annotatr: Making sense of genomic regions
2021-07-13
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:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("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:
- 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.
- AnnotationHub annotations include any GRanges resource within the Bioconductor AnnotationHub web resource.
- 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.
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.
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 tx_id
## <Rle> <IRanges> <Rle> | <character> <logical>
## [1] chr1 713208-713477 * | H3K4me3_Gm12878:1 <NA>
## [2] chr1 713874-714056 * | H3K4me3_Gm12878:2 <NA>
## [3] chr1 714474-714750 * | H3K4me3_Gm12878:3 <NA>
## [4] chr1 715069-715388 * | H3K4me3_Gm12878:4 <NA>
## [5] chr1 724097-724311 * | H3K4me3_Gm12878:5 <NA>
## ... ... ... ... . ... ...
## [57472] chrX 154996923-154997189 * | H3K4me3_Gm12878:57472 <NA>
## [57473] chrX 154997422-154997785 * | H3K4me3_Gm12878:57473 <NA>
## [57474] chrX 155100454-155128015 * | H3K4me3_Gm12878:57474 <NA>
## [57475] chrX 155148379-155155444 * | H3K4me3_Gm12878:57475 <NA>
## [57476] chrX 155227027-155228269 * | H3K4me3_Gm12878:57476 <NA>
## gene_id symbol type
## <logical> <logical> <character>
## [1] <NA> <NA> hg19_H3K4me3_Gm12878
## [2] <NA> <NA> hg19_H3K4me3_Gm12878
## [3] <NA> <NA> hg19_H3K4me3_Gm12878
## [4] <NA> <NA> hg19_H3K4me3_Gm12878
## [5] <NA> <NA> hg19_H3K4me3_Gm12878
## ... ... ... ...
## [57472] <NA> <NA> hg19_H3K4me3_Gm12878
## [57473] <NA> <NA> hg19_H3K4me3_Gm12878
## [57474] <NA> <NA> hg19_H3K4me3_Gm12878
## [57475] <NA> <NA> hg19_H3K4me3_Gm12878
## [57476] <NA> <NA> hg19_H3K4me3_Gm12878
## -------
## seqinfo: 298 sequences (2 circular) from hg19 genome3.7 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 gene_id
## <Rle> <IRanges> <Rle> | <character> <logical> <logical>
## [1] chr1 860063-860382 * | ezh2:1 <NA> <NA>
## [2] chr1 934911-935230 * | ezh2:2 <NA> <NA>
## [3] chr1 3573321-3573640 * | ezh2:3 <NA> <NA>
## [4] chr1 6301401-6301720 * | ezh2:4 <NA> <NA>
## [5] chr1 6301996-6302315 * | ezh2:5 <NA> <NA>
## ... ... ... ... . ... ... ...
## [2468] chrX 99880950-99881269 * | ezh2:2468 <NA> <NA>
## [2469] chrX 108514101-108514420 * | ezh2:2469 <NA> <NA>
## [2470] chrX 111981673-111981992 * | ezh2:2470 <NA> <NA>
## [2471] chrX 118109216-118109535 * | ezh2:2471 <NA> <NA>
## [2472] chrX 136114771-136115090 * | ezh2:2472 <NA> <NA>
## symbol type
## <logical> <character>
## [1] <NA> hg19_custom_ezh2
## [2] <NA> hg19_custom_ezh2
## [3] <NA> hg19_custom_ezh2
## [4] <NA> hg19_custom_ezh2
## [5] <NA> hg19_custom_ezh2
## ... ... ...
## [2468] <NA> hg19_custom_ezh2
## [2469] <NA> hg19_custom_ezh2
## [2470] <NA> hg19_custom_ezh2
## [2471] <NA> hg19_custom_ezh2
## [2472] <NA> hg19_custom_ezh2
## -------
## seqinfo: 298 sequences (2 circular) from hg19 genomeTo see what is in the annotatr_cache environment, do the following:
print(annotatr_cache$list_env())## [1] "hg19_H3K4me3_Gm12878" "hg19_custom_ezh2"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 diff_meth
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric>
## [1] chr9 10850-10948 * | none 0.5045502 -10.7329047
## [2] chr9 10950-11048 * | none 0.2227126 8.7195270
## [3] chr9 28950-29048 * | none 0.5530958 0.0700847
## [4] chr9 72850-72948 * | hyper 0.0116294 44.8753244
## [5] chr9 72950-73048 * | none 0.1752872 17.7606626
## ... ... ... ... . ... ... ...
## [1996] chr9 35605150-35605248 * | none 0.274255 -0.0539158
## [1997] chr9 35605250-35605348 * | none 0.918064 0.0329283
## [1998] chr9 35605350-35605448 * | none 0.614312 -0.0977500
## [1999] chr9 35605450-35605548 * | none 1.000000 0.0000000
## [2000] chr9 35605550-35605648 * | none 0.814567 0.0349967
## mu0 mu1
## <numeric> <numeric>
## [1] 79.981920 90.7148252
## [2] 86.704015 77.9844878
## [3] 0.124081 0.0539963
## [4] 72.455413 27.5800883
## [5] 28.440368 10.6797057
## ... ... ...
## [1996] 0.000000 0.0539158
## [1997] 0.328024 0.2950959
## [1998] 0.130184 0.2279345
## [1999] 0.000000 0.0000000
## [2000] 0.118272 0.0832756
## -------
## seqinfo: 298 sequences (2 circular) from hg19 genome4.2 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 diff_meth
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric>
## [1] chr9 10850-10948 * | none 0.504550 -10.73290
## [2] chr9 10850-10948 * | none 0.504550 -10.73290
## [3] chr9 10950-11048 * | none 0.222713 8.71953
## [4] chr9 10950-11048 * | none 0.222713 8.71953
## [5] chr9 10950-11048 * | none 0.222713 8.71953
## ... ... ... ... . ... ... ...
## [11370] chr9 35605550-35605648 * | none 0.814567 0.0349967
## [11371] chr9 35605550-35605648 * | none 0.814567 0.0349967
## [11372] chr9 35605550-35605648 * | none 0.814567 0.0349967
## [11373] chr9 35605550-35605648 * | none 0.814567 0.0349967
## [11374] chr9 35605550-35605648 * | none 0.814567 0.0349967
## mu0 mu1 annot
## <numeric> <numeric> <GRanges>
## [1] 79.9819 90.7148 chr9:6987-10986:+
## [2] 79.9819 90.7148 chr9:1-24849:*
## [3] 86.7040 77.9845 chr9:10987-11986:+
## [4] 86.7040 77.9845 chr9:6987-10986:+
## [5] 86.7040 77.9845 chr9:1-24849:*
## ... ... ... ...
## [11370] 0.118272 0.0832756 chr9:35605281-35605616:+
## [11371] 0.118272 0.0832756 chr9:35605281-35605835:+
## [11372] 0.118272 0.0832756 chr9:35605281-35605835:+
## [11373] 0.118272 0.0832756 chr9:35605281-35605835:+
## [11374] 0.118272 0.0832756 chr9:35603969-35605991:*
## -------
## seqinfo: 298 sequences (2 circular) from hg19 genomeThe 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 mu0
## 1 chr9 10850 10948 99 * none 0.5045502 -10.73290471 79.981920
## 2 chr9 10850 10948 99 * none 0.5045502 -10.73290471 79.981920
## 3 chr9 10950 11048 99 * none 0.2227126 8.71952705 86.704015
## 4 chr9 10950 11048 99 * none 0.2227126 8.71952705 86.704015
## 5 chr9 10950 11048 99 * none 0.2227126 8.71952705 86.704015
## 6 chr9 28950 29048 99 * none 0.5530958 0.07008468 0.124081
## mu1 annot.seqnames annot.start annot.end annot.width annot.strand
## 1 90.7148252 chr9 6987 10986 4000 +
## 2 90.7148252 chr9 1 24849 24849 *
## 3 77.9844878 chr9 10987 11986 1000 +
## 4 77.9844878 chr9 6987 10986 4000 +
## 5 77.9844878 chr9 1 24849 24849 *
## 6 0.0539963 chr9 28923 29077 155 *
## 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 strand
## [6] DM_status pval diff_meth mu0 mu1
## [11] annot.seqnames annot.start annot.end annot.width annot.strand
## [16] annot.id 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 [2]
## 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 [14]
## annot.type annot.id n mean sd
## <chr> <chr> <int> <dbl> <dbl>
## 1 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27530 1 0.0701 NA
## 2 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27531 8 1.28 3.78
## 3 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27532 2 13.4 5.11
## 4 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27534 10 0.526 0.975
## 5 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27535 8 0.407 0.923
## 6 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27543 2 -0.0530 0.0749
## 7 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27544 11 0.192 0.427
## 8 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27545 2 2.80 10.1
## 9 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27549 2 -0.811 1.75
## 10 hg19_H3K4me3_Gm12878 H3K4me3_Gm12878:27555 1 -1.50 NA
## # … with 3,587 more rows# 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 [14]
## 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 rows4.5 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)
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)
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)
Figure 3: Number of DM regions per pair of annotations
4.5.3 Plotting Numerical Data Over Regions
With numerical data, the plot_numerical() function plots a single variable (histogram) or two variables (scatterplot) at the region level, faceting over the categorical variable of choice. It is possible to include two categorical variables to facet over (see below). Note, when the plot is a histogram, the distribution over all regions is plotted within each facet.
dm_vs_regions_annot = plot_numerical(
annotated_regions = dm_annotated,
x = 'mu0',
facet = 'annot.type',
facet_order = c('hg19_genes_1to5kb','hg19_genes_promoters',
'hg19_genes_5UTRs','hg19_genes_3UTRs', 'hg19_custom_ezh2',
'hg19_genes_intergenic', 'hg19_cpg_islands'),
bin_width = 5,
plot_title = 'Group 0 Region Methylation In Genes',
x_label = 'Group 0')
print(dm_vs_regions_annot)
Figure 4: Methylation Rates in Group 0 for Regions Over DM Status
dm_vs_regions_annot2 = plot_numerical(
annotated_regions = dm_annotated,
x = 'diff_meth',
facet = c('annot.type','DM_status'),
facet_order = list(c('hg19_genes_promoters','hg19_genes_5UTRs','hg19_cpg_islands'), c('hyper','hypo','none')),
bin_width = 5,
plot_title = 'Group 0 Region Methylation In Genes',
x_label = 'Methylation Difference')
print(dm_vs_regions_annot2)
Figure 5: Methylation Differences for Regions Over DM Status and Annotation Type
dm_vs_regions_name = plot_numerical(
annotated_regions = dm_annotated,
x = 'mu0',
y = 'mu1',
facet = 'annot.type',
facet_order = c('hg19_genes_1to5kb','hg19_genes_promoters',
'hg19_genes_5UTRs','hg19_genes_3UTRs', 'hg19_custom_ezh2',
'hg19_genes_intergenic', 'hg19_cpg_islands', 'hg19_cpg_shores'),
plot_title = 'Region Methylation: Group 0 vs Group 1',
x_label = 'Group 0',
y_label = 'Group 1')
print(dm_vs_regions_name)
Figure 6: Methylation Rates in Regions Over DM Status in Group 0 vs Group 1
The plot_numerical_coannotations() shows the distribution of numerical data for regions occurring in any two annotations, as well as in one or the other annotation. For example, the following example shows CpG methylation rates for CpGs occurring in just promoters, just CpG islands, and both promoters and CpG islands.
dm_vs_num_co = plot_numerical_coannotations(
annotated_regions = dm_annotated,
x = 'mu0',
annot1 = 'hg19_cpg_islands',
annot2 = 'hg19_genes_promoters',
bin_width = 5,
plot_title = 'Group 0 Perc. Meth. in CpG Islands and Promoters',
x_label = 'Percent Methylation')
print(dm_vs_num_co)
Figure 7: Group 0 methylation Rates in Regions in promoters, CpG islands, and both
4.5.4 Plotting Categorical Data
# View the counts of CpG annotations in data classes
# The orders for the x-axis labels. This is also a subset
# of the labels (hyper, hypo, none).
x_order = c(
'hyper',
'hypo')
# The orders for the fill labels. Can also use this
# parameter to subset annotation types to fill.
fill_order = c(
'hg19_cpg_islands',
'hg19_cpg_shores',
'hg19_cpg_shelves',
'hg19_cpg_inter')
# Make a barplot of the data class where each bar
# is composed of the counts of CpG annotations.
dm_vs_cpg_cat1 = plot_categorical(
annotated_regions = dm_annotated, x='DM_status', fill='annot.type',
x_order = x_order, fill_order = fill_order, position='stack',
plot_title = 'DM Status by CpG Annotation Counts',
legend_title = 'Annotations',
x_label = 'DM status',
y_label = 'Count')
print(dm_vs_cpg_cat1)
Figure 8: Differential methylation classification with counts of CpG annotations
# Use the same order vectors as the previous code block,
# but use proportional fill instead of counts.
# Make a barplot of the data class where each bar
# is composed of the *proportion* of CpG annotations.
dm_vs_cpg_cat2 = plot_categorical(
annotated_regions = dm_annotated, x='DM_status', fill='annot.type',
x_order = x_order, fill_order = fill_order, position='fill',
plot_title = 'DM Status by CpG Annotation Proportions',
legend_title = 'Annotations',
x_label = 'DM status',
y_label = 'Proportion')
print(dm_vs_cpg_cat2)
Figure 9: Differential methylation classification with proportion of CpG annotations
As with plot_annotation() one may add annotations for random regions to the annotated_random parameter of plot_categorical(). The result is a Random Regions bar representing the distribution of random regions for the categorical variable used for fill. NOTE: Random regions can only be added when fill = 'annot.type'.
# Add in the randomized annotations for "Random Regions" bar
# Make a barplot of the data class where each bar
# is composed of the *proportion* of CpG annotations, and
# includes "All" regions tested for DM and "Random Regions"
# regions consisting of randomized regions.
dm_vs_cpg_cat_random = plot_categorical(
annotated_regions = dm_annotated, annotated_random = dm_random_annotated,
x='DM_status', fill='annot.type',
x_order = x_order, fill_order = fill_order, position='fill',
plot_title = 'DM Status by CpG Annotation Proportions',
legend_title = 'Annotations',
x_label = 'DM status',
y_label = 'Proportion')
print(dm_vs_cpg_cat_random)
Figure 10: Differential methylation classification with proportion of CpG annotations and random regions
# View the proportions of data classes in knownGene annotations
# The orders for the x-axis labels.
x_order = c(
'hg19_custom_ezh2',
'hg19_genes_1to5kb',
'hg19_genes_promoters',
'hg19_genes_5UTRs',
'hg19_genes_exons',
'hg19_genes_introns',
'hg19_genes_3UTRs',
'hg19_genes_intergenic')
# The orders for the fill labels.
fill_order = c(
'hyper',
'hypo',
'none')
dm_vs_kg_cat = plot_categorical(
annotated_regions = dm_annotated, x='annot.type', fill='DM_status',
x_order = x_order, fill_order = fill_order, position='fill',
legend_title = 'DM Status',
x_label = 'knownGene Annotations',
y_label = 'Proportion')
print(dm_vs_kg_cat)
Figure 11: Basic gene annotations with proportions of DM classification