tidygenomics

Tidy Verbs for Dealing with Genomic Data Frames

Description

Handle genomic data within data frames just as you would with GRanges. This packages provides method to deal with genomics intervals the "tidy-way" which makes it simpler to integrate in the the general data munging process. The API is inspired by the popular bedtools and the genome_join() method from the fuzzyjoin package.

Installation

install.packages("tidygenomics")

Or to get the latest development version

devtools::install_github("const-ae/tidygenomics")

Documentation

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")

id.x	chromosome	id.y	start	end
1	chr1	1	140	150
4	chr2	3	400	415

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"))

id	chromosome	start	end
1	chr1	100	119
1	chr1	126	150
2	chr1	200	250
3	chr2	300	350
4	chr1	416	450

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

x2 <- data_frame(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")

id.x	chr.x	start.x	end.x	id.y	chr.y	start.y	end.y	distance
1	chr1	100	150	2	chr1	210	240	59
2	chr1	200	250	1	chr1	220	225	0
2	chr1	200	250	2	chr1	210	240	0
3	chr2	300	350	4	chr2	400	415	49
4	chr3	400	450	NA	NA	NA	NA	NA

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"))

id	bla	chromosome	start	end	cluster_id
1	a	chr1	100	150	0
2	b	chr1	120	250	0
3	c	chr2	300	350	2
4	d	chr1	260	450	1

genome_cluster(x1, by=c("chromosome", "start", "end"), max_distance=10)

id	bla	chromosome	start	end	cluster_id
1	a	chr1	100	150	0
2	b	chr1	120	250	0
3	c	chr2	300	350	1
4	d	chr1	260	450	0

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"))

chromosome	start	end
chr1	1	99
chr1	151	199
chr1	251	399
chr2	1	299

genome_join

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

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

x2 <- data_frame(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")

id.x	chr.x	start.x	end.x	id.y	chr.y	start.y	end.y
2	chr1	200	250	1	chr1	220	225
2	chr1	200	250	2	chr1	210	240

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

id.x	chr.x	start.x	end.x	id.y	chr.y	start.y	end.y
1	chr1	100	150	NA	NA	NA	NA
2	chr1	200	250	1	chr1	220	225
2	chr1	200	250	2	chr1	210	240
3	chr2	300	350	NA	NA	NA	NA
4	chr3	400	450	NA	NA	NA	NA

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

id	chr	start	end
1	chr1	100	150
3	chr2	300	350
4	chr3	400	450

Inspiration

If you have any additional questions or encounter issues please raise them on the github page.

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