Getting started with the plyranges package

In plyranges: A fluent interface for manipulating GenomicRanges

Ranges revisited

In Bioconductor there are two classes, IRanges and GRanges, that are standard data structures for representing genomics data. Throughout this document I refer to either of these classes as Ranges if an operation can be performed on either class, otherwise I explicitly mention if a function is appropriate for an IRanges or GRanges.

Ranges objects can either represent sets of integers as IRanges (which have start, end and width attributes) or represent genomic intervals (which have additional attributes, sequence name, and strand) as GRanges. In addition, both types of Ranges can store information about their intervals as metadata columns (for example GC content over a genomic interval).

Ranges objects follow the tidy data principle: each row of a Ranges object corresponds to an interval, while each column will represent a variable about that interval, and generally each object will represent a single unit of observation (like gene annotations).

Consequently, Ranges objects provide a powerful representation for reasoning about genomic data. In this vignette, you will learn more about Ranges objects and how via grouping, restriction and summarisation you can perform common data tasks.

Constructing Ranges

To construct an IRanges we require that there are at least two columns that represent at either a starting coordinate, finishing coordinate or the width of the interval.

suppressPackageStartupMessages(library(plyranges))
set.seed(100)
df <- data.frame(start=c(2:-1, 13:15),
                 width=c(0:3, 2:0))

# produces IRanges
rng <- df %>% as_iranges()
rng

To construct a GRanges we require a column that represents that sequence name ( contig or chromosome id), and an optional column to represent the strandedness of an interval.

# seqname is required for GRanges, metadata is automatically kept
grng <- df %>%
  transform(seqnames = sample(c("chr1", "chr2"), 7, replace = TRUE),
         strand = sample(c("+", "-"), 7, replace = TRUE),
         gc = runif(7)) %>%
  as_granges()

grng

Arithmetic on Ranges

Sometimes you want to modify a genomic interval by altering the width of the interval while leaving the start, end or midpoint of the coordinates unaltered. This is achieved with the mutate verb along with anchor_* adverbs.

The act of anchoring fixes either the start, end, center coordinates of the Range object, as shown in the figure and code below and anchors are used in combination with either mutate or stretch.

knitr::include_graphics("anchors.png", dpi = 150)

rng <- as_iranges(data.frame(start=c(1, 2, 3), end=c(5, 2, 8)))
grng <- as_granges(data.frame(start=c(1, 2, 3), end=c(5, 2, 8),
                          seqnames = "seq1",
                          strand = c("+", "*", "-")))
mutate(rng, width = 10)
mutate(anchor_start(rng), width = 10)
mutate(anchor_end(rng), width = 10)
mutate(anchor_center(rng), width = 10)
mutate(anchor_3p(grng), width = 10) # leave negative strand fixed
mutate(anchor_5p(grng), width = 10) # leave positive strand fixed

Similarly, you can modify the width of an interval using the stretch verb. Without anchoring, this function will extend the interval in either direction by an integer amount. With anchoring, either the start, end or midpoint are preserved.

rng2 <- stretch(anchor_center(rng), 10)
rng2
stretch(anchor_end(rng2), 10)
stretch(anchor_start(rng2), 10)
stretch(anchor_3p(grng), 10)
stretch(anchor_5p(grng), 10)

Ranges can be shifted left or right. If strand information is available we can also shift upstream or downstream.

shift_left(rng, 10)
shift_right(rng, 10)
shift_upstream(grng, 10)
shift_downstream(grng, 10)

Grouping Ranges

plyranges introduces a new class of Ranges called RangesGrouped, this is a similar idea to the grouped data.frame\tibble in dplyr.

Grouping can act on either the core components or the metadata columns of a Ranges object.

It is most effective when combined with other verbs such as mutate(), summarise(), filter(), reduce_ranges() or disjoin_ranges().

grng <- data.frame(seqnames = sample(c("chr1", "chr2"), 7, replace = TRUE),
         strand = sample(c("+", "-"), 7, replace = TRUE),
         gc = runif(7),
         start = 1:7,
         width = 10) %>%
  as_granges()

grng_by_strand <- grng %>%
  group_by(strand)

grng_by_strand

Restricting Ranges

The verb filter can be used to restrict rows in the Ranges. Note that grouping will cause the filter to act within each group of the data.

grng %>% filter(gc < 0.3)
# filtering by group
grng_by_strand %>% filter(gc == max(gc))

We also provide the convenience methods filter_by_overlaps and filter_by_non_overlaps for restricting by any overlapping Ranges.

ir0 <- data.frame(start = c(5,10, 15,20), width = 5) %>%
  as_iranges()
ir1 <- data.frame(start = 2:6, width = 3:7) %>%
  as_iranges()
ir0
ir1
ir0 %>% filter_by_overlaps(ir1)
ir0 %>% filter_by_non_overlaps(ir1)

Summarising Ranges

The summarise function will return a DataFrame because the information required to return a Ranges object is lost. It is often most useful to use summarise() in combination with the group_by() family of functions.

ir1 <- ir1 %>%
  mutate(gc = runif(length(.)))

ir0 %>%
  group_by_overlaps(ir1) %>%
  summarise(gc = mean(gc))

Joins, or another way at looking at overlaps between Ranges

A join acts on two GRanges objects, a query and a subject.

query <- data.frame(seqnames = "chr1",
               strand = c("+", "-"),
               start = c(1, 9),
               end =  c(7, 10),
               key.a = letters[1:2]) %>%
  as_granges()

subject <- data.frame(seqnames = "chr1",
               strand = c("-", "+"),
               start = c(2, 6),
               end = c(4, 8),
               key.b = LETTERS[1:2]) %>%
  as_granges()

library(ggplot2)
query_df <- as.data.frame(query)[, -6]
query_df$key <- "Query"
subject_df <- as.data.frame(subject)[, -6]
subject_df$key <- "Subject"
melted_ranges <- rbind(query_df, subject_df)
ggplot(melted_ranges, aes(xmin = start, xmax = end, ymin = 1, ymax = 3)) +
  geom_rect() +
  facet_grid(key ~ .) +
  scale_x_continuous(breaks = seq(1, 10, by = 1)) +
  xlab("Position") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank())

The join operator is relational in the sense that metadata from the query and subject ranges is retained in the joined range. All join operators in the plyranges DSL generate a set of hits based on overlap or proximity of ranges and use those hits to merge the two datasets in different ways. There are four supported matching algorithms: overlap, nearest, precede, and follow. We can further restrict the matching by whether the query is completely within the subject, and adding the directed suffix ensures that matching ranges have the same direction (strand).

knitr::include_graphics("olaps.png")

The first function, join_overlap_intersect() will return a Ranges object where the start, end, and width coordinates correspond to the amount of any overlap between the left and right input Ranges. It also returns any metadatain the subject range if the subject overlaps the query.

intersect_rng <- join_overlap_intersect(query, subject)
intersect_rng

intersect_df <- as.data.frame(intersect_rng)[, -c(6,7)]
intersect_df$key <- "Intersect Join"
melted_ranges <- rbind(query_df, subject_df, intersect_df)
melted_ranges$key <- factor(melted_ranges$key,
                              levels = c("Query", "Subject", "Intersect Join"))
ggplot(melted_ranges, aes(xmin = start, xmax = end, ymin = 1, ymax = 3)) +
  geom_rect() +
  facet_grid(key ~ .) +
  scale_x_continuous(breaks = seq(1, 10, by = 1)) +
  xlab("Position") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank())

The join_overlap_inner() function will return the Ranges in the query that overlap any Ranges in the subject. Like the join_overlap_intersect() function metadata of the subject Range is returned if it overlaps the query.

inner_rng <- join_overlap_inner(query, subject)
inner_rng

inner_df <- as.data.frame(inner_rng)[, -c(6,7)]
inner_df$ymin <- c(1,4)
inner_df$ymax <- c(3,6)
inner_df$key <- "Inner Join"
melted_ranges <- rbind(query_df, subject_df)
melted_ranges$ymin <- 1
melted_ranges$ymax <- 3
melted_ranges <- rbind(melted_ranges, inner_df)
melted_ranges$key <- factor(melted_ranges$key,
                              levels = c("Query", "Subject", "Inner Join"))

ggplot(melted_ranges, aes(xmin = start, xmax = end, ymin = ymin, ymax = ymax)) +
  geom_rect() +
  facet_grid(key ~ ., scales = "free_y") +
  scale_x_continuous(breaks = seq(1, 10, by = 1)) +
  xlab("Position") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank())

We also provide a convenience method called find_overlaps that computes the same result as join_overlap_inner().

find_overlaps(query, subject)

The join_overlap_left() method will perform an outer left join.

First any overlaps that are found will be returned similar to join_overlap_inner(). Then any non-overlapping ranges will be returned, with missing values on the metadata columns.

left_rng <- join_overlap_left(query, subject)
left_rng

left_df <- as.data.frame(left_rng)[, -c(6,7)]
left_df$ymin <- c(1,4, 1)
left_df$ymax <- c(3,6, 3)
left_df$key <- "Left Join"
melted_ranges <- rbind(query_df, subject_df)
melted_ranges$ymin <- 1
melted_ranges$ymax <- 3
melted_ranges <- rbind(melted_ranges, left_df)
melted_ranges$key <- factor(melted_ranges$key,
                              levels = c("Query", "Subject", "Left Join"))

ggplot(melted_ranges,
       aes(xmin = start, xmax = end, ymin = ymin, ymax = ymax)) +
  geom_rect() +
  facet_grid(key ~ ., scales = "free_y") +
  scale_x_continuous(breaks = seq(1, 10, by = 1)) +
  xlab("Position") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank())

Compared with filter_by_overlaps() above, the overlap left join expands the Ranges to give information about each interval on the query Ranges that overlap those on the subject Ranges as well as the intervals on the left that do not overlap any range on the right.

Finding your neighbours

We also provide methods for finding nearest, preceding or following Ranges. Conceptually this is identical to our approach for finding overlaps, except the semantics of the join are different.

knitr::include_graphics("neighbours.png")

join_nearest(ir0, ir1)
join_follow(ir0, ir1)
join_precede(ir0, ir1) # nothing precedes returns empty `Ranges`
join_precede(ir1, ir0)

Example: dealing with multi-mapping

This example is taken from the Bioconductor support site.

We have two Ranges objects. The first contains single nucleotide positions corresponding to an intensity measurement such as a ChiP-seq experiment, while the other contains coordinates for two genes of interest.

We want to identify which positions in the intensities Ranges overlap the genes, where each row corresponds to a position that overlaps a single gene.

First we create the two Ranges objects

intensities <- data.frame(seqnames = "VI",
                          start = c(3320:3321,3330:3331,3341:3342),
                          width = 1) %>%
  as_granges()

intensities

genes <- data.frame(seqnames = "VI",
                    start = c(3322, 3030),
                    end = c(3846, 3338),
                    gene_id=c("YFL064C", "YFL065C")) %>%
  as_granges()

genes

Now to find where the positions overlap each gene, we can perform an overlap join. This will automatically carry over the gene_id information as well as their coordinates (we can drop those by only selecting the gene_id).

olap <- join_overlap_inner(intensities, genes) %>%
  select(gene_id)
olap

Several positions match to both genes. We can count them using summarise and grouping by the start position:

olap %>%
  group_by(start) %>%
  summarise(n = n())

Grouping by overlaps

It's also possible to group by overlaps. Using this approach we can count the number of overlaps that are greater than 0.

grp_by_olap <- ir0 %>%
  group_by_overlaps(ir1)
grp_by_olap
grp_by_olap %>%
  mutate(n_overlaps = n())

Of course we can also add overlap counts via the count_overlaps() function.

ir0 %>%
  mutate(n_overlaps = count_overlaps(., ir1))

Data Import/Output

We provide convenience functions via rtracklayer and GenomicAlignments for reading/writing the following data formats to/from Ranges objects.

| plyranges functions | File Format | |-----------------------|-------------| | read_bam() | BAM | | read_bed()/write_bed() | BED | | read_bed_graph()/ write_bed_graph() | BEDGraph | | read_narrowpeaks()/write_narrowpeaks() | narrowPeaks | | read_gff() / write_gff() | GFF(1-3)/ GTF | | read_bigwig() / write_bigwig() | BigWig | | read_wig() /write_wig() | Wig |

Learning more

There are many other resources and workshops available to learn to use plyranges and related Bioconductor packages, especially for more realistic analyses than the ones covered here:

• The fluentGenomics workflow package is an end-to-end workflow package for integrating differential expression results with differential accessibility results.
• The Bioc 2018 Workshop book has worked examples of using plyranges to analyse publicly available genomics data.
• The extended vignette in the plyrangesWorkshops package has a detailed walk through of using plyranges for coverage analysis.
• The case study by Michael Love using plyranges with tximeta to follow up on interesting hits from a combined RNA-seq and ATAC-seq analysis.
• The journal article (preprint here) has details about the overall philosophy and design of plyranges.

Appendix

sessionInfo()

plyranges documentation built on Nov. 8, 2020, 7:36 p.m.