RMAFster

RMAfster allows to calculate RNA mutated allele frequencies (RMAFs) given a list of mutations and RNA-seq BAM files.

Installation

You can install the development version from github using devtools:

# install.packages("devtools")
devtools::install_github("fcaramia/RMAFster")

Basic example

library(RMAFster)
#> RMAFster uses a local python environment to execute RmafsterCalc,
#>   unless you specify a python environment using reticule::use_...
#>   you will be prompted to install miniconda the first time you use RmasterCalc.
#>   Select (Y) to proceed. Python dependencies will be handled automatically
samples = data.frame(
               sample_id='CT26',
               bam_path=system.file("extdata","CT26_chr8_115305465.bam",
               package = 'RMAFster', mustWork=TRUE),
               rna_purity=1,
               dna_purity=1,
               stringsAsFactors = FALSE)
mutations = data.frame(
                 chr='chr8',
                 pos=115305465,
                 ref='G',
                 alt='A',
                 sample_id='CT26',
                 symbol ='Cntnap4')
rmafs = RmafsterCalc(
     mutations,
     samples
)
#> Warning in RmafsterCalc(mutations, samples): var column not found in mutation
#> table, using SNP for all mutations
#> Warning in RmafsterCalc(mutations, samples): vaf column not found in mutation
#> table, using 0.5 for all mutations
#> Warning in RmafsterCalc(mutations, samples): dna_dp column not found in mutation
#> table, using 200 for all mutations

Once RMAFs are calculated you can quickly explore them and compare groups of genes/samples/etc..

rmafs = data.frame(
            rmaf = c(sample(800:900,100,replace = TRUE)/1000,
                      sample(400:600,90,replace = TRUE)/1000,
                      sample(0:900,80,replace = TRUE)/1000,
                      sample(0:300,60,replace = TRUE)/1000,
                      sample(1:900,10,replace = TRUE)/1000
                     ),
            rna_purity = c(rep(.9,340)),
            dna_purity = c(rep(.8,340)),
            rna_dp = c(sample(20:500,340,replace = TRUE)),
            dna_dp = c(sample(100:500,340,replace = TRUE)),
            vaf = c(sample(50:800,340,replace = TRUE)/1000),
            symbol = c(rep('gene1',100),
                       rep('gene2',90),
                       rep('gene3',80),
                       rep('gene4',60),
                       rep('gene5',10)),
             stringsAsFactors = FALSE
               )

RmafsterExpl(
     rmafs,
     'symbol',
     20,
     print_plot = TRUE
)

#> # A tibble: 330 x 10
#>    symbol n_muts  rmaf rna_purity dna_purity rna_dp dna_dp   vaf    z1    z2
#>    <chr>   <int> <dbl>      <dbl>      <dbl>  <int>  <int> <dbl> <dbl> <dbl>
#>  1 gene1     100 0.847        0.9        0.8     28    241 0.097  4.22 10.1 
#>  2 gene1     100 0.85         0.9        0.8    328    433 0.063 14.6  23.8 
#>  3 gene1     100 0.822        0.9        0.8     77    238 0.407  6.56  6.32
#>  4 gene1     100 0.833        0.9        0.8    415    151 0.302 15.7  13.9 
#>  5 gene1     100 0.879        0.9        0.8    473    133 0.119 18.8  20.9 
#>  6 gene1     100 0.848        0.9        0.8    461    359 0.421 17.2  13.8 
#>  7 gene1     100 0.884        0.9        0.8    208    231 0.589 12.6   7.26
#>  8 gene1     100 0.871        0.9        0.8    112    307 0.373  8.96  9.28
#>  9 gene1     100 0.819        0.9        0.8    431    357 0.401 15.4  12.8 
#> 10 gene1     100 0.895        0.9        0.8    448    129 0.279 18.9  18.1 
#> # … with 320 more rows