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more tweaks to pkgdown
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mikelove committed Apr 8, 2024
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plyranges.png
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README.html
248 changes: 117 additions & 131 deletions README.md
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Expand Up @@ -5,11 +5,11 @@

<!-- badges: start -->

[![R-CMD-check-bioc](https://github.com/sa-lee/plyranges/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/sa-lee/plyranges/actions?query=workflow%3AR-CMD-check-bioc)
[![R-CMD-check-bioc](https://github.com/tidyomics/plyranges/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/tidyomics/plyranges/actions?query=workflow%3AR-CMD-check-bioc)
[![BioC
status](http://www.bioconductor.org/shields/build/release/bioc/plyranges.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/plyranges)
[![Codecov test
coverage](https://codecov.io/gh/sa-lee/plyranges/branch/master/graph/badge.svg)](https://codecov.io/gh/sa-lee/plyranges?branch=master)
coverage](https://codecov.io/gh/tidyomics/plyranges/branch/master/graph/badge.svg)](https://codecov.io/gh/tidyomics/plyranges?branch=master)
<!-- badges: end -->

[plyranges](https://www.bioconductor.org/packages/release/bioc/html/plyranges.html)
Expand All @@ -20,30 +20,29 @@ data transformation based on `dplyr` and the Bioconductor packages
a set of verbs for developing analysis pipelines based on *Ranges*
objects that represent genomic regions:

- Modify genomic regions with the `mutate()` and `stretch()`
functions.
- Modify genomic regions while fixing the start/end/center coordinates
with the `anchor_` family of functions.
- Sort genomic ranges with `arrange()`.
- Modify, subset, and aggregate genomic data with the `mutate()`,
`filter()`, and `summarise()`functions.
- Any of the above operations can be performed on partitions of the
data with `group_by()`.
- Find nearest neighbour genomic regions with the `join_nearest_`
family of functions.
- Find overlaps between ranges with the `join_overlaps_` family of
functions.
- Merge all overlapping and adjacent genomic regions with
`reduce_ranges()`.
- Merge the end points of all genomic regions with `disjoin_ranges()`.
- Import and write common genomic data formats with the `read_/write_`
family of functions.
- Modify genomic regions with the `mutate()` and `stretch()` functions.
- Modify genomic regions while fixing the start/end/center coordinates
with the `anchor_` family of functions.
- Sort genomic ranges with `arrange()`.
- Modify, subset, and aggregate genomic data with the `mutate()`,
`filter()`, and `summarise()`functions.
- Any of the above operations can be performed on partitions of the data
with `group_by()`.
- Find nearest neighbour genomic regions with the `join_nearest_` family
of functions.
- Find overlaps between ranges with the `join_overlaps_` family of
functions.
- Merge all overlapping and adjacent genomic regions with
`reduce_ranges()`.
- Merge the end points of all genomic regions with `disjoin_ranges()`.
- Import and write common genomic data formats with the `read_/write_`
family of functions.

For more details on the features of plyranges, read the
[vignette](https://sa-lee.github.io/plyranges/articles/an-introduction.html).
[vignette](https://tidyomics.github.io/plyranges/articles/an-introduction.html).
For a complete case-study on using plyranges to combine ATAC-seq and
RNA-seq results read the [*fluentGenomics*
workflow](https://sa-lee.github.io/fluentGenomics).
workflow](https://tidyomics.github.io/fluentGenomics).

# Installation

Expand All @@ -58,7 +57,7 @@ BiocManager::install("plyranges")
To install the development version from GitHub:

``` r
BiocManager::install("sa-lee/plyranges")
BiocManager::install("tidyomics/plyranges")
```

# Quick overview
Expand Down Expand Up @@ -156,54 +155,41 @@ We could check the number of exons per chromosome using `group_by` and
``` r
exons
#> GRanges object with 459752 ranges and 2 metadata columns:
#> seqnames ranges strand |
#> <Rle> <IRanges> <Rle> |
#> [1] chr1 11874-12227 + |
#> [2] chr1 12613-12721 + |
#> [3] chr1 13221-14409 + |
#> [4] chr1 14362-14829 - |
#> [5] chr1 14970-15038 - |
#> ... ... ... ... .
#> [459748] chrY 59338754-59338859 + |
#> [459749] chrY 59338754-59338859 + |
#> [459750] chrY 59340194-59340278 + |
#> [459751] chrY 59342487-59343488 + |
#> [459752] chrY 59342487-59343488 + |
#> name score
#> <character> <numeric>
#> [1] NR_046018_exon_0_0_chr1_11874_f 0
#> [2] NR_046018_exon_1_0_chr1_12613_f 0
#> [3] NR_046018_exon_2_0_chr1_13221_f 0
#> [4] NR_024540_exon_0_0_chr1_14362_r 0
#> [5] NR_024540_exon_1_0_chr1_14970_r 0
#> ... ... ...
#> [459748] NM_002186_exon_6_0_chrY_59338754_f 0
#> [459749] NM_176786_exon_7_0_chrY_59338754_f 0
#> [459750] NM_002186_exon_7_0_chrY_59340194_f 0
#> [459751] NM_002186_exon_8_0_chrY_59342487_f 0
#> [459752] NM_176786_exon_8_0_chrY_59342487_f 0
#> seqnames ranges strand | name score
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] chr1 11874-12227 + | NR_046018_exon_0_0_c.. 0
#> [2] chr1 12613-12721 + | NR_046018_exon_1_0_c.. 0
#> [3] chr1 13221-14409 + | NR_046018_exon_2_0_c.. 0
#> [4] chr1 14362-14829 - | NR_024540_exon_0_0_c.. 0
#> [5] chr1 14970-15038 - | NR_024540_exon_1_0_c.. 0
#> ... ... ... ... . ... ...
#> [459748] chrY 59338754-59338859 + | NM_002186_exon_6_0_c.. 0
#> [459749] chrY 59338754-59338859 + | NM_176786_exon_7_0_c.. 0
#> [459750] chrY 59340194-59340278 + | NM_002186_exon_7_0_c.. 0
#> [459751] chrY 59342487-59343488 + | NM_002186_exon_8_0_c.. 0
#> [459752] chrY 59342487-59343488 + | NM_176786_exon_8_0_c.. 0
#> -------
#> seqinfo: 93 sequences from an unspecified genome; no seqlengths
exons %>%
group_by(seqnames) %>%
summarise(n = n())
#> DataFrame with 49 rows and 2 columns
#> seqnames n
#> <Rle> <integer>
#> 1 chr1 43366
#> 2 chr1_gl000191_random 42
#> 3 chr1_gl000192_random 46
#> 4 chr10 19420
#> 5 chr11 24476
#> ... ... ...
#> 45 chrUn_gl000222 20
#> 46 chrUn_gl000223 22
#> 47 chrUn_gl000228 85
#> 48 chrX 18173
#> 49 chrY 4128
#> seqnames n
#> <Rle> <integer>
#> 1 chr1 43366
#> 2 chr10 19420
#> 3 chr11 24476
#> 4 chr12 24949
#> 5 chr13 7974
#> ... ... ...
#> 45 chrUn_gl000222 20
#> 46 chrUn_gl000223 22
#> 47 chrUn_gl000228 85
#> 48 chrX 18173
#> 49 chrY 4128
```

Next we create a column representing the transcript\_id with `mutate`:
Next we create a column representing the transcript_id with `mutate`:

``` r
exons <- exons %>%
Expand All @@ -218,32 +204,32 @@ overlap exons, as well as metadata from both objects.
olap <- join_overlap_inner(gwas, exons)
olap
#> GRanges object with 3439 ranges and 4 metadata columns:
#> seqnames ranges strand | name.x
#> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1079198 * | rs11260603
#> [2] chr1 1247494 * | rs12103
#> [3] chr1 1247494 * | rs12103
#> [4] chr1 1247494 * | rs12103
#> [5] chr1 1247494 * | rs12103
#> ... ... ... ... . ...
#> [3435] chrX 153764217 * | rs1050828
#> [3436] chrX 153764217 * | rs1050828
#> [3437] chrX 153764217 * | rs1050828
#> [3438] chrX 153764217 * | rs1050828
#> [3439] chrX 153764217 * | rs1050828
#> name.y score tx_id
#> <character> <numeric> <character>
#> [1] NR_038869_exon_2_0_chr1_1078119_f 0 NR_038869
#> [2] NM_001256456_exon_1_0_chr1_1247398_r 0 NM_001256456
#> [3] NM_001256460_exon_1_0_chr1_1247398_r 0 NM_001256460
#> [4] NM_001256462_exon_1_0_chr1_1247398_r 0 NM_001256462
#> [5] NM_001256463_exon_1_0_chr1_1247398_r 0 NM_001256463
#> ... ... ... ...
#> [3435] NM_001042351_exon_9_0_chrX_153764152_r 0 NM_001042351
#> [3436] NM_000402_exon_9_0_chrX_153764152_r 0 NM_000402
#> [3437] NM_001042351_exon_9_0_chrX_153764152_r 0 NM_001042351
#> [3438] NM_000402_exon_9_0_chrX_153764152_r 0 NM_000402
#> [3439] NM_001042351_exon_9_0_chrX_153764152_r 0 NM_001042351
#> seqnames ranges strand | name.x name.y score
#> <Rle> <IRanges> <Rle> | <character> <character> <numeric>
#> [1] chr1 1079198 * | rs11260603 NR_038869_exon_2_0_c.. 0
#> [2] chr1 1247494 * | rs12103 NM_001256456_exon_1_.. 0
#> [3] chr1 1247494 * | rs12103 NM_001256460_exon_1_.. 0
#> [4] chr1 1247494 * | rs12103 NM_001256462_exon_1_.. 0
#> [5] chr1 1247494 * | rs12103 NM_001256463_exon_1_.. 0
#> ... ... ... ... . ... ... ...
#> [3435] chrX 153764217 * | rs1050828 NM_001042351_exon_9_.. 0
#> [3436] chrX 153764217 * | rs1050828 NM_000402_exon_9_0_c.. 0
#> [3437] chrX 153764217 * | rs1050828 NM_001042351_exon_9_.. 0
#> [3438] chrX 153764217 * | rs1050828 NM_000402_exon_9_0_c.. 0
#> [3439] chrX 153764217 * | rs1050828 NM_001042351_exon_9_.. 0
#> tx_id
#> <character>
#> [1] NR_038869
#> [2] NM_001256456
#> [3] NM_001256460
#> [4] NM_001256462
#> [5] NM_001256463
#> ... ...
#> [3435] NM_001042351
#> [3436] NM_000402
#> [3437] NM_001042351
#> [3438] NM_000402
#> [3439] NM_001042351
#> -------
#> seqinfo: 93 sequences from an unspecified genome; no seqlengths
```
Expand Down Expand Up @@ -281,54 +267,54 @@ right_ss <- flank_right(exons, 2L)
all_ss <- interweave(left_ss, right_ss, .id = "side")
all_ss
#> GRanges object with 919504 ranges and 4 metadata columns:
#> seqnames ranges strand |
#> <Rle> <IRanges> <Rle> |
#> [1] chr1 11872-11873 + |
#> [2] chr1 12228-12229 + |
#> [3] chr1 12611-12612 + |
#> [4] chr1 12722-12723 + |
#> [5] chr1 13219-13220 + |
#> ... ... ... ... .
#> [919500] chrY 59340279-59340280 + |
#> [919501] chrY 59342485-59342486 + |
#> [919502] chrY 59343489-59343490 + |
#> [919503] chrY 59342485-59342486 + |
#> [919504] chrY 59343489-59343490 + |
#> name score tx_id side
#> <character> <numeric> <character> <character>
#> [1] NR_046018_exon_0_0_chr1_11874_f 0 NR_046018 left
#> [2] NR_046018_exon_0_0_chr1_11874_f 0 NR_046018 right
#> [3] NR_046018_exon_1_0_chr1_12613_f 0 NR_046018 left
#> [4] NR_046018_exon_1_0_chr1_12613_f 0 NR_046018 right
#> [5] NR_046018_exon_2_0_chr1_13221_f 0 NR_046018 left
#> ... ... ... ... ...
#> [919500] NM_002186_exon_7_0_chrY_59340194_f 0 NM_002186 right
#> [919501] NM_002186_exon_8_0_chrY_59342487_f 0 NM_002186 left
#> [919502] NM_002186_exon_8_0_chrY_59342487_f 0 NM_002186 right
#> [919503] NM_176786_exon_8_0_chrY_59342487_f 0 NM_176786 left
#> [919504] NM_176786_exon_8_0_chrY_59342487_f 0 NM_176786 right
#> seqnames ranges strand | name score
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] chr1 11872-11873 + | NR_046018_exon_0_0_c.. 0
#> [2] chr1 12228-12229 + | NR_046018_exon_0_0_c.. 0
#> [3] chr1 12611-12612 + | NR_046018_exon_1_0_c.. 0
#> [4] chr1 12722-12723 + | NR_046018_exon_1_0_c.. 0
#> [5] chr1 13219-13220 + | NR_046018_exon_2_0_c.. 0
#> ... ... ... ... . ... ...
#> [919500] chrY 59340279-59340280 + | NM_002186_exon_7_0_c.. 0
#> [919501] chrY 59342485-59342486 + | NM_002186_exon_8_0_c.. 0
#> [919502] chrY 59343489-59343490 + | NM_002186_exon_8_0_c.. 0
#> [919503] chrY 59342485-59342486 + | NM_176786_exon_8_0_c.. 0
#> [919504] chrY 59343489-59343490 + | NM_176786_exon_8_0_c.. 0
#> tx_id side
#> <character> <character>
#> [1] NR_046018 left
#> [2] NR_046018 right
#> [3] NR_046018 left
#> [4] NR_046018 right
#> [5] NR_046018 left
#> ... ... ...
#> [919500] NM_002186 right
#> [919501] NM_002186 left
#> [919502] NM_002186 right
#> [919503] NM_176786 left
#> [919504] NM_176786 right
#> -------
#> seqinfo: 93 sequences from an unspecified genome; no seqlengths
```

# Learning more

- The [*fluentGenomics*
workflow](https://sa-lee.github.io/fluentGenomics) package shows you
how to combine differential expression genes and differential
chromatin accessibility peaks using plyranges. It extends the [case
study](https://github.com/mikelove/plyrangesTximetaCaseStudy) by
Michael Love for using plyranges with
[tximeta](https://bioconductor.org/packages/release/bioc/html/tximeta.html).

- The [extended vignette in the plyrangesWorkshops
package](https://github.com/sa-lee/plyrangesWorkshops) has a
detailed walk through of using plyranges for coverage analysis.

- The [Bioc 2018 Workshop
book](https://bioconductor.github.io/BiocWorkshops/fluent-genomic-data-analysis-with-plyranges.html)
has worked examples of using `plyranges` to analyse publicly
available genomics data.
- The [*fluentGenomics*
workflow](https://sa-lee.github.io/fluentGenomics) package shows you
how to combine differential expression genes and differential
chromatin accessibility peaks using plyranges. It extends the [case
study](https://github.com/mikelove/plyrangesTximetaCaseStudy) by
Michael Love for using plyranges with
[tximeta](https://bioconductor.org/packages/release/bioc/html/tximeta.html).

- The [extended vignette in the plyrangesWorkshops
package](https://github.com/sa-lee/plyrangesWorkshops) has a detailed
walk through of using plyranges for coverage analysis.

- The [Bioc 2018 Workshop
book](https://bioconductor.github.io/BiocWorkshops/fluent-genomic-data-analysis-with-plyranges.html)
has worked examples of using `plyranges` to analyse publicly available
genomics data.

# Citation

Expand Down
4 changes: 4 additions & 0 deletions pkgdown/_pkgdown.yml
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Expand Up @@ -9,6 +9,10 @@ authors:
href: https://github.com/lawremi
Di Cook:
href: http://dicook.org
footer:
roles: [aut]
sidebar:
roles: [aut, ctb]

reference:
- title: About plyranges
Expand Down

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