# Biscuiteer User Guide

knitr::opts_chunk$set( warning=FALSE, message=FALSE )

# Biscuiteer

`biscuiteer` is package to process output from biscuit into bsseq objects. It includes a number of features, such as VCF header parsing, shrunken M-value calculations (which can be used for compartment inference), and age inference. However, the task of locus- and region-level differential methylation inference is delegated to other packages (such as `dmrseq`).

# Quick Start

## Installing

From Bioconductor,

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

A development version is available on GitHub and can be installed via:

    if (!requireNamespace("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("trichelab/biscuiteerData")
    BiocManager::install("trichelab/biscuiteer")

## Loading Methylation Data

`biscuiteer` can load either headered or header-free BED files produced from `biscuit vcf2bed` or `biscuit mergecg`. In either case, a VCF file is needed when loading `biscuit` output. For practical purposes, only the VCF header is for `biscuiteer`. However, it is encouraged that the user keep the entire VCF, as `biscuit` can be used to call SNVs and allows for structural variant detection in a similar manner to typical whole-genome sequencing tools. Furthermore, `biscuit` records the version of the software and the calling arguments used during processing the output VCF, which allows for better reproducibility.

NOTE: Both the input BED and VCF files must be tabix’ed before being input to `biscuiteer`. This can be done by running `bgzip biscuit_output.xxx` followed by `tabix -p xxx biscuit_output.xxx.gz`, where `xxx` is either `bed` or `vcf`.

Data can be loaded using the `readBiscuit` function in `biscuiteer`:

    library(biscuiteer)

    ## Loading required package: biscuiteerData

    ## Loading required package: ExperimentHub

    ## Loading required package: BiocGenerics

    ## Loading required package: generics

    ##
    ## Attaching package: 'generics'

    ## The following objects are masked from 'package:base':
    ##
    ##     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    ##     setequal, union

    ##
    ## Attaching package: 'BiocGenerics'

    ## The following objects are masked from 'package:stats':
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    ##     rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
    ##     unsplit, which.max, which.min

    ## Loading required package: AnnotationHub

    ## Loading required package: BiocFileCache

    ## Loading required package: dbplyr

    ## Loading biscuiteerData.

    ## Loading required package: bsseq

    ## Loading required package: GenomicRanges

    ## Loading required package: stats4

    ## Loading required package: S4Vectors

    ##
    ## Attaching package: 'S4Vectors'

    ## The following object is masked from 'package:utils':
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    ## The following objects are masked from 'package:base':
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    ## Loading required package: IRanges

    ## Loading required package: Seqinfo

    ## Loading required package: SummarizedExperiment

    ## Loading required package: MatrixGenerics

    ## Loading required package: matrixStats

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    ## The following objects are masked from 'package:matrixStats':
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    ##     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    ##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    ##     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    ##     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    ##     colWeightedMeans, colWeightedMedians, colWeightedSds,
    ##     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    ##     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    ##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    ##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    ##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    ##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    ##     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    ##     rowWeightedSds, rowWeightedVars

    ## 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")'.

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    ##

    ##

    ## Warning: replacing previous import 'BiocParallel::bpstart' by
    ## 'QDNAseq::bpstart' when loading 'biscuiteer'

    orig_bed <- system.file("extdata", "MCF7_Cunha_chr11p15.bed.gz",
                            package="biscuiteer")
    orig_vcf <- system.file("extdata", "MCF7_Cunha_header_only.vcf.gz",
                            package="biscuiteer")
    bisc <- readBiscuit(BEDfile = orig_bed, VCFfile = orig_vcf,
                        merged = FALSE)

    ## Checking /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15.bed.gz for import...

    ## Extracting sample names from /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_header_only.vcf.gz...

    ## /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15.bed.gz does not have a header. Using VCF file header information to help set column names.

    ## Assuming unmerged data. Checking now... ...The file might be alright. Double check if you're worried.
    ## /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15.bed.gz has 254147 indexed loci.
    ## /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15.bed.gz looks valid for import.
    ## Reading unmerged input from /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15.bed.gz...
    ## Excluding CpG sites with uniformly zero coverage...
    ## Loaded /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15.bed.gz. Creating bsseq object......Done!

Metadata from the `biscuit` output can be viewed via:

    biscuitMetadata(bisc)

    ## CharacterList of length 3
    ## [["Reference genome"]] hg19.fa
    ## [["Biscuit version"]] 0.1.3.20160324
    ## [["Invocation"]] biscuit pileup -r /primary/vari/genomicdata/genomes/hg19/hg1...

## Combining Methylation Results

In the instance where you have two separate BED files that you would like to analyze in a single bsseq object, you can combine the files using `unionize`, which is a wrapper around the BiocGenerics function, `combine`.

    shuf_bed <- system.file("extdata", "MCF7_Cunha_chr11p15_shuffled.bed.gz",
                            package="biscuiteer")
    shuf_vcf <- system.file("extdata",
                            "MCF7_Cunha_shuffled_header_only.vcf.gz",
                            package="biscuiteer")
    bisc2 <- readBiscuit(BEDfile = shuf_bed, VCFfile = shuf_vcf,
                         merged = FALSE)

    ## Checking /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15_shuffled.bed.gz for import...

    ## Extracting sample names from /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_shuffled_header_only.vcf.gz...

    ## /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15_shuffled.bed.gz does not have a header. Using VCF file header information to help set column names.

    ## Assuming unmerged data. Checking now... ...The file might be alright. Double check if you're worried.
    ## /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15_shuffled.bed.gz has 254147 indexed loci.
    ## /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15_shuffled.bed.gz looks valid for import.
    ## Reading unmerged input from /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15_shuffled.bed.gz...
    ## Excluding CpG sites with uniformly zero coverage...
    ## Loaded /tmp/Rtmp3Pjbs1/Rinst269343263b44/biscuiteer/extdata/MCF7_Cunha_chr11p15_shuffled.bed.gz. Creating bsseq object......Done!

    comb <- unionize(bisc, bisc2)

## Loading epiBED files

The epiBED file format provides an easy way to analyze read- or fragment-level methylation and genetic information at the same time. `readEpibed` provides functionality for parsing the RLE strings found in the epiBED file into a GRanges object for analysis in R.

NOTE: The input file must be bgzip’ed and tabix’ed.

    epibed.nome <- system.file("extdata", "hct116.nome.epibed.gz", package="biscuiteer")
    epibed.bsseq <- system.file("extdata", "hct116.bsseq.epibed.gz", package="biscuiteer")
    epibed.nome.gr <- readEpibed(epibed = epibed.nome, genome = "hg19", chr = "chr1")

    ## Decoding RLE and converting to GRanges

    ## Collapsing to fragment level

    ## This will take some time if a large region is being analyzed

    epibed.bsseq.gr <- readEpibed(epibed = epibed.bsseq, genome = "hg19", chr = "chr1")

    ## Decoding RLE and converting to GRanges

    ## Collapsing to fragment level

    ## This will take some time if a large region is being analyzed

# Analysis Functionality

A handful of analysis paths are available in `biscuiteer`, including A/B compartment inference, age estimation from WGBS data, hypermethylation of Polycomb Repressor Complex (PRC) binding sites, and hypomethylation of CpG-poor “partially methylated domains” (PMDs).

## Inputs for A/B Compartment Inference

When performing A/B compartment inference, the goal is to have something that has roughly gaussian error. `getLogitFracMeth` uses Dirichlet smoothing to turn raw measurements into lightly moderated, logit-transformed methylated-fraction estimates, which can the be used as inputs to compartmap

    reg <- GRanges(seqnames = rep("chr11",5),
                   strand = rep("*",5),
                   ranges = IRanges(start = c(0,2.8e6,1.17e7,1.38e7,1.69e7),
                                    end= c(2.8e6,1.17e7,1.38e7,1.69e7,2.2e7))
                  )

    frac <- getLogitFracMeth(bisc, minSamp = 1, r = reg)
    frac

    ## GRanges object with 5 ranges and 1 metadata column:
    ##       seqnames            ranges strand | MCF7_Cunha
    ##          <Rle>         <IRanges>  <Rle> |  <numeric>
    ##   [1]    chr11         0-2800000      * |   1.340682
    ##   [2]    chr11  2800000-11700000      * |   0.575875
    ##   [3]    chr11 11700000-13800000      * |   1.162989
    ##   [4]    chr11 13800000-16900000      * |   0.581874
    ##   [5]    chr11 16900000-22000000      * |   0.442985
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

## Age Estimation

`biscuiteer` has the functionality to guess the age of the sample(s) provided using the Horvath-style “clock” models (see Horvath, 2013 for more information).

NOTE: The prediction accuracy of this function is entirely dependent on the parameters set by the user. As such, the defaults (as shown in the example below) should only be used as a starting point for exploration by the user.

NOTE: Please cite the appropriate papers for the epigenetic “clock” chosen:

- For `horvath` or `horvathshrunk`
  - Horvath, Genome Biology, 2013
- For `hannum`
  - Hannum et al., Molecular Cell, 2013
- For `skinandblood`
  - Horvath et al., Aging, 2018

    ages <- WGBSage(comb, "horvath")

    ## Assessing coverage across age-associated regions...

    ## You have NAs. Change `padding` (15), `minCovg` (5), `useHMMI`, and/or `useENSR`. You have 702 positions in coverage matrix (regions x samples) with less than 5 minCovg. This represents 99.43 % missing data

    ages

    ## $call
    ## WGBSage(comb, "horvath")
    ##
    ## $droppedSamples
    ## NULL
    ##
    ## $droppedRegions
    ## NULL
    ##
    ## $intercept
    ## [1] 0.6955073
    ##
    ## $methcoefs
    ## GRanges object with 353 ranges and 3 metadata columns:
    ##                           seqnames            ranges strand | MCF7_Cunha
    ##                              <Rle>         <IRanges>  <Rle> |  <numeric>
    ##      chr1:1168022-1168051     chr1   1168022-1168051      * |         NA
    ##    chr1:19746550-19746579     chr1 19746550-19746579      * |         NA
    ##    chr1:23858021-23858050     chr1 23858021-23858050      * |         NA
    ##    chr1:32084950-32084979     chr1 32084950-32084979      * |         NA
    ##    chr1:32687553-32687582     chr1 32687553-32687582      * |         NA
    ##                       ...      ...               ...    ... .        ...
    ##   chr22:42322132-42322161    chr22 42322132-42322161      * |         NA
    ##   chr22:43506007-43506036    chr22 43506007-43506036      * |         NA
    ##   chr22:46449447-46449476    chr22 46449447-46449476      * |         NA
    ##   chr22:46450093-46450122    chr22 46450093-46450122      * |         NA
    ##   chr22:50968329-50968358    chr22 50968329-50968358      * |         NA
    ##                           MCF7_Cunha_shuffled      coefs
    ##                                     <numeric>  <numeric>
    ##      chr1:1168022-1168051                  NA  0.6285003
    ##    chr1:19746550-19746579                  NA  0.0138482
    ##    chr1:23858021-23858050                  NA -0.1663978
    ##    chr1:32084950-32084979                  NA  0.0989124
    ##    chr1:32687553-32687582                  NA  0.0358242
    ##                       ...                 ...        ...
    ##   chr22:42322132-42322161                  NA  0.7000011
    ##   chr22:43506007-43506036                  NA  0.1270524
    ##   chr22:46449447-46449476                  NA -0.1662689
    ##   chr22:46450093-46450122                  NA -0.0912389
    ##   chr22:50968329-50968358                  NA  0.1373155
    ##   -------
    ##   seqinfo: 22 sequences from hg19 genome
    ##
    ## $age
    ##          MCF7_Cunha MCF7_Cunha_shuffled
    ##            33.18896            34.88742