Contents

1 Introduction

Genomic regions resulting from next-generation sequencing experiments and bioinformatics pipelines are more meaningful when annotated to genomic features. A SNP occurring in an exon, or an enhancer, is likely of greater interest than one occurring in an inter-genic region. It may be of interest to find that a particular transcription factor overwhelmingly binds in promoters, while another binds mostly in 3’UTRs. Hyper-methylation at promoters containing a CpG island may indicate different regulatory regimes in one condition compared to another.

annotatr provides genomic annotations and a set of functions to read, intersect, summarize, and visualize genomic regions in the context of genomic annotations.

2 Installation

The release version of annotatr is available via Bioconductor, and can be installed as follows:

source("http://bioconductor.org/biocLite.R")
biocLite("annotatr")

The development version of annotatr can be obtained via the GitHub repository or Bioconductor. It is easiest to install development versions with the devtools package as follows:

devtools::install_github('rcavalcante/annotatr')

Changelogs for development releases will be detailed on GitHub releases.

3 Annotations

There are three types of annotations available to annotatr:

  1. Built-in annotations including CpG annotations, genic annotations, enhancers, GENCODE lncRNAs, and chromatin states from chromHMM. Base data for each of these annotations is retrieved and processed in some way. See each below for details on data source and processing.
  2. AnnotationHub annotations include any GRanges resource within the Bioconductor AnnotationHub web resource.
  3. Custom annotations provided by the user.

3.1 CpG Annotations

The CpG islands are the basis for all CpG annotations, and are given by the AnnotationHub package for the given organism. CpG shores are defined as 2Kb upstream/downstream from the ends of the CpG islands, less the CpG islands. CpG shelves are defined as another 2Kb upstream/downstream of the farthest upstream/downstream limits of the CpG shores, less the CpG islands and CpG shores. The remaining genomic regions make up the inter-CGI annotation.

CpG annotations are available for hg19, hg38, mm9, mm10, rn4, rn5, rn6.

Schematic of CpG annotations.

Schematic of CpG annotations.

3.2 Genic Annotations

The genic annotations are determined by functions from GenomicFeatures and data from the TxDb.* and org.*.eg.db packages. Genic annotations include 1-5Kb upstream of the TSS, the promoter (< 1Kb upstream of the TSS), 5’UTR, first exons, exons, introns, CDS, 3’UTR, and intergenic regions (the intergenic regions exclude the previous list of annotations). The schematic below illustrates the relationship between the different annotations as extracted from the TxDb.* packages via GenomicFeatures functions.

Schematic of knownGene annotations.

Schematic of knownGene annotations.

Also included in genic annotations are intronexon and exonintron boundaries. These annotations are 200bp up/down stream of any boundary between an exon and intron. Important to note, is that the boundaries are with respect to the strand of the gene.

Non-intergenic gene annotations include Entrez ID and gene symbol information where it exists. The org.*.eg.db packages for the appropriate organisms are used to provide gene IDs and gene symbols.

The genic annotations have populated tx_id, gene_id, and symbol columns. Respectively they are, the knownGene transcript name, Entrez Gene ID, and gene symbol.

Genic annotations are available for all hg19, hg38, mm9, mm10, rn4, rn5, rn6, dm3, and dm6.

3.3 FANTOM5 Permissive Enhancers

FANTOM5 permissive enhancers were determined from bi-directional CAGE transcription as in Andersson et al. (2014), and are downloaded and processed for hg19 and mm9 from the FANTOM5 resource. Using the rtracklayer::liftOver() function, enhancers from hg19 are lifted to hg38, and mm9 to mm10.

3.4 GENCODE lncRNA transcripts

The long non-coding RNA (lncRNA) annotations are from GENCODE for hg19, hg38, and mm10. The lncRNA transcripts are used, and we eventually plan to include the lncRNA introns/exons at a later date. The lncRNA annotations have populated tx_id, gene_id, and symbol columns. Respectively they are, the Ensembl transcript name, Entrez Gene ID, and gene symbol. As per the transcript_type field in the GENCODE anntotations, the biotypes are given in the id column.

3.5 Chromatin states from ChromHMM

Chromatin states determined by chromHMM (Ernst and Kellis (2012)) in hg19 are available for nine cell lines (Gm12878, H1hesc, Hepg2, Hmec, Hsmm, Huvec, K562, Nhek, and Nhlf) via the UCSC Genome Browser tracks. Annotations for all states can be built using a shortcut like hg19_Gm12878-chromatin, or specific chromatin states can be accessed via codes like hg19_chromatin_Gm12878-StrongEnhancer or hg19_chromatin_Gm12878-Repressed.

3.6 AnnotationHub Annotations

The AnnotationHub Bioconductor package is a client for the AnnotationHub web resource. From the package description:

The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access.

Using the build_ah_annots() function, users can turn any resource of class GRanges into an annotation for use in annotatr. As an example, we create annotations for H3K4me3 ChIP-seq peaks in Gm12878 and H1-hesc cells.

# Create a named vector for the AnnotationHub accession codes with desired names
h3k4me3_codes = c('Gm12878' = 'AH23256')
# Fetch ah_codes from AnnotationHub and create annotations annotatr understands
build_ah_annots(genome = 'hg19', ah_codes = h3k4me3_codes, annotation_class = 'H3K4me3')
# The annotations as they appear in annotatr_cache
ah_names = c('hg19_H3K4me3_Gm12878')

print(annotatr_cache$get('hg19_H3K4me3_Gm12878'))
## GRanges object with 57476 ranges and 5 metadata columns:
##           seqnames                 ranges strand |                    id
##              <Rle>              <IRanges>  <Rle> |           <character>
##       [1]     chr1       [713208, 713477]      * |     H3K4me3_Gm12878:1
##       [2]     chr1       [713874, 714056]      * |     H3K4me3_Gm12878:2
##       [3]     chr1       [714474, 714750]      * |     H3K4me3_Gm12878:3
##       [4]     chr1       [715069, 715388]      * |     H3K4me3_Gm12878:4
##       [5]     chr1       [724097, 724311]      * |     H3K4me3_Gm12878:5
##       ...      ...                    ...    ... .                   ...
##   [57472]     chrX [154996923, 154997189]      * | H3K4me3_Gm12878:57472
##   [57473]     chrX [154997422, 154997785]      * | H3K4me3_Gm12878:57473
##   [57474]     chrX [155100454, 155128015]      * | H3K4me3_Gm12878:57474
##   [57475]     chrX [155148379, 155155444]      * | H3K4me3_Gm12878:57475
##   [57476]     chrX [155227027, 155228269]      * | H3K4me3_Gm12878:57476
##               tx_id   gene_id    symbol                 type
##           <logical> <logical> <logical>          <character>
##       [1]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##       [2]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##       [3]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##       [4]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##       [5]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##       ...       ...       ...       ...                  ...
##   [57472]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##   [57473]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##   [57474]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##   [57475]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##   [57476]      <NA>      <NA>      <NA> hg19_H3K4me3_Gm12878
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

3.7 Custom Annotations

Users may load their own annotations from BED files using the read_annotations() function, which uses the rtracklayer::import() function. The output is a GRanges with mcols() for id, tx_id, gene_id, symbol, and type. If a user wants to include tx_id, gene_id, and/or symbol in their custom annotations they can be included as extra columns on a BED6 input file.

## Use ENCODE ChIP-seq peaks for EZH2 in GM12878
## These files contain chr, start, and end columns
ezh2_file = system.file('extdata', 'Gm12878_Ezh2_peak_annotations.txt.gz', package = 'annotatr')

## Custom annotation objects are given names of the form genome_custom_name
read_annotations(con = ezh2_file, genome = 'hg19', name = 'ezh2', format = 'bed')

print(annotatr_cache$get('hg19_custom_ezh2'))
## GRanges object with 2472 ranges and 5 metadata columns:
##          seqnames                 ranges strand |          id     tx_id
##             <Rle>              <IRanges>  <Rle> | <character> <logical>
##      [1]     chr1     [ 860063,  860382]      * |      ezh2:1      <NA>
##      [2]     chr1     [ 934911,  935230]      * |      ezh2:2      <NA>
##      [3]     chr1     [3573321, 3573640]      * |      ezh2:3      <NA>
##      [4]     chr1     [6301401, 6301720]      * |      ezh2:4      <NA>
##      [5]     chr1     [6301996, 6302315]      * |      ezh2:5      <NA>
##      ...      ...                    ...    ... .         ...       ...
##   [2468]     chrX [ 99880950,  99881269]      * |   ezh2:2468      <NA>
##   [2469]     chrX [108514101, 108514420]      * |   ezh2:2469      <NA>
##   [2470]     chrX [111981673, 111981992]      * |   ezh2:2470      <NA>
##   [2471]     chrX [118109216, 118109535]      * |   ezh2:2471      <NA>
##   [2472]     chrX [136114771, 136115090]      * |   ezh2:2472      <NA>
##            gene_id    symbol             type
##          <logical> <logical>      <character>
##      [1]      <NA>      <NA> hg19_custom_ezh2
##      [2]      <NA>      <NA> hg19_custom_ezh2
##      [3]      <NA>      <NA> hg19_custom_ezh2
##      [4]      <NA>      <NA> hg19_custom_ezh2
##      [5]      <NA>      <NA> hg19_custom_ezh2
##      ...       ...       ...              ...
##   [2468]      <NA>      <NA> hg19_custom_ezh2
##   [2469]      <NA>      <NA> hg19_custom_ezh2
##   [2470]      <NA>      <NA> hg19_custom_ezh2
##   [2471]      <NA>      <NA> hg19_custom_ezh2
##   [2472]      <NA>      <NA> hg19_custom_ezh2
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

4 Usage

The following example is based on the results of testing for differential methylation of genomic regions between two conditions using methylSig. The file (inst/extdata/IDH2mut_v_NBM_multi_data_chr9.txt.gz) contains chromosome locations, as well as categorical and numerical data columns, and provides a good example of the flexibility of annotatr.

4.1 Reading Genomic Regions

read_regions() uses the rtracklayer::import() function to read in BED files and convert them to GRanges objects. The name and score columns in a normal BED file can be used for categorical and numeric data, respectively. Additionally, an arbitrary number of categorical and numeric data columns can be appended to a BED6 file. The extraCols parameter is used for this purpose, and the rename_name and rename_score columns allow users to give more descriptive names to these columns.

# This file in inst/extdata represents regions tested for differential
# methylation between two conditions. Additionally, there are columns
# reporting the p-value on the test for differential meth., the
# meth. difference between the two groups, and the group meth. rates.
dm_file = system.file('extdata', 'IDH2mut_v_NBM_multi_data_chr9.txt.gz', package = 'annotatr')
extraCols = c(diff_meth = 'numeric', mu0 = 'numeric', mu1 = 'numeric')
dm_regions = read_regions(con = dm_file, genome = 'hg19', extraCols = extraCols, format = 'bed',
    rename_name = 'DM_status', rename_score = 'pval')
# Use less regions to speed things up
dm_regions = dm_regions[1:2000]
print(dm_regions)
## GRanges object with 2000 ranges and 5 metadata columns:
##          seqnames               ranges strand |   DM_status       pval
##             <Rle>            <IRanges>  <Rle> | <character>  <numeric>
##      [1]     chr9       [10850, 10948]      * |        none 0.50455021
##      [2]     chr9       [10950, 11048]      * |        none 0.22271260
##      [3]     chr9       [28950, 29048]      * |        none 0.55309581
##      [4]     chr9       [72850, 72948]      * |       hyper 0.01162935
##      [5]     chr9       [72950, 73048]      * |        none 0.17528718
##      ...      ...                  ...    ... .         ...        ...
##   [1996]     chr9 [35605150, 35605248]      * |        none  0.2742551
##   [1997]     chr9 [35605250, 35605348]      * |        none  0.9180642
##   [1998]     chr9 [35605350, 35605448]      * |        none  0.6143125
##   [1999]     chr9 [35605450, 35605548]      * |        none  1.0000000
##   [2000]     chr9 [35605550, 35605648]      * |        none  0.8145674
##             diff_meth       mu0        mu1
##             <numeric> <numeric>  <numeric>
##      [1] -10.73290471 79.981920 90.7148252
##      [2]   8.71952705 86.704015 77.9844878
##      [3]   0.07008468  0.124081  0.0539963
##      [4]  44.87532440 72.455413 27.5800883
##      [5]  17.76066258 28.440368 10.6797057
##      ...          ...       ...        ...
##   [1996]  -0.05391585 0.0000000 0.05391585
##   [1997]   0.03292831 0.3280242 0.29509592
##   [1998]  -0.09774999 0.1301845 0.22793448
##   [1999]   0.00000000 0.0000000 0.00000000
##   [2000]   0.03499665 0.1182722 0.08327557
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

4.2 Annotating Regions

Users may select annotations a la carte via the accessors listed with builtin_annotations(), shortcuts, or use custom annotations as described above. The hg19_cpgs shortcut annotates regions to CpG islands, CpG shores, CpG shelves, and inter-CGI. The hg19_basicgenes shortcut annotates regions to 1-5Kb, promoters, 5’UTRs, exons, introns, and 3’UTRs. Shortcuts for other builtin_genomes() are accessed in a similar way.

annotate_regions() requires a GRanges object (either the result of read_regions() or an existing object), a GRanges object of the annotations, and a logical value indicating whether to ignore.strand when calling GenomicRanges::findOverlaps(). The positive integer minoverlap is also passed to GenomicRanges::findOverlaps() and specifies the minimum overlap required for a region to be assigned to an annotation.

Before annotating regions, they must be built with build_annotations() which requires a character vector of desired annotation codes.

# Select annotations for intersection with regions
# Note inclusion of custom annotation, and use of shortcuts
annots = c('hg19_cpgs', 'hg19_basicgenes', 'hg19_genes_intergenic',
    'hg19_genes_intronexonboundaries',
    'hg19_custom_ezh2', 'hg19_H3K4me3_Gm12878')

# Build the annotations (a single GRanges object)
annotations = build_annotations(genome = 'hg19', annotations = annots)

# Intersect the regions we read in with the annotations
dm_annotated = annotate_regions(
    regions = dm_regions,
    annotations = annotations,
    ignore.strand = TRUE,
    quiet = FALSE)
# A GRanges object is returned
print(dm_annotated)
## GRanges object with 11374 ranges and 6 metadata columns:
##           seqnames               ranges strand |   DM_status      pval
##              <Rle>            <IRanges>  <Rle> | <character> <numeric>
##       [1]     chr9       [10850, 10948]      * |        none 0.5045502
##       [2]     chr9       [10850, 10948]      * |        none 0.5045502
##       [3]     chr9       [10950, 11048]      * |        none 0.2227126
##       [4]     chr9       [10950, 11048]      * |        none 0.2227126
##       [5]     chr9       [10950, 11048]      * |        none 0.2227126
##       ...      ...                  ...    ... .         ...       ...
##   [11370]     chr9 [35605550, 35605648]      * |        none 0.8145674
##   [11371]     chr9 [35605550, 35605648]      * |        none 0.8145674
##   [11372]     chr9 [35605550, 35605648]      * |        none 0.8145674
##   [11373]     chr9 [35605550, 35605648]      * |        none 0.8145674
##   [11374]     chr9 [35605550, 35605648]      * |        none 0.8145674
##            diff_meth       mu0        mu1                    annot
##            <numeric> <numeric>  <numeric>                <GRanges>
##       [1] -10.732905  79.98192   90.71483        chr9:6987-10986:+
##       [2] -10.732905  79.98192   90.71483           chr9:1-24849:*
##       [3]   8.719527  86.70401   77.98449       chr9:10987-11986:+
##       [4]   8.719527  86.70401   77.98449        chr9:6987-10986:+
##       [5]   8.719527  86.70401   77.98449           chr9:1-24849:*
##       ...        ...       ...        ...                      ...
##   [11370] 0.03499665 0.1182722 0.08327557 chr9:35605281-35605616:+
##   [11371] 0.03499665 0.1182722 0.08327557 chr9:35605281-35605835:+
##   [11372] 0.03499665 0.1182722 0.08327557 chr9:35605281-35605835:+
##   [11373] 0.03499665 0.1182722 0.08327557 chr9:35605281-35605835:+
##   [11374] 0.03499665 0.1182722 0.08327557 chr9:35603969-35605991:*
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

The annotate_regions() function returns a GRanges, but it may be more convenient to manipulate a coerced data.frame. For example,

# Coerce to a data.frame
df_dm_annotated = data.frame(dm_annotated)

# See the GRanges column of dm_annotaed expanded
print(head(df_dm_annotated))
##   seqnames start   end width strand DM_status      pval    diff_meth
## 1     chr9 10850 10948    99      *      none 0.5045502 -10.73290471
## 2     chr9 10850 10948    99      *      none 0.5045502 -10.73290471
## 3     chr9 10950 11048    99      *      none 0.2227126   8.71952705
## 4     chr9 10950 11048    99      *      none 0.2227126   8.71952705
## 5     chr9 10950 11048    99      *      none 0.2227126   8.71952705
## 6     chr9 28950 29048    99      *      none 0.5530958   0.07008468
##         mu0        mu1 annot.seqnames annot.start annot.end annot.width
## 1 79.981920 90.7148252           chr9        6987     10986        4000
## 2 79.981920 90.7148252           chr9           1     24849       24849
## 3 86.704015 77.9844878           chr9       10987     11986        1000
## 4 86.704015 77.9844878           chr9        6987     10986        4000
## 5 86.704015 77.9844878           chr9           1     24849       24849
## 6  0.124081  0.0539963           chr9       28923     29077         155
##   annot.strand              annot.id annot.tx_id annot.gene_id annot.symbol
## 1            +          1to5kb:34327  uc011llp.1     100287596      DDX11L5
## 2            *            inter:8599        <NA>          <NA>         <NA>
## 3            +        promoter:34327  uc011llp.1     100287596      DDX11L5
## 4            +          1to5kb:34327  uc011llp.1     100287596      DDX11L5
## 5            *            inter:8599        <NA>          <NA>         <NA>
## 6            * H3K4me3_Gm12878:27530        <NA>          <NA>         <NA>
##             annot.type
## 1    hg19_genes_1to5kb
## 2       hg19_cpg_inter
## 3 hg19_genes_promoters
## 4    hg19_genes_1to5kb
## 5       hg19_cpg_inter
## 6 hg19_H3K4me3_Gm12878
# Subset based on a gene symbol, in this case NOTCH1
notch1_subset = subset(df_dm_annotated, annot.symbol == 'NOTCH1')
print(head(notch1_subset))
##  [1] seqnames       start          end            width         
##  [5] strand         DM_status      pval           diff_meth     
##  [9] mu0            mu1            annot.seqnames annot.start   
## [13] annot.end      annot.width    annot.strand   annot.id      
## [17] annot.tx_id    annot.gene_id  annot.symbol   annot.type    
## <0 rows> (or 0-length row.names)

4.3 Randomizing Regions

Given a set of annotated regions, it is important to know how the annotations compare to those of a randomized set of regions. The randomize_regions() function is a wrapper of regioneR::randomizeRegions() from the regioneR package that creates a set of random regions given a GRanges object. After creating the random set, they must be annotated with annotate_regions() for later use. Only builtin_genomes() can be used in our wrapper function. Downstream functions that support using random region annotations are summarize_annotations(), plot_annotation(), and plot_categorical().

It is important to note that if the regions to be randomized have a particular property, for example they are CpGs, the randomize_regions() wrapper will not preserve that property! Instead, we recommend using regioneR::resampleRegions() with universe being the superset of the data regions you want to sample from.

# Randomize the input regions
dm_random_regions = randomize_regions(
    regions = dm_regions,
    allow.overlaps = TRUE,
    per.chromosome = TRUE)

# Annotate the random regions using the same annotations as above
# These will be used in later functions
dm_random_annotated = annotate_regions(
    regions = dm_random_regions,
    annotations = annotations,
    ignore.strand = TRUE,
    quiet = TRUE)

4.4 Summarizing Over Annotations

When there is no categorical or numerical information associated with the regions, summarize_annotations() is the only possible summarization function to use. It gives the counts of regions in each annotation type (see example below). If there is categorical and/or numerical information, then summarize_numerical() and/or summarize_categorical() may be used. Using random region annotations is only available for summarize_annotations().

# Find the number of regions per annotation type
dm_annsum = summarize_annotations(
    annotated_regions = dm_annotated,
    quiet = TRUE)
print(dm_annsum)
## # A tibble: 14 x 2
##                         annot.type     n
##                              <chr> <int>
##  1            hg19_H3K4me3_Gm12878   747
##  2                  hg19_cpg_inter   905
##  3                hg19_cpg_islands   848
##  4                hg19_cpg_shelves    46
##  5                 hg19_cpg_shores   341
##  6                hg19_custom_ezh2     7
##  7               hg19_genes_1to5kb   257
##  8                hg19_genes_3UTRs    28
##  9                hg19_genes_5UTRs   271
## 10                hg19_genes_exons   483
## 11           hg19_genes_intergenic   557
## 12 hg19_genes_intronexonboundaries   319
## 13              hg19_genes_introns   951
## 14            hg19_genes_promoters   393
# Find the number of regions per annotation type
# and the number of random regions per annotation type
dm_annsum_rnd = summarize_annotations(
    annotated_regions = dm_annotated,
    annotated_random = dm_random_annotated,
    quiet = TRUE)
print(dm_annsum_rnd)
## # A tibble: 28 x 3
## # Groups:   data_type [?]
##    data_type           annot.type     n
##        <chr>                <chr> <int>
##  1      Data hg19_H3K4me3_Gm12878   747
##  2      Data       hg19_cpg_inter   905
##  3      Data     hg19_cpg_islands   848
##  4      Data     hg19_cpg_shelves    46
##  5      Data      hg19_cpg_shores   341
##  6      Data     hg19_custom_ezh2     7
##  7      Data    hg19_genes_1to5kb   257
##  8      Data     hg19_genes_3UTRs    28
##  9      Data     hg19_genes_5UTRs   271
## 10      Data     hg19_genes_exons   483
## # ... with 18 more rows
# Take the mean of the diff_meth column across all regions
# occurring in an annotation.
dm_numsum = summarize_numerical(
    annotated_regions = dm_annotated,
    by = c('annot.type', 'annot.id'),
    over = c('diff_meth'),
    quiet = TRUE)
print(dm_numsum)
## # A tibble: 3,597 x 5
## # Groups:   annot.type [?]
##              annot.type              annot.id diff_meth_n diff_meth_mean
##                   <chr>                 <chr>       <int>          <dbl>
##  1 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27530           1     0.07008468
##  2 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27531           8     1.27910330
##  3 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27532           2    13.37461167
##  4 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27534          10     0.52624106
##  5 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27535           8     0.40656753
##  6 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27543           2    -0.05295042
##  7 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27544          11     0.19161372
##  8 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27545           2     2.80488139
##  9 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27549           2    -0.81142071
## 10 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27555           1    -1.49726015
## # ... with 3,587 more rows, and 1 more variables: diff_meth_sd <dbl>
# Count the occurrences of classifications in the DM_status
# column across the annotation types.
dm_catsum = summarize_categorical(
    annotated_regions = dm_annotated,
    by = c('annot.type', 'DM_status'),
    quiet = TRUE)
print(dm_catsum)
## # A tibble: 39 x 3
## # Groups:   annot.type [?]
##              annot.type DM_status     n
##                   <chr>     <chr> <int>
##  1 hg19_H3K4me3_Gm12878     hyper    78
##  2 hg19_H3K4me3_Gm12878      hypo     8
##  3 hg19_H3K4me3_Gm12878      none   661
##  4       hg19_cpg_inter     hyper    32
##  5       hg19_cpg_inter      hypo    90
##  6       hg19_cpg_inter      none   783
##  7     hg19_cpg_islands     hyper   151
##  8     hg19_cpg_islands      hypo     4
##  9     hg19_cpg_islands      none   693
## 10     hg19_cpg_shelves     hyper     2
## # ... with 29 more rows

4.5 Plotting

The 5 plot functions described below are to be used on the object returned by annotate_regions(). The plot functions return an object of type ggplot that can be viewed (print), saved (ggsave), or modified with additional ggplot2 code.

4.5.1 Plotting Regions per Annotation

# View the number of regions per annotation. This function
# is useful when there is no classification or data
# associated with the regions.
annots_order = c(
    'hg19_custom_ezh2',
    'hg19_H3K4me3_Gm12878',
    'hg19_genes_1to5kb',
    'hg19_genes_promoters',
    'hg19_genes_5UTRs',
    'hg19_genes_exons',
    'hg19_genes_intronexonboundaries',
    'hg19_genes_introns',
    'hg19_genes_3UTRs',
    'hg19_genes_intergenic')
dm_vs_kg_annotations = plot_annotation(
    annotated_regions = dm_annotated,
    annotation_order = annots_order,
    plot_title = '# of Sites Tested for DM annotated on chr9',
    x_label = 'knownGene Annotations',
    y_label = 'Count')
print(dm_vs_kg_annotations)
Number of DM regions per annotation.

Figure 1: Number of DM regions per annotation

The plot_annotation() can also use the annotated random regions in the annotated_random argument to plot the number of random regions per annotation type next to the number of input data regions.

# View the number of regions per annotation and include the annotation
# of randomized regions
annots_order = c(
    'hg19_custom_ezh2',
    'hg19_H3K4me3_Gm12878',
    'hg19_genes_1to5kb',
    'hg19_genes_promoters',
    'hg19_genes_5UTRs',
    'hg19_genes_exons',
    'hg19_genes_intronexonboundaries',
    'hg19_genes_introns',
    'hg19_genes_3UTRs',
    'hg19_genes_intergenic')
dm_vs_kg_annotations_wrandom = plot_annotation(
    annotated_regions = dm_annotated,
    annotated_random = dm_random_annotated,
    annotation_order = annots_order,
    plot_title = 'Dist. of Sites Tested for DM (with rndm.)',
    x_label = 'Annotations',
    y_label = 'Count')
print(dm_vs_kg_annotations_wrandom)
Number of DM regions per annotation with randomized regions.

Figure 2: Number of DM regions per annotation with randomized regions

4.5.2 Plotting Regions Occurring in Pairs of Annotations

# View a heatmap of regions occurring in pairs of annotations
annots_order = c(
    'hg19_custom_ezh2',
    'hg19_H3K4me3_Gm12878',
    'hg19_genes_promoters',
    'hg19_genes_5UTRs',
    'hg19_genes_exons',
    'hg19_genes_introns',
    'hg19_genes_3UTRs',
    'hg19_genes_intergenic')
dm_vs_coannotations = plot_coannotations(
    annotated_regions = dm_annotated,
    annotation_order = annots_order,
    axes_label = 'Annotations',
    plot_title = 'Regions in Pairs of Annotations')
print(dm_vs_coannotations)