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bailey-lab / miplicorn

A unified framework for molecular inversion probe and amplicon analysis. Provides the tools to parse, manipulate, analyze, and visualize data.
https://bailey-lab.github.io/miplicorn
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amplicon mip r

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miplicorn

R-CMD-check Codecov test coverage Lifecycle: experimental License: MIT

miplicorn establishes a unified analysis framework in R for molecular inversion probe (MIP) and amplicon-targeted sequencing analysis after micro haplotyping or variant calling. It provides tools for parsing large variant files, filtering and manipulating the data, and basic analyses and visualization.

Installation

You may install the package from Github using devtools.

``` r # Install most recent released version devtools::install_github("bailey-lab/miplicorn@v0.2.1") ```
``` r # Install development version devtools::install_github("bailey-lab/miplicorn") ```

Usage

See vignette("miplicorn") for a more extensive introduction and a demonstration of several features of the package.

library(miplicorn)

ref_file <- miplicorn_example("reference_AA_table.csv")
alt_file <- miplicorn_example("alternate_AA_table.csv")
cov_file <- miplicorn_example("coverage_AA_table.csv")

data <- read_tbl_ref_alt_cov(ref_file, alt_file, cov_file, gene == "atp6" | gene == "crt")
data
#> # A ref alt cov table: 832 × 10
#>   sample   gene_id gene  mutat…¹ exoni…² aa_ch…³ targe…⁴ ref_u…⁵ alt_u…⁶ cover…⁷
#>   <chr>    <chr>   <chr> <chr>   <chr>   <chr>   <chr>     <dbl>   <dbl>   <dbl>
#> 1 D10-JJJ… PF3D7_… atp6  atp6-A… missen… Ala623… Yes         608       0     608
#> 2 D10-JJJ… PF3D7_… atp6  atp6-A… missen… Ala623… Yes          20       0      20
#> 3 D10-JJJ… PF3D7_… atp6  atp6-A… missen… Ala623… Yes         158       0     158
#> 4 D10-JJJ… PF3D7_… atp6  atp6-A… missen… Ala623… Yes           2       0       2
#> 5 D10-JJJ… PF3D7_… atp6  atp6-A… missen… Ala623… Yes           1       0       1
#> # … with 827 more rows, and abbreviated variable names ¹​mutation_name,
#> #   ²​exonic_func, ³​aa_change, ⁴​targeted, ⁵​ref_umi_count, ⁶​alt_umi_count,
#> #   ⁷​coverage

plot_coverage(data, mutation_name)

prev <- mutation_prevalence(data, threshold = 5)
prev
#> # A tibble: 16 × 4
#>   mutation_name  n_total n_mutant prevalence
#>   <chr>            <int>    <int>      <dbl>
#> 1 atp6-Ala623Glu      36        0      0
#> 2 atp6-Glu431Lys      39        0      0
#> 3 atp6-Gly639Asp      26       19      0.731
#> 4 atp6-Ser466Asn      15        9      0.6
#> 5 atp6-Ser769Asn      17        0      0
#> # … with 11 more rows

plot(prev)