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README

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 âtidy-wayâ 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")

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"))

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")

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"))

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)

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"))

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")

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")

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")

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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