Tidy Verbs for Dealing with Genomic Data Frames

Handle genomic data within data frames just as you would with 'GRanges'. This packages provides method to deal with genomic 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.


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

devtools::install_github("Artjom-Metro/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.

genome_intersect
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.

genome_subtract
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.

genome_join_closest
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.

genome_cluster
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.

genome_complement
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 mainted in the fuzzyjoin package and documented here only for completeness.

genome_join
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.

News

Reference manual

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install.packages("tidygenomics")

0.1.0 by Constantin Ahlmann-Eltze, 8 months ago


https://github.com/const-ae/tidygenomics


Browse source code at https://github.com/cran/tidygenomics


Authors: Constantin Ahlmann-Eltze [aut, cre], Stan Developers [cph] (Code from the Stan Math library is reused in 'cluster_interval.cpp'), David Robinson [cph] (Code from the fuzzyjoin package is reused)


Documentation:   PDF Manual  


GPL-3 license


Imports dplyr, purrr, tidyr, fuzzyjoin, IRanges, Rcpp

Suggests testthat, knitr, rmarkdown

Linking to Rcpp


See at CRAN