DAMEfinder Workflow
2021-10-26
Package
DAMEfinder 1.6.0
Contents
1 Introduction
1.1 What is allele-specific methylation?
The phenomenon occurs when there is an asymmetry in methylation between one specific allele and the alternative allele (Hu et al. 2013). The best studied example of allele-specific methylation (ASM) is genomic imprinting. When a gene is imprinted, one of the parental alleles is hyper-methylated compared to the other allele, which leads to parent-allele-specific expression. This asymmetry is conferred in the gametes or very early in embryogenesis, and will remain for the lifetime of the individual (Kelsey and Feil 2013). ASM not related to imprinting, exhibits parental-specific methylation, but is not inherited from the germline (Hanna and Kelsey 2017). Another example of ASM is X chromosome inactivation in females. DAMEfinder detects ASM for several bisulfite-sequenced (BS-seq) samples in a cohort, and performs differential detection for regions that exhibit loss or gain of ASM.
2 Overview
We focus on any case of ASM in which there is an imbalance in the methylation level between two alleles, regardless of the allele of origin.
DAMEfinder runs in two modes: SNP-based (exhaustive-mode) and tuple-based (fast-mode), which converge when differential ASM is detected.
2.1 Why SNP-based?
This is the exhaustive mode because it extracts an ASM score for every CpG site in the reads containing the SNPs in a VCF file. Based on this score, DAMEs are detected. From a biological point of view, you might want to run this mode if you are interested in loss or gain of allele-specificity linked to somatic or germline heterozygous SNPs (sequence-dependent ASM). More specifically, you could detect genes that exhibit loss of imprinting (e.g. as in colorectal cancer (Cui et al. 2002)).
2.2 Why tuple-based?
To run the tuple-based mode you have to run methtuple(Hickey 2015) first. The methtuple output is the only thing needed for this mode. I call this the fast-mode because you don’t need SNP information. The assumption is that intermediate levels of methylation represent ASM along the genome. For example, we have shown (paper in prep) that the ASM score can distinguish females from males in the X chromosome. Using SNP information this wouldn’t be possible.
2.3 Installation
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DAMEfinder")3 Get bam files
In order to run any of the two modes, you must obtain aligned bam files using
bismark. Here we
demonstrate how to generate these starting from paired-end fastq files of
bisulfite-treated reads:
#Check quality of reads
fastqc -t 2 sample1_R1.fastq.gz sample1_R2.fastq.gz
#Trim reads to remove bad quality regions and adapter sequence
trim_galore --paired sample1_R1.fastq.gz sample2_R2.fastq.gzTo trim the reads we use Trim Galore and specify the use of
paired reads. By default it will remove any adapter sequence it recognizes.
Please refer to the user guide for further specifications.
#Build bisulfite reference
bismark_genome_preparation <path_to_genome_folder>
#run Bismark
bismark -B sample1 --genome <path_to_genome_folder>
-1 sample1_R1_val_1.fq.gz
-2 sample1_R2_val_2.fq.gz
#deduplicate (optional)
deduplicate_bismark -p --bam sample1_pe.bam
#sort and index files
samtools sort -m 20G -O bam -T _tmp
-o sample1_pe.dedupl_s.bam sample1_pe.deduplicated.bam
samtools index file1_pe.dedupl_s.bamBefore the alignment, you must download a reference fasta file from
Ensembl or
Gencode, and generate a bisulfite converted
reference. For this we use bismark_genome_preparation from the bismark
suite, and specify the folder that contains the fasta file with its index
file. Depending on the library type and kit used to obtain the reads, you may
want to deduplicate your bam files (e.g. TruSeq). Please refer to the user
guide
for further explanation and specifications.
4 SNP-based (aka slow-mode)
To run the SNP-based mode, you need to additionally have a VCF file including
the heterozygous SNPs per sample. If you do not have this, we recommend using
the tuple-based mode, or running
Bis-SNP to obtain variant
calls from bisulfite-converted reads.
4.1 Example Workflow
In this example we use samples from two patients with colorectal cancer from a
published dataset (Parker et al. 2018). For each patient two samples were taken:
NORM# corresponds to normal mucosa tissue and CRC# corresponds to the paired
adenoma lesion. Each of these samples was sequenced using targeted BS-seq
followed by variant calling using Bis-SNP.
4.1.1 Obtain allele-based methylation calls
Similar to the bismark_methylation_extractor, we obtain methylation calls.
However since we are interested in allele-specific methylation, we only extract
methylation for CpG sites that fall within reads including a SNP. For every SNP
in the VCF file an independent methylation call is performed by using
extract_bams, which “extracts” reads from the bam file according to the
alleles, and generates a list of GRangesLists:
suppressPackageStartupMessages({
library(DAMEfinder)
library(SummarizedExperiment)
library(GenomicRanges)
library(BSgenome.Hsapiens.UCSC.hg19)
})
bam_files <- c(system.file("extdata", "NORM1_chr19_trim.bam",
package = "DAMEfinder"),
system.file("extdata", "CRC1_chr19_trim.bam",
package = "DAMEfinder"))
vcf_files <- c(system.file("extdata", "NORM1.chr19.trim.vcf",
package = "DAMEfinder"),
system.file("extdata", "CRC1.chr19.trim.vcf",
package = "DAMEfinder"))
sample_names <- c("NORM1", "CRC1")
#Use another reference file for demonstration, and fix the seqnames
genome <- BSgenome.Hsapiens.UCSC.hg19
seqnames(genome) <- gsub("chr","",seqnames(genome))
reference_file <- DNAStringSet(genome[[19]], use.names = TRUE)
names(reference_file) <- 19
#Extract reads and extract methylation according to allele
snp.list <- extract_bams(bam_files, vcf_files, sample_names, reference_file,
coverage = 2)## Reading reference file## Running sample NORM1## Reading VCF file## Extracting methylation per SNP## Warning in .make_GAlignmentPairs_from_GAlignments(gal, strandMode = strandMode, : 3 alignments with ambiguous pairing were dumped.
## Use 'getDumpedAlignments()' to retrieve them from the dump environment.## Done with sample NORM1## Running sample CRC1## Reading VCF file## Extracting methylation per SNP## Done with sample CRC1#CpG sites for first SNP in VCF file from sample NORM1
snp.list$NORM1[[1]]## GRanges object with 24 ranges and 5 metadata columns:
## seqnames ranges strand | cov.ref cov.alt meth.ref meth.alt
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric> <numeric>
## [1] 19 266963 * | 2 4 0 0
## [2] 19 266999 * | 7 15 0 0
## [3] 19 267087 * | 37 42 0 1
## [4] 19 267097 * | 36 42 2 4
## [5] 19 267103 * | 35 41 1 2
## ... ... ... ... . ... ... ... ...
## [20] 19 267207 * | 5 3 0 0
## [21] 19 267215 * | 4 2 0 0
## [22] 19 267224 * | 4 2 0 0
## [23] 19 267229 * | 3 2 0 0
## [24] 19 267234 * | 3 2 0 0
## snp
## <character>
## [1] 19.267039
## [2] 19.267039
## [3] 19.267039
## [4] 19.267039
## [5] 19.267039
## ... ...
## [20] 19.267039
## [21] 19.267039
## [22] 19.267039
## [23] 19.267039
## [24] 19.267039
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths#CpG sites for first SNP in VCF file from sample CRC1
snp.list$CRC1[[1]]## GRanges object with 19 ranges and 5 metadata columns:
## seqnames ranges strand | cov.ref cov.alt meth.ref meth.alt
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric> <numeric>
## [1] 19 266999 * | 21 13 2 1
## [2] 19 267087 * | 51 37 0 0
## [3] 19 267097 * | 50 37 1 0
## [4] 19 267103 * | 50 38 0 0
## [5] 19 267109 * | 49 38 1 1
## ... ... ... ... . ... ... ... ...
## [15] 19 267162 * | 26 18 1 1
## [16] 19 267164 * | 26 18 0 0
## [17] 19 267178 * | 16 10 0 0
## [18] 19 267181 * | 13 8 0 0
## [19] 19 267207 * | 4 2 0 0
## snp
## <character>
## [1] 19.267039
## [2] 19.267039
## [3] 19.267039
## [4] 19.267039
## [5] 19.267039
## ... ...
## [15] 19.267039
## [16] 19.267039
## [17] 19.267039
## [18] 19.267039
## [19] 19.267039
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsFor demonstration, we include bam files from chromosome 19, and shortened VCF files. Typically we would run the function on an entire bam and VCF file, which would generate a large output.
The function also takes as input the reference file used to generate the
alignments. For demonstration we use chromosome 19 of the GRCh37.91 reference
fasta file.
4.1.2 Summarize methylation calls across samples
We use calc_derivedasm() to generate a RangedSummarizedExperiment from the
large list we generated above:
derASM <- calc_derivedasm(snp.list)## ..Summarizing scores
## Summarizing coverages
## Summarizing SNP info
## Returning 158 CpG sites for 2 samplesderASM## class: RangedSummarizedExperiment
## dim: 158 2
## metadata(0):
## assays(7): der.ASM z.ASM ... ref.meth alt.meth
## rownames(158): 19.266963 19.266999 ... 19.292625 19.292645
## rowData names(0):
## colnames(2): NORM1 CRC1
## colData names(1): samplesassays(derASM)## List of length 7
## names(7): der.ASM z.ASM snp.table ref.cov alt.cov ref.meth alt.methEvery row in the object is a single CpG site, and each column a sample. It
contains 6 matrices in assays:
der.ASM: A derived SNP-based ASM defined as \(abs(\frac{X^{r}_M}{X^{r}} - \frac{X^{a}_M}{X^{a}})\), where \(X\) is the coverage in the reference \(r\) or alternative allele \(a\), and \(X_M\) the number of methylated reads in \(r\) or \(a\). Basically, CpG sites with values of 1 or close to 1 have more allele-specificity. ASM of 1 represents the perfect scenario in which none of the reads belonging to one allele are methylated, and the reads of the other allele are completely methylated.NEW
z.ASM: SNP-based ASM defined as a Z score in a two-proportions test: \(abs(\frac{p^{r}-p^{a}} {p(1-p)(1/X^{r} + 1/X^{a})})\), where \(p\) is \(\frac{X_M}{X}\) of either the reference, the alternative or both alleles. This score is more sensitive to the coverage at each CpG site, and has no upper limit.snp.table: Location of the SNP associated to the CpG site.ref.cov: Coverage of the “reference” allele.alt.cov: Covearage of the “alternative” allele.ref.meth: Methylated reads from the “reference” allele.alt.meth: Methylated reads from the “alternative” allele.
You can access these assays as:
x <- assay(derASM, "der.ASM")
head(x)## NORM1 CRC1
## 19.266963 0.00000000 NA
## 19.266999 0.00000000 0.018315018
## 19.267087 0.02380952 0.000000000
## 19.267097 0.03968254 0.020000000
## 19.267103 0.02020906 0.000000000
## 19.267109 0.05000000 0.0059076264.1.3 Find DAMEs
Now we detect regions that show differential ASM. The function find_dames()
performs several steps:
- Obtains a moderated t-statistic per CpG site using
lmFit()andeBayes()from thelimmapackage. The statistic reflects a measure of difference between the conditions being compared, in this case normal Vs cancer. The t-statistic is optionally smoothed (smoothparameter).
After this, two methods can be chosen (pvalAssign parameter):
- Simes method:
- (Default) Clusters of CpG sites are determined by closeness (
maxGap), and a p-value for each cluster is calculated using the simes method, similar to the packagecsawfrom Lun and Smyth (2014). With this approach, the p-value represents evidence against the null hypothesis that no sites are differential in the cluster.
- (Default) Clusters of CpG sites are determined by closeness (
- Bumphunting method:
- CpG sites with a t-statistic above and below a certain cutoff (set with
Q), are grouped into segments (after being clustered). This is done with theregionFinder()function frombumphunter(Jaffe et al. 2012). - For each of these segments, a p-value is calculated empirically by
permuting the groups (covariate) of interest. Depending on the number of
samples, this can take longer than the Simes method. However the number of
permutations can be controlled with
maxPerms.
- CpG sites with a t-statistic above and below a certain cutoff (set with
Here we show an example with a pre-processed set of samples: 4 colorectal cancer samples, and their paired normal mucosa:
data(extractbams_output)
#The data loaded is an output from `split_bams()`, therefore we run
#`calc_derivedasm` to get the SummarizedExperiment
derASM <- calc_derivedasm(extractbams_output, cores = 1, verbose = FALSE)
#We remove all CpG sites with any NA values, but not 0s
filt <- rowSums(!is.na(assay(derASM, "der.ASM"))) == 8
derASM <- derASM[filt,]
#set the design matrix
grp <- factor(c(rep("CRC",4),rep("NORM",4)), levels = c("NORM", "CRC"))
mod <- model.matrix(~grp)
mod## (Intercept) grpCRC
## 1 1 1
## 2 1 1
## 3 1 1
## 4 1 1
## 5 1 0
## 6 1 0
## 7 1 0
## 8 1 0
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$grp
## [1] "contr.treatment"#Run default
dames <- find_dames(derASM, mod)## Using ASMsnp## Calculating moderated t-statistics## Smoothing moderated t-statistics## Detecting DAMEs## 12 DAMEs found.head(dames)## chr start end meanTstat sumTstat pvalSimes clusterL numup numdown
## 9 19 388375 388375 1.6961319 1.6961319 0.1256264 1 1 0
## 7 19 388049 388094 0.7068111 3.5340553 0.1625003 5 3 2
## 3 19 292528 292528 -0.9472183 -0.9472183 0.3693545 1 0 1
## 2 19 292499 292499 0.9428371 0.9428371 0.3714680 1 1 0
## 4 19 292578 292578 -0.7244911 -0.7244911 0.4879850 1 0 1
## 5 19 387966 387983 0.8596840 3.4387358 0.6238076 4 4 0
## FDR
## 9 0.9750021
## 7 0.9750021
## 3 0.9803244
## 2 0.9803244
## 4 0.9803244
## 5 0.9803244#Run empirical method
dames <- find_dames(derASM, mod, pvalAssign = "empirical")## Using ASMsnp## Calculating moderated t-statistics## Smoothing moderated t-statistics## Detecting DAMEs## getSegments: segmenting## getSegments: splitting## Generating 10 permutations## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## Calculating moderated t-statistics## Smoothing moderated t-statistics## 11 DAMEs found.head(dames)## chr start end meanTstat sumTstat segmentL clusterL pvalEmp FDR
## 5 19 388049 388055 1.4428937 2.885787 2 5 0.1132075 0.7610063
## 6 19 388094 388094 2.4413268 2.441327 1 5 0.1603774 0.7610063
## 11 19 388073 388090 -0.8965295 1.793059 2 5 0.3207547 0.7610063
## 8 19 388375 388375 1.6961319 1.696132 1 1 0.3584906 0.7610063
## 3 19 387983 387983 1.5547490 1.554749 1 4 0.4150943 0.7610063
## 7 19 388340 388350 0.7805881 1.561176 2 8 0.4150943 0.7610063A significant p-value represent regions where samples belonging to one group (in this case the cancer samples), gain or lose allele-specificity compared to the other group (here the normal group).
5 tuple-based (aka fast-mode)
Before running the tuple-based mode, you must obtain files from the methtuple
tool to input them in the read_tuples function.
5.1 Run Methtuple on bam files
Methtuple requires the input BAM files of paired-end reads to be sorted by
query name. For more information on the options in methtuple, refer to the
user guide. For example the --sc
option combines strand information.
# Sort bam file by query name
samtools sort -n -@ 10 -m 20G -O bam -T _tmp
-o sample1_pe_sorted.bam sample1_pe.deduplicated.bam
# Run methtuple
methtuple --sc --gzip -m 2 sample1_pe_sorted.bam5.2 Example Workflow
5.2.1 Read methtuple files
We use the same samples as above to run methtuple and obtain .tsv.gz files.
We read in these files using read_tuples and obtain a list of tibbles, each
one for every sample:
tuple_files <- c(system.file("extdata", "NORM1_chr19.qs.CG.2.tsv.gz",
package = "DAMEfinder"),
system.file("extdata", "CRC1_chr19.qs.CG.2.tsv.gz",
package = "DAMEfinder"))
sample_names <- c("NORM1", "CRC1")
tuple_list <- read_tuples(tuple_files, sample_names)## Reading D:/biocbuild/bbs-3.14-bioc/tmpdir/Rtmp8OqJjU/Rinst1964684428a9/DAMEfinder/extdata/NORM1_chr19.qs.CG.2.tsv.gz## Reading D:/biocbuild/bbs-3.14-bioc/tmpdir/Rtmp8OqJjU/Rinst1964684428a9/DAMEfinder/extdata/CRC1_chr19.qs.CG.2.tsv.gz## Filtering and sorting: .. done.head(tuple_list$NORM1)## # A tibble: 6 x 10
## chr strand pos1 pos2 MM MU UM UU cov inter_dist
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 19 * 267086 267096 1 0 4 93 98 10
## 2 19 * 267096 267102 2 3 0 94 99 6
## 3 19 * 267102 267108 1 1 1 95 98 6
## 4 19 * 267108 267113 2 0 2 94 98 5
## 5 19 * 267113 267116 1 3 0 94 98 3
## 6 19 * 267116 267120 0 1 1 95 97 4Each row in the tibble displays a tuple. The chromosome name and strand are
shown followed by pos1 and pos2, which refer to the genomic positions of the
first and second CpG in the tuple. The MM, MU, UM, and UU counts of the
tuple are displayed where M stands for methylated and U for unmethylated.
For example, UM shows the read counts for the instances where pos1 is
unmethylated and pos2 is methylated. The coverage and distance between the two
genomic positions in the tuple are shown under cov and inter_dist
respectively.
5.2.2 Calculate ASM Score
The calc_asm function takes the output from read_tuples(), and as in the
SNP-based mode, generates a RangedSummarizedExperiment where each row is a
tuple and each column is a sample. The object contains 6 assays including the
MM, MU, UM, and UU counts, as well as the total coverage and the
tuple-based ASM score. This score is a measure of ASM calculated directly from
the reads without the need of SNP information. Because of this, it is a lot
quicker than the SNP-based ASM, and is useful for more explorative purposes.
Equations (1), (2) and (3) show
how the score is calculated. The log odds ratio in equation (1)
provides a higher score the more MM and UU counts the tuple has, whereas a
higher UM and MU would indicate “random” methylation. The weight further
adds allele-specificity where a rather balanced MM:UU increases the score.
\[\begin{equation} ASM^{(i)} = log{ \Big\{ \frac{X_{MM}^{(i)} \cdot X_{UU}^{(i)}}{X_{MU}^{(i)} \cdot X_{UM}^{(i)}} \Big\} \cdot w_i } \tag{1} \end{equation}\]
\[\begin{equation} w_i = P(0.5-\epsilon < \theta < 0.5+\epsilon~|~ X_{MM}^{(i)}, X_{UU}^{(i)}, \beta_1, \beta_2) \tag{2} \end{equation}\]
\[\begin{equation} \theta^{(i)} | X_{MM}^{(i)}, X_{UU}^{(i)},\beta_1, \beta_2 \sim Beta(\beta_1+X_{MM}^{(i)}, \beta_2+X_{UU}^{(i)}) \tag{3} \end{equation}\]
where \(\theta^{(i)}\) represents the moderated proportion of MM to MM+UU alleles. The weight, \(w_i\) is set such that the observed split between MM and UU alleles can depart somewhat from 50/50, while fully methylated or unmethylated tuples, which represents evidence for absence of allele-specificity, are attenuated to 0. The degree of allowed departure can be set according to \(\epsilon\), the deviation from 50/50 allowed and the level of moderation, \(\beta_1\) and \(\beta_2\).
ASM_mat <- calc_asm(tuple_list)## Calculating log odds.## Calculating ASM score: .. done.
## Creating position pair keys: .. done.
## Assembling table: .. done.
## Transforming.
## Assembling coverage tables: ..........Returning SummarizedExperiment with 3005 CpG pairsASM_mat## class: RangedSummarizedExperiment
## dim: 1825 2
## metadata(0):
## assays(6): asm cov ... UM UU
## rownames(1825): 19.267086.267096 19.267096.267102 ... 19.469008.469024
## 19.469051.469066
## rowData names(1): midpt
## colnames(2): NORM1 CRC1
## colData names(0):5.2.3 Find DAMEs
As above, the RangedSummarizedExperiment is used to detect differential ASM.
Here we show an example with a pre-processed set of samples: 3 colorectal cancer
samples, an 2 normal mucosa samples
#load package data
data(readtuples_output)
#run calc_asm and filter object
ASMscore <- calc_asm(readtuples_output)## Calculating log odds.## Calculating ASM score: ..... done.
## Creating position pair keys: ..... done.
## Assembling table: ..... done.
## Transforming.
## Assembling coverage tables: .........................Returning SummarizedExperiment with 3361 CpG pairsfilt <- rowSums(!is.na(assay(ASMscore, "asm"))) == 5 #filt to avoid warnings
ASMscore <- ASMscore[filt,]
#make design matrix (or specify a contrast)
grp <- factor(c(rep("CRC",3),rep("NORM",2)), levels = c("NORM", "CRC"))
mod <- model.matrix(~grp)
#run default and increase maxGap to get longer, more sparse regions
dames <- find_dames(ASMscore, mod, maxGap = 300)## Using ASMtuple
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Detecting DAMEs
## 78 DAMEs found.head(dames)## chr start end meanTstat sumTstat pvalSimes clusterL numup numdown
## 52 19 424232 424419 2.060434 14.42304 0.005531465 7 7 0
## 33 19 385930 385974 3.325203 16.62602 0.008697161 5 4 1
## 18 19 323736 324622 1.913688 44.01483 0.021421423 23 23 0
## 23 19 359435 359579 1.976496 11.85897 0.025508984 6 6 0
## 38 19 400995 401194 1.770387 19.47425 0.030898659 11 11 0
## 42 19 407086 407360 -1.290909 -25.81818 0.042587680 20 0 20
## FDR
## 52 0.3391893
## 33 0.3391893
## 18 0.4820191
## 23 0.4820191
## 38 0.4820191
## 42 0.4995466#run alternative mode
dames <- find_dames(ASMscore, mod, maxGap = 300, pvalAssign = "empirical")## Using ASMtuple
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Detecting DAMEs
## getSegments: segmenting
## getSegments: splitting
## Generating 6 permutations
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## Calculating moderated t-statistics
## Smoothing moderated t-statistics
## 78 DAMEs found.head(dames)## chr start end meanTstat sumTstat segmentL clusterL pvalEmp FDR
## 56 19 460493 461064 1.0292896 96.75322 94 112 0.003539823 0.2070796
## 42 19 428886 430321 0.9433651 59.43200 63 67 0.005309735 0.2070796
## 17 19 360074 360660 0.9417149 45.20231 48 150 0.015929204 0.3451327
## 12 19 323736 324622 1.9136883 44.01483 23 23 0.017699115 0.3451327
## 36 19 418902 419309 1.0129308 25.32327 25 27 0.053097345 0.4693805
## 7 19 308732 308925 1.1983194 25.16471 21 35 0.054867257 0.46938056 Visualization
6.1 DAME tracks
After detecting a set of DAMEs you want to look at them individually. We do this
with the function dame_track.
Depending on which object I used to obtain my DAMEs (tuple or SNP mode), I
choose which SummarizedExperiment to input in the field ASM (for tuple), or
derASM (for SNP). Either way, the SummarizedExperiment must have the columns
group and samples in the colData field:
#Here I will use the tuple-ASM SummExp
colData(ASMscore)$group <- grp
colData(ASMscore)$samples <- colnames(ASMscore)
#Set a DAME as a GRanges. I choose a one from the tables we obtained above
dame <- GRanges(19,IRanges(323736,324622))
dame_track(dame = dame,
ASM = ASMscore)## Using ASMtuple score
Because we used the tuple-ASM object, we get by default two tracks: the ASM score, and the marginal methylation (aka beta-value).
The shaded square delimits the DAME we defined to plot. We can look at the
flanking regions of the DAME by changing window or positions. With window
we specify the number of CpG positions we want to add to the plot up and
down-stream. With positions we specify the number of base pairs.
dame_track(dame = dame,
ASM = ASMscore,
window = 2)## Using ASMtuple score
If we use the SNP-ASM as input we get different tracks:
dame <- GRanges(19,IRanges(387966,387983))
grp <- factor(c(rep("CRC",4),rep("NORM",4)), levels = c("NORM", "CRC"))
colData(derASM)$group <- grp
dame_track(dame = dame,
derASM = derASM)## Using ASMsnp score
Here we get three tracks: the SNP-ASM score, and the methylation levels for each allele. Since the ASM score here depends on SNPs, we can see what SNPs are involved in the ASM calculation at each CpG position:
dame_track(dame = dame,
derASM = derASM,
plotSNP = TRUE)## Using ASMsnp score
We see that the SNP located at chr19:388,065 was the one used to split the
allele methylation.
If you put both SummarizedExperiments with a single DAME, you would get all the tracks:
dame_track(dame = dame,
derASM = derASM,
ASM = ASMscore)## Using both scores
Notice that the first two tracks depend on the tuple-ASM, hence each point represents the midpoint between a pair of CpG sites.
If you think plotting all the samples separately is difficult to see, you can
use the function dame_track_mean to summarize:
dame_track_mean(dame = dame,
derASM = derASM,
ASM = ASMscore)## Using both scores
As you can see, this region is not a very good DAME.
6.2 Methyl-circle plot
A typical way of visualizing ASM is to look at the reads overlapping a
particular SNP, and the methylation state of the CpG sites in those reads (black
circles for methylated and white for unmethylated, see Shoemaker et al. (2010) for
examples). Here we offer this option with the function methyl_circle_plot().
As input it takes a GRanges with the SNP of interest, and the bam, VCF and
reference files as in the extract_bams() function.
#put SNP in GRanges (you can find the SNP with the dame_track function)
snp <- GRanges(19, IRanges(267039, width = 1)) #always set the width if your
#GRanges has 1 site
snp## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] 19 267039 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsbam.file <- system.file("extdata", "CRC1_chr19_trim.bam",
package = "DAMEfinder")
vcf.file <- system.file("extdata", "CRC1.chr19.trim.vcf",
package = "DAMEfinder")
methyl_circle_plot(snp = snp, vcfFile = vcf.file, bamFile = bam.file,
refFile = reference_file)## Reading vcf file## Getting reads## Sorting reads## Reading reference## Getting meth state per read-pair## Plotting## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.## Warning: Removed 1081 rows containing missing values (geom_point).
You can reduce the number of reads included with the option sampleReads, which
performs a random sampling of the number of reads to be shows per allele. The
number of reads can be specified with numReads.
If you are interested in a specific CpG site within this plot, you can include
an extra GRanges with its location, and the triangle at the bottom will point
to it:
cpgsite <- GRanges(19, IRanges(266998, width = 1))
methyl_circle_plot(snp = snp, vcfFile = vcf.file, bamFile = bam.file,
refFile = reference_file, cpgsite = cpgsite)## Reading vcf file## Getting reads## Sorting reads## Reading reference## Getting meth state per read-pair## Plotting## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.## Warning: Removed 1081 rows containing missing values (geom_point).
If you are instead interested in reads overlapping a CpG site, you can use
methyl_circle_plotCpG(), which is useful if you run the tuple-mode:
cpgsite <- GRanges(19, IRanges(266998, width = 1))
methyl_circle_plotCpG(cpgsite = cpgsite, bamFile = bam.file,
refFile = reference_file)## Reading reference## Getting meth state per read-pair## Plotting## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.## Warning: Removed 230 rows containing missing values (geom_point).
You can also limit both the SNP plot and the CpG plot to a specific window of interest (to zoom in or out), or if you want to look at the specific DAME region:
#a random region
dame <- GRanges(19, IRanges(266998,267100))
methyl_circle_plot(snp = snp, vcfFile = vcf.file, bamFile = bam.file,
refFile = reference_file, dame = dame)## Reading vcf file## Getting reads## Sorting reads## Reading reference## Getting meth state per read-pair## Plotting## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.## Warning: Removed 61 rows containing missing values (geom_point).
6.3 MDS plot
To plot a multidimensional scaling plot (MDS), we provide a wrapper to
plotMDS() from limma, which adjusts the ASM score to calculate the euclidean
distances. The input is a SummarizedExperiment, and the vector of covariates
to color the points by:
grp <- factor(c(rep("CRC",3),rep("NORM",2)), levels = c("NORM", "CRC"))
methyl_MDS_plot(ASMscore, group = grp)
7 Session Info
utils::sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.62.0
## [3] rtracklayer_1.54.0 Biostrings_2.62.0
## [5] XVector_0.34.0 SummarizedExperiment_1.24.0
## [7] Biobase_2.54.0 GenomicRanges_1.46.0
## [9] GenomeInfoDb_1.30.0 IRanges_2.28.0
## [11] S4Vectors_0.32.0 BiocGenerics_0.40.0
## [13] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [15] DAMEfinder_1.6.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5 filelock_1.0.2
## [4] progress_1.2.2 httr_1.4.2 doRNG_1.8.2
## [7] tools_4.1.1 bslib_0.3.1 utf8_1.2.2
## [10] R6_2.5.1 DBI_1.1.1 colorspace_2.0-2
## [13] tidyselect_1.1.1 prettyunits_1.1.1 bit_4.0.4
## [16] curl_4.3.2 compiler_4.1.1 cli_3.0.1
## [19] xml2_1.3.2 DelayedArray_0.20.0 labeling_0.4.2
## [22] bookdown_0.24 sass_0.4.0 scales_1.1.1
## [25] readr_2.0.2 rappdirs_0.3.3 stringr_1.4.0
## [28] digest_0.6.28 Rsamtools_2.10.0 rmarkdown_2.11
## [31] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9
## [34] limma_3.50.0 dbplyr_2.1.1 fastmap_1.1.0
## [37] rlang_0.4.12 RSQLite_2.2.8 farver_2.1.0
## [40] jquerylib_0.1.4 BiocIO_1.4.0 generics_0.1.1
## [43] jsonlite_1.7.2 vroom_1.5.5 BiocParallel_1.28.0
## [46] dplyr_1.0.7 VariantAnnotation_1.40.0 RCurl_1.98-1.5
## [49] magrittr_2.0.1 GenomeInfoDbData_1.2.7 Matrix_1.3-4
## [52] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
## [55] lifecycle_1.0.1 stringi_1.7.5 yaml_2.2.1
## [58] zlibbioc_1.40.0 bumphunter_1.36.0 plyr_1.8.6
## [61] BiocFileCache_2.2.0 grid_4.1.1 blob_1.2.2
## [64] parallel_4.1.1 crayon_1.4.1 lattice_0.20-45
## [67] cowplot_1.1.1 GenomicFeatures_1.46.0 hms_1.1.1
## [70] KEGGREST_1.34.0 magick_2.7.3 locfit_1.5-9.4
## [73] knitr_1.36 pillar_1.6.4 rjson_0.2.20
## [76] rngtools_1.5.2 reshape2_1.4.4 codetools_0.2-18
## [79] biomaRt_2.50.0 XML_3.99-0.8 glue_1.4.2
## [82] evaluate_0.14 BiocManager_1.30.16 tzdb_0.1.2
## [85] foreach_1.5.1 vctrs_0.3.8 png_0.1-7
## [88] gtable_0.3.0 purrr_0.3.4 assertthat_0.2.1
## [91] cachem_1.0.6 ggplot2_3.3.5 xfun_0.27
## [94] restfulr_0.0.13 tibble_3.1.5 iterators_1.0.13
## [97] GenomicAlignments_1.30.0 AnnotationDbi_1.56.0 memoise_2.0.0
## [100] ellipsis_0.3.2References
Cui, Hengmi, Patrick Onyango, Sheri Brandenburg, Yiqian Wu, Chih-Lin Hsieh, and Andrew P. Feinberg. 2002. “Loss of Imprinting in Colorectal Cancer Linked to Hypomethylation of H19 and IGF2.” Cancer Research 62 (22): 6442–6. http://cancerres.aacrjournals.org/content/62/22/6442.
Hanna, Courtney W., and Gavin Kelsey. 2017. “Genomic Imprinting Beyond DNA Methylation: A Role for Maternal Histones.” Genome Biology 18 (1): 177. https://doi.org/10.1186/s13059-017-1317-9.
Hickey, Peter. 2015. “Methtuple.” https://github.com/PeteHaitch/methtuple.
Hu, Bo, Yuan Ji, Yaomin Xu, and Angela H. Ting. 2013. “Screening for SNPs with Allele-Specific Methylation Based on Next-Generation Sequencing Data.” Statistics in Biosciences 5 (1): 179–97. https://doi.org/10.1007/s12561-013-9086-9.
Jaffe, Andrew E, Andrew P Feinberg, Hwajin Lee, Jeffrey T Leek, M Daniele Fallin, Peter Murakami, and Rafael A Irizarry. 2012. “Bump Hunting to Identify Differentially Methylated Regions in Epigenetic Epidemiology Studies.” International Journal of Epidemiology 41 (1): 200–209. https://doi.org/10.1093/ije/dyr238.
Kelsey, Gavin, and Robert Feil. 2013. “New Insights into Establishment and Maintenance of DNA Methylation Imprints in Mammals.” Philosophical Transactions of the Royal Society of London B: Biological Sciences 368 (1609). https://doi.org/10.1098/rstb.2011.0336.
Lun, Aaron T. L., and Gordon K. Smyth. 2014. “De Novo Detection of Differentially Bound Regions for ChIP-seq Data Using Peaks and Windows: Controlling Error Rates Correctly.” Nucleic Acids Research 42 (11): e95. https://doi.org/10.1093/nar/gku351.
Parker, Hannah R., Stephany Orjuela, Andreia Martinho Oliveira, Fabrizio Cereatti, Matthias Sauter, Henriette Heinrich, Giulia Tanzi, et al. 2018. “The Proto CpG Island Methylator Phenotype of Sessile Serrated Adenomas/Polyps.” Epigenetics 13 (10-11): 1088–1105. https://doi.org/10.1080/15592294.2018.1543504.
Shoemaker, Robert, Jie Deng, Wei Wang, and Kun Zhang. 2010. “Allele-Specific Methylation Is Prevalent and Is Contributed by CpG-SNPs in the Human Genome.” Genome Research 20 (7): 883–89. https://doi.org/10.1101/gr.104695.109.