Introduction

To install this package, run

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("NanoMethViz")

library(NanoMethViz)

To generate a methylation plot we need 3 components:

methylation data in tabix format
annotation of exons
annotation of samples

The methylation information has been modified from the output of nanopolish/f5c. It has then been compressed and indexed using bgzip() and indexTabix() from the Rsamtools package.

# methylation data stored in tabix file
methy <- system.file(package = "NanoMethViz", "methy_subset.tsv.bgz")

# tabix is just a special gzipped tab-separated-values file
read.table(gzfile(methy), col.names = methy_col_names(), nrows = 6)

##               sample   chr       pos strand statistic
## 1  B6Cast_Prom_1_bl6 chr11 101463573      *     -0.33
## 2  B6Cast_Prom_1_bl6 chr11 101463573      *     -1.87
## 3  B6Cast_Prom_1_bl6 chr11 101463573      *     -4.19
## 4  B6Cast_Prom_1_bl6 chr11 101463573      *      0.10
## 5 B6Cast_Prom_1_cast chr11 101463573      *     -0.38
## 6 B6Cast_Prom_1_cast chr11 101463573      *     -0.84
##                              read_name
## 1 6cc38b35-6570-4b44-a1e3-2605fcf2ffe8
## 2 787f5f43-d144-4e15-ab7d-6b1474083389
## 3 c7ee7fb4-a915-4da7-9f36-da6ed5e68af2
## 4 bff8b135-0296-4495-9354-098242ea8cc4
## 5 11fe130b-8d48-4399-a9fa-2ca2860fa355
## 6 502fef95-c2f2-46ad-9bc5-fb3fc80b4245

The exon annotation was obtained from the Mus.musculus package, and joined into a single table. It is important that the chromosomes share the same convention as that found in the methylation data.

# helper function extracts exons from Mus.musculus package
exon_tibble <- get_exons_mus_musculus()

## Loading required package: Mus.musculus

## Loading required package: AnnotationDbi

## Loading required package: stats4

## Loading required package: BiocGenerics

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs

## The following objects are masked from 'package:base':
## 
##     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
##     as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
##     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
##     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
##     tapply, union, unique, unsplit, which.max, which.min

## Loading required package: Biobase

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## 
## Attaching package: 'Biobase'

## The following object is masked from 'package:NanoMethViz':
## 
##     samples

## Loading required package: IRanges

## Loading required package: S4Vectors

## 
## Attaching package: 'S4Vectors'

## The following objects are masked from 'package:base':
## 
##     I, expand.grid, unname

## Loading required package: OrganismDbi

## Loading required package: GenomicFeatures

## Loading required package: GenomeInfoDb

## Loading required package: GenomicRanges

## 
## Attaching package: 'GenomicFeatures'

## The following object is masked from 'package:NanoMethViz':
## 
##     exons

## Loading required package: GO.db

##

## Loading required package: org.Mm.eg.db

##

## Loading required package: TxDb.Mmusculus.UCSC.mm10.knownGene

head(exon_tibble)

## # A tibble: 6 x 7
##   gene_id   chr   strand    start      end transcript_id symbol
##   <chr>     <chr> <chr>     <int>    <int>         <int> <chr> 
## 1 100009600 chr9  -      21062393 21062717         74536 Zglp1 
## 2 100009600 chr9  -      21062894 21062987         74536 Zglp1 
## 3 100009600 chr9  -      21063314 21063396         74536 Zglp1 
## 4 100009600 chr9  -      21066024 21066377         74536 Zglp1 
## 5 100009600 chr9  -      21066940 21067093         74536 Zglp1 
## 6 100009600 chr9  -      21062400 21062717         74538 Zglp1

We will defined the sample annotation ourselves. It is important that the sample names match those found in the methylation data.

sample <- c(
  "B6Cast_Prom_1_bl6",
  "B6Cast_Prom_1_cast",
  "B6Cast_Prom_2_bl6",
  "B6Cast_Prom_2_cast",
  "B6Cast_Prom_3_bl6",
  "B6Cast_Prom_3_cast"
)

group <- c(
  "bl6",
  "cast",
  "bl6",
  "cast",
  "bl6",
  "cast"
)

sample_anno <- data.frame(sample, group, stringsAsFactors = FALSE)

sample_anno

##               sample group
## 1  B6Cast_Prom_1_bl6   bl6
## 2 B6Cast_Prom_1_cast  cast
## 3  B6Cast_Prom_2_bl6   bl6
## 4 B6Cast_Prom_2_cast  cast
## 5  B6Cast_Prom_3_bl6   bl6
## 6 B6Cast_Prom_3_cast  cast

For convenience we assemble these three pieces of data into a single object.

nmeth_results <- NanoMethResult(methy, sample_anno, exon_tibble)

The genes we have available are

Peg3
Meg3
Impact
Xist
Brca1
Brca2

For demonstrative purposes we will plot Peg3.

plot_gene(nmeth_results, "Peg3")

We can also load in some DMR results to highlight DMR regions.

# loading saved results from previous bsseq analysis
bsseq_dmr <- read.table(
    system.file(package = "NanoMethViz", "dmr_subset.tsv.gz"),
    sep = "\t",
    header = TRUE,
    stringsAsFactors = FALSE
)

plot_gene(nmeth_results, "Peg3", anno_regions = bsseq_dmr)

Individual long reads can be visualised using the spaghetti argument.

# warnings have been turned off in this vignette, but this will generally
# generate many warnings as the smoothing for many reads will fail
plot_gene(nmeth_results, "Peg3", anno_regions = bsseq_dmr, spaghetti = TRUE)

Introduction

Shian Su