In const-ae/tidygenomics: Tidy Verbs for Dealing with Genomic Data Frames

The most dramatic impact on programming in R the last years was the development of the tidyverse by Hadley Wickham et al. which, combined with the ingenious %>% from magrittr, provides a uniform philosophy for handling data.

The genomics community has an alternative set of approaches, for which bioconductor and the GenomicRanges package provide the basis. The GenomicRanges and the underlying IRanges package provide a great set of methods for dealing with intervals as they typically encountered in genomics.

Unfortunately it is not always easy to combine those two worlds, many common operations in GenomicRanges focus solely on the ranges and loose the additional metadata columns. On the other hand the tidyverse does not provide a unified set of methods to do common set operations with intervals.

At least until recently, when the fuzzyjoin package was extended with the genome_join method for combining genomic data stored in a data.frame. It demonstrated that genomic data could appropriately be handled with the tidy-philosophy.

The tidygenomics package extends the limited set of methods provided by the fuzzyjoin package for dealing with genomic data. Its API is inspired by the very popular bedtools:

• genome_intersect
• genome_subtract
• genome_join_closest
• genome_cluster
• genome_complement
• genome_join Provided by the fuzzyjoin package

library(dplyr)
library(tidygenomics)

genome_intersect

Joins 2 data frames based on their genomic overlap. Unlike the genome_join function it updates the boundaries to reflect the overlap of the regions.

x1 <- data.frame(id = 1:4, 
                chromosome = c("chr1", "chr1", "chr2", "chr2"),
                start = c(100, 200, 300, 400),
                end = c(150, 250, 350, 450))

x2 <- data.frame(id = 1:4,
                 chromosome = c("chr1", "chr2", "chr2", "chr1"),
                 start = c(140, 210, 400, 300),
                 end = c(160, 240, 415, 320))

genome_intersect(x1, x2, by=c("chromosome", "start", "end"), mode="both")

genome_subtract

Subtracts one data frame from the other. This can be used to split the x data frame into smaller areas.

x1 <- data.frame(id = 1:4,
                chromosome = c("chr1", "chr1", "chr2", "chr1"),
                start = c(100, 200, 300, 400),
                end = c(150, 250, 350, 450))

x2 <- data.frame(id = 1:4,
                chromosome = c("chr1", "chr2", "chr1", "chr1"),
                start = c(120, 210, 300, 400),
                end = c(125, 240, 320, 415))

genome_subtract(x1, x2, by=c("chromosome", "start", "end"))

genome_join_closest

Joins 2 data frames based on their genomic location. If no exact overlap is found the next closest interval is used.

x1 <- tibble(id = 1:4, 
             chr = c("chr1", "chr1", "chr2", "chr3"),
             start = c(100, 200, 300, 400),
             end = c(150, 250, 350, 450))

x2 <- tibble(id = 1:4,
             chr = c("chr1", "chr1", "chr1", "chr2"),
             start = c(220, 210, 300, 400),
             end = c(225, 240, 320, 415))
genome_join_closest(x1, x2, by=c("chr", "start", "end"), distance_column_name="distance", mode="left")

genome_cluster

Add a new column with the cluster if 2 intervals are overlapping or are within the max_distance.

x1 <- data.frame(id = 1:4, bla=letters[1:4],
                chromosome = c("chr1", "chr1", "chr2", "chr1"),
                start = c(100, 120, 300, 260),
                end = c(150, 250, 350, 450))
genome_cluster(x1, by=c("chromosome", "start", "end"))
genome_cluster(x1, by=c("chromosome", "start", "end"), max_distance=10)

genome_complement

Calculates the complement of a genomic region.

x1 <- data.frame(id = 1:4,
                 chromosome = c("chr1", "chr1", "chr2", "chr1"),
                 start = c(100, 200, 300, 400),
                 end = c(150, 250, 350, 450))

genome_complement(x1, by=c("chromosome", "start", "end"))

genome_join

Classical join function based on the overlap of the interval. Implemented and mainted in the fuzzyjoin package and documented here only for completeness.

x1 <- tibble(id = 1:4, 
             chr = c("chr1", "chr1", "chr2", "chr3"),
             start = c(100, 200, 300, 400),
             end = c(150, 250, 350, 450))

x2 <- tibble(id = 1:4,
             chr = c("chr1", "chr1", "chr1", "chr2"),
             start = c(220, 210, 300, 400),
             end = c(225, 240, 320, 415))
fuzzyjoin::genome_join(x1, x2, by=c("chr", "start", "end"), mode="inner")

fuzzyjoin::genome_join(x1, x2, by=c("chr", "start", "end"), mode="left")

fuzzyjoin::genome_join(x1, x2, by=c("chr", "start", "end"), mode="anti")

const-ae/tidygenomics documentation built on April 17, 2021, 4:27 a.m.