1.521958e-05 6.575118e-05 -28.31442751
## 323 chr21 13999252 13999252 - 1.185429e-05 5.407178e-05 28.19514430
## 25 chr21 9944663 9944663 - 1.790779e-05 7.678302e-05 -28.09991079
## 76 chr21 10077705 10077705 + 1.331087e-06 7.745606e-06 -27.59568018
## 377 chr21 14305244 14305244 + 1.603159e-04 5.898203e-04 -27.32456140
## 85 chr21 10114351 10114351 - 2.484244e-07 1.770840e-06 -27.17054264
## 396 chr21 14358179 14358179 - 2.018062e-03 5.454149e-03 27.05696203
## 47 chr21 10011990 10011990 + 6.044847e-05 2.377345e-04 26.68552570
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## 52 chr21 10012079 10012079 + 1.325366e-07 1.049731e-06 26.61691542
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## 71 chr21 10022157 10022157 - 4.316338e-03 1.065561e-02 -26.10275039
## 728 chr21 17731698 17731698 + 1.237703e-03 3.511521e-03 -26.09022556
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## 32 chr21 9967634 9967634 - 3.081873e-04 1.027764e-03 -25.86255790
## 316 chr21 13999206 13999206 + 2.378706e-03 6.309242e-03 25.75030012
## 245 chr21 13282440 13282440 + 1.696739e-10 2.418967e-09 -25.57837487
## 330 chr21 13999562 13999562 - 3.117343e-03 7.969354e-03 25.53956835
## 81 chr21 10099950 10099950 + 3.907632e-03 9.748659e-03 -25.26881720
## 361 chr21 14229227 14229227 - 2.858745e-02 5.126526e-02 25.26455026
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## 712 chr21 16454628 16454628 - 2.236933e-04 7.874317e-04 -25.00000000
## 299 chr21 13999031 13999031 - 3.914763e-03 9.748659e-03 24.79069767
## 40 chr21 10011878 10011878 + 9.176517e-05 3.488683e-04 24.69387755
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## 41 chr21 10011925 10011925 - 2.456880e-04 8.543092e-04 24.09525188
## 153 chr21 10133402 10133402 - 2.092254e-02 3.990415e-02 23.88206388
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## 342 chr21 14018027 14018027 + 8.110882e-04 2.384192e-03 23.69925672
## 724 chr21 17419460 17419460 - 1.558414e-03 4.314103e-03 -23.66666667
## 345 chr21 14018053 14018053 + 1.951332e-02 3.823958e-02 23.45238095
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## 907 chr21 18538998 18538998 + 1.059695e-05 4.913044e-05 23.30508475
## 318 chr21 13999212 13999212 + 1.383193e-02 2.868302e-02 23.00564061
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## 349 chr21 14018268 14018268 + 2.005259e-02 3.906031e-02 22.75432900
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## 35 chr21 10011855 10011855 + 3.292371e-10 4.579308e-09 22.64150943
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## 161 chr21 10136773 10136773 - 1.248526e-10 1.825610e-09 -21.78576992
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## 206 chr21 10190633 10190633 + 6.036893e-03 1.393771e-02 21.36899626
## 936 chr21 18576141 18576141 + 2.618312e-05 1.089872e-04 -21.17647059
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## 65 chr21 10012938 10012938 - 1.767536e-04 6.379491e-04 21.01449275
## 61 chr21 10012887 10012887 + 1.645398e-02 3.315580e-02 20.80792683
## 454 chr21 14367213 14367213 + 1.543210e-02 3.154248e-02 -20.62062062
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## 621 chr21 15584415 15584415 + 6.760422e-04 2.050647e-03 -20.00000000
## 326 chr21 13999541 13999541 + 1.141466e-02 2.437960e-02 19.76878613
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## 366 chr21 14230216 14230216 - 3.978291e-04 1.281735e-03 -19.60784314
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## 946 chr21 19539511 19539511 - 7.142144e-04 2.143630e-03 -19.33566434
## 124 chr21 10128745 10128745 + 1.194720e-05 5.407178e-05 19.26081158
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## 66 chr21 10012940 10012940 - 1.364114e-04 5.117783e-04 18.81942241
## 5 chr21 9906616 9906616 + 1.576498e-03 4.322201e-03 18.81750466
## 534 chr21 14568220 14568220 + 3.130374e-03 7.969354e-03 18.80701754
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## 154 chr21 10136614 10136614 + 4.096255e-08 3.593755e-07 -18.67093945
## 903 chr21 18498731 18498731 - 8.660041e-03 1.892145e-02 -18.61471861
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## 17 chr21 9906869 9906869 + 7.859689e-04 2.334420e-03 18.51851852
## 897 chr21 18178579 18178579 - 3.440162e-02 5.926876e-02 -18.42105263
## 133 chr21 10128964 10128964 - 2.076642e-02 3.990415e-02 -18.25226001
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## 78 chr21 10086785 10086785 + 6.070256e-03 1.395823e-02 -17.61615044
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## 280 chr21 13989452 13989452 - 1.612882e-02 3.261583e-02 17.39393939
## 51 chr21 10012075 10012075 + 2.296300e-05 9.628634e-05 17.29629630
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## 944 chr21 19390694 19390694 - 8.557371e-04 2.494873e-03 -16.81576740
## 835 chr21 17907505 17907505 - 1.222150e-03 3.484732e-03 -16.70834559
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## 935 chr21 18539976 18539976 - 2.118279e-02 3.999918e-02 15.47619048
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## 359 chr21 14229198 14229198 - 8.002022e-03 1.755098e-02 -15.09433962
## 836 chr21 17907513 17907513 - 2.300612e-03 6.130621e-03 -14.87809515
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## 901 chr21 18491657 18491657 + 2.466786e-03 6.512575e-03 -14.28571429
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## 6 chr21 9906619 9906619 + 2.824594e-03 7.321638e-03 14.19373165
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## 483 chr21 14370181 14370181 - 3.554025e-08 3.217028e-07 13.92554706
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## 7 chr21 9906634 9906634 + 3.432652e-02 5.926876e-02 13.88888889
## 713 chr21 16454650 16454650 - 7.021651e-03 1.576451e-02 -13.79310345
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## 841 chr21 17988383 17988383 + 3.155696e-02 5.606154e-02 13.50000000
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## 21 chr21 9913543 9913543 + 1.254379e-02 2.632641e-02 -13.34310850
## 16 chr21 9906864 9906864 + 1.555091e-01 2.071703e-01 13.33333333
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## 622 chr21 15584444 15584444 + 3.155309e-02 5.606154e-02 -13.27751196
## 72 chr21 10022165 10022165 - 6.042468e-02 9.571650e-02 13.12921640
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## 232 chr21 10191851 10191851 - 3.957467e-05 1.611997e-04 -13.06577129
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## 119 chr21 10128720 10128720 + 1.480847e-03 4.139567e-03 12.98041390
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## 79 chr21 10087014 10087014 - 3.910017e-01 3.960455e-01 -11.90476190
## 49 chr21 10012030 10012030 + 8.551484e-02 1.256854e-01 11.85605801
## 902 chr21 18491661 18491661 + 6.859092e-02 1.054308e-01 -11.78861789
## 20 chr21 9906889 9906889 + 1.121225e-02 2.412804e-02 11.76470588
## 405 chr21 14358585 14358585 - 5.374421e-02 8.633327e-02 11.68831169
## 930 chr21 18539804 18539804 + 4.520820e-04 1.434870e-03 11.49425287
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## 8 chr21 9906640 9906640 + 6.469598e-04 1.972925e-03 11.17318436
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## 235 chr21 10191900 10191900 - 1.431176e-03 4.020424e-03 -10.97592484
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## 947 chr21 19539528 19539528 - 5.294717e-02 8.529320e-02 10.62388592
## 28 chr21 9959569 9959569 - 1.867858e-02 3.702161e-02 -10.62298337
## 33 chr21 10011833 10011833 + 2.453779e-04 8.543092e-04 10.59027778
## 74 chr21 10065061 10065061 + 1.632724e-01 2.145349e-01 -10.58855886
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## 505 chr21 14376609 14376609 - 1.732999e-01 2.233472e-01 -9.72850679
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## 10 chr21 9906676 9906676 + 2.399584e-01 2.850819e-01 -4.33048433
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## 3 chr21 9860126 9860126 + 4.160449e-02 6.978084e-02 -4.11158117
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## 623 chr21 15584560 15584560 - 5.387495e-01 5.020077e-01 -4.04483431
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## 337 chr21 13999588 13999588 - 3.776379e-01 3.866330e-01 3.96825397
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## 60 chr21 10012883 10012883 + 4.287026e-01 4.251706e-01 3.75000000
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## 130 chr21 10128867 10128867 + 7.278967e-03 1.621454e-02 -3.62839155
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## 120 chr21 10128721 10128721 - 3.652256e-01 3.786809e-01 3.60993905
## 934 chr21 18539970 18539970 - 1.214328e-01 1.680791e-01 3.59712230
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## 726 chr21 17719707 17719707 - 3.557065e-01 3.770374e-01 -3.53271778
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## 760 chr21 17832330 17832330 - 5.461083e-01 5.072071e-01 3.24324324
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## 842 chr21 17988393 17988393 + 6.781834e-01 5.921730e-01 3.00000000
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## 508 chr21 14377513 14377513 + 5.593419e-01 5.176294e-01 2.81524927
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## 380 chr21 14348845 14348845 + 6.506369e-01 5.797405e-01 2.77777778
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## 955 chr21 19788093 19788093 - 1.333692e-01 1.823872e-01 2.77777778
## 264 chr21 13958080 13958080 - 5.181231e-01 4.923835e-01 -2.77310924
## 506 chr21 14376611 14376611 - 6.160904e-01 5.559072e-01 -2.72410091
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## 211 chr21 10190896 10190896 + 4.918876e-01 4.706461e-01 -2.66290727
## 721 chr21 17043261 17043261 + 2.282202e-01 2.751490e-01 2.63157895
## 268 chr21 13989277 13989277 + 3.721873e-01 3.837525e-01 2.62626263
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## 338 chr21 13999610 13999610 - 6.004554e-01 5.443836e-01 -2.57936508
## 906 chr21 18538983 18538983 + 1.679340e-01 2.191453e-01 2.54237288
## 846 chr21 18038995 18038995 + 5.663180e-01 5.213032e-01 2.50712251
## 722 chr21 17043269 17043269 + 2.456291e-01 2.896763e-01 -2.50000000
## 230 chr21 10191830 10191830 - 4.381528e-01 4.330367e-01 2.48033656
## 200 chr21 10190587 10190587 - 5.255617e-01 4.945680e-01 -2.46969697
## 270 chr21 13989284 13989284 + 5.959555e-01 5.411642e-01 2.46687054
## 363 chr21 14229946 14229946 - 2.329051e-01 2.790275e-01 -2.43902439
## 420 chr21 14365015 14365015 - 2.749261e-01 3.160888e-01 -2.43902439
## 476 chr21 14368475 14368475 - 3.429142e-01 3.690515e-01 -2.43902439
## 376 chr21 14281495 14281495 - 5.009169e-01 4.784827e-01 2.43559719
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## 702 chr21 16033213 16033213 - 7.676277e-01 5.921730e-01 2.39018088
## 400 chr21 14358203 14358203 - 1.702171e-01 2.210961e-01 -2.38095238
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## 430 chr21 14365748 14365748 - 4.621706e-01 4.512990e-01 2.30508475
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## 626 chr21 15788733 15788733 - 7.131285e-01 5.921730e-01 -2.22575517
## 471 chr21 14367979 14367979 - 2.298593e-01 2.759583e-01 2.22222222
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## 960 chr21 19855706 19855706 + 2.371789e-01 2.829587e-01 2.17391304
## 904 chr21 18538970 18538970 + 2.103788e-01 2.594531e-01 2.10084034
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## 29 chr21 9959577 9959577 - 5.170987e-01 4.922906e-01 -2.08488459
## 467 chr21 14367740 14367740 - 3.101403e-01 3.467871e-01 -2.08333333
## 743 chr21 17807600 17807600 + 1.616142e-01 2.128943e-01 -2.08333333
## 718 chr21 16869844 16869844 + 5.871720e-01 5.348918e-01 -2.04761905
## 36 chr21 10011858 10011858 + 7.585884e-01 5.921730e-01 2.04081633
## 742 chr21 17807597 17807597 + 1.558511e-01 2.071703e-01 -2.00000000
## 938 chr21 18576157 18576157 + 6.446281e-01 5.761870e-01 -1.96581197
## 741 chr21 17807588 17807588 + 1.554620e-01 2.071703e-01 -1.96078431
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## 650 chr21 16023947 16023947 - 1.686666e-01 2.195987e-01 1.93548387
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## 852 chr21 18052081 18052081 + 6.373381e-01 5.714624e-01 -1.84782609
## 387 chr21 14357926 14357926 + 3.180191e-01 3.521444e-01 -1.84746877
## 355 chr21 14120949 14120949 + 7.869016e-01 5.921730e-01 1.83901919
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## 80 chr21 10099942 10099942 + 8.117156e-01 5.921730e-01 1.79211470
## 850 chr21 18052041 18052041 - 3.732411e-01 3.837525e-01 -1.78571429
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## 229 chr21 10191824 10191824 - 6.714069e-01 5.921730e-01 -1.71324183
## 723 chr21 17385151 17385151 - 2.021349e-01 2.522319e-01 1.67339925
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## 752 chr21 17807677 17807677 - 4.154389e-01 4.163608e-01 1.62601626
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## 500 chr21 14374686 14374686 + 2.518555e-01 2.949153e-01 -1.58730159
## 99 chr21 10128603 10128603 + 5.404387e-01 5.027602e-01 1.57273919
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## 951 chr21 19739452 19739452 + 7.969542e-01 5.921730e-01 1.53452685
## 855 chr21 18052194 18052194 - 6.942585e-01 5.921730e-01 -1.47707980
## 962 chr21 19943653 19943653 + 7.528367e-01 5.921730e-01 1.45454545
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## 102 chr21 10128639 10128639 + 6.665902e-01 5.911842e-01 1.43502331
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## 9 chr21 9906644 9906644 + 7.711669e-01 5.921730e-01 -1.40550239
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## 954 chr21 19788089 19788089 - 3.638140e-01 3.786809e-01 -1.36986301
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## 957 chr21 19788117 19788117 - 3.639211e-01 3.786809e-01 -1.33333333
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## 414 chr21 14364894 14364894 + 2.014605e-01 2.519417e-01 1.26582278
## 629 chr21 16014722 16014722 - 4.759118e-01 4.603252e-01 1.25000000
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## 543 chr21 15358331 15358331 + 5.855625e-01 5.342791e-01 -1.21951220
## 950 chr21 19626384 19626384 - 7.161813e-01 5.921730e-01 -1.21212121
## 799 chr21 17906996 17906996 - 4.066124e-01 4.096747e-01 1.20481928
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## 757 chr21 17807801 17807801 - 2.922772e-01 3.307039e-01 1.17647059
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## 857 chr21 18113300 18113300 + 4.165108e-01 4.167027e-01 1.11111111
## 135 chr21 10128989 10128989 - 8.126659e-01 5.921730e-01 -1.10032362
## 480 chr21 14370143 14370143 - 7.224563e-01 5.921730e-01 1.06828938
## 86 chr21 10120564 10120564 - 9.001105e-01 5.921730e-01 1.06227106
## 822 chr21 17907131 17907131 - 3.569557e-01 3.771534e-01 1.03092784
## 181 chr21 10154897 10154897 + 3.326013e-01 3.633527e-01 -1.01010101
## 891 chr21 18113751 18113751 - 3.469745e-01 3.705367e-01 1.00502513
## 415 chr21 14364896 14364896 + 2.796340e-01 3.202105e-01 -1.00000000
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## 110 chr21 10128682 10128682 + 4.005472e-01 4.042781e-01 0.96153846
## 374 chr21 14275582 14275582 - 4.091694e-01 4.113649e-01 -0.96153846
## 715 chr21 16713421 16713421 + 8.859389e-01 5.921730e-01 0.95238095
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## 635 chr21 16023788 16023788 + 3.052922e-01 3.433860e-01 0.91743119
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## 469 chr21 14367894 14367894 - 3.378437e-01 3.667373e-01 -0.89285714
## 48 chr21 10012008 10012008 + 9.083844e-01 5.921730e-01 0.88126160
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## 928 chr21 18539290 18539290 + 7.723273e-01 5.921730e-01 -0.83603020
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## 70 chr21 10022131 10022131 - 9.421034e-01 5.921730e-01 -0.69686411
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## 596 chr21 15359094 15359094 - 5.080370e-01 4.844724e-01 0.58139535
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## 137 chr21 10129003 10129003 - 9.408835e-01 5.921730e-01 -0.46958855
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## 844 chr21 18038986 18038986 + 9.050132e-01 5.921730e-01 -0.35560547
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## 277 chr21 13989448 13989448 + 9.495895e-01 5.921730e-01 -0.32467532
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## 2 chr21 9853326 9853326 + 9.473546e-01 5.921730e-01 0.20913594
## 851 chr21 18052058 18052058 + 9.492917e-01 5.921730e-01 0.19792182
## 69 chr21 10012947 10012947 - 9.538162e-01 5.921730e-01 0.18552876
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## 59 chr21 10012881 10012881 + 1.000000e+00 5.921730e-01 0.00000000
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## 413 chr21 14364882 14364882 + 1.000000e+00 5.921730e-01 0.00000000
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## 544 chr21 15358368 15358368 + 1.000000e+00 5.921730e-01 0.00000000
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## 549 chr21 15358658 15358658 + 1.000000e+00 5.921730e-01 0.00000000
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## 554 chr21 15358689 15358689 - 1.000000e+00 5.921730e-01 0.00000000
## 555 chr21 15358693 15358693 - 1.000000e+00 5.921730e-01 0.00000000
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## 557 chr21 15358702 15358702 - 1.000000e+00 5.921730e-01 0.00000000
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## 563 chr21 15358815 15358815 + 1.000000e+00 5.921730e-01 0.00000000
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## 566 chr21 15358829 15358829 + 1.000000e+00 5.921730e-01 0.00000000
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## 568 chr21 15358831 15358831 + 1.000000e+00 5.921730e-01 0.00000000
## 569 chr21 15358832 15358832 - 1.000000e+00 5.921730e-01 0.00000000
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## 571 chr21 15358856 15358856 - 1.000000e+00 5.921730e-01 0.00000000
## 572 chr21 15358859 15358859 - 1.000000e+00 5.921730e-01 0.00000000
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## 575 chr21 15358868 15358868 - 1.000000e+00 5.921730e-01 0.00000000
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## 894 chr21 18113863 18113863 - 1.000000e+00 5.921730e-01 0.000000007 Acknowledgements
This package is initially developed at Weill Cornell Medical College by Altuna Akalin with important code contributions from Matthias Kormaksson(mk375@cornell.edu) and Sheng Li (shl2018@med.cornell.edu). We wish to thank especially Maria E. Figueroa, Francine Garret-Bakelman, Christopher Mason and Ari Melnick for their contribution of ideas, data and support. Their support and discussions lead to development of methylKit.
7.1 Full list of contributors
- Altuna Akalin (main design and development)
- Matthias Kormaksson (initial differential methylation tests)
- Sheng Li (adding PCA and clustering)
- Adrian Bierling (methylation simulation, covariate and over-dispersion correction)
- Alexander Gosdschan (C++ based BAM parsing and tabix based classes )
- Arsene Webo (methylation segmentation)
8 R session info
sessionInfo() ## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] genomation_1.22.0 methylKit_1.16.1 GenomicRanges_1.42.0
## [4] GenomeInfoDb_1.26.2 IRanges_2.24.1 S4Vectors_0.28.1
## [7] BiocGenerics_0.36.0
##
## loaded via a namespace (and not attached):
## [1] MatrixGenerics_1.2.0 Biobase_2.50.0
## [3] splines_4.0.3 R.utils_2.10.1
## [5] gtools_3.8.2 assertthat_0.2.1
## [7] highr_0.8 BSgenome_1.58.0
## [9] GenomeInfoDbData_1.2.4 Rsamtools_2.6.0
## [11] impute_1.64.0 yaml_2.2.1
## [13] numDeriv_2016.8-1.1 pillar_1.4.7
## [15] lattice_0.20-41 glue_1.4.2
## [17] limma_3.46.0 bbmle_1.0.23.1
## [19] digest_0.6.27 XVector_0.30.0
## [21] qvalue_2.22.0 colorspace_2.0-0
## [23] htmltools_0.5.1.1 Matrix_1.3-2
## [25] R.oo_1.24.0 plyr_1.8.6
## [27] XML_3.99-0.5 pkgconfig_2.0.3
## [29] emdbook_1.3.12 zlibbioc_1.36.0
## [31] purrr_0.3.4 mvtnorm_1.1-1
## [33] scales_1.1.1 BiocParallel_1.24.1
## [35] tibble_3.0.5 mgcv_1.8-33
## [37] generics_0.1.0 ggplot2_3.3.3
## [39] ellipsis_0.3.1 seqPattern_1.22.0
## [41] SummarizedExperiment_1.20.0 magrittr_2.0.1
## [43] crayon_1.3.4 mclust_5.4.7
## [45] evaluate_0.14 R.methodsS3_1.8.1
## [47] nlme_3.1-151 MASS_7.3-53
## [49] tools_4.0.3 data.table_1.13.6
## [51] hms_1.0.0 gridBase_0.4-7
## [53] lifecycle_0.2.0 matrixStats_0.57.0
## [55] stringr_1.4.0 munsell_0.5.0
## [57] plotrix_3.8-1 DelayedArray_0.16.1
## [59] Biostrings_2.58.0 compiler_4.0.3
## [61] fastseg_1.36.0 rlang_0.4.10
## [63] RCurl_1.98-1.2 bitops_1.0-6
## [65] rmarkdown_2.6 gtable_0.3.0
## [67] DBI_1.1.1 reshape2_1.4.4
## [69] R6_2.5.0 GenomicAlignments_1.26.0
## [71] knitr_1.31 dplyr_1.0.3
## [73] rtracklayer_1.50.0 bdsmatrix_1.3-4
## [75] readr_1.4.0 KernSmooth_2.23-18
## [77] stringi_1.5.3 Rcpp_1.0.6
## [79] vctrs_0.3.6 tidyselect_1.1.0
## [81] xfun_0.20 coda_0.19-4