Baracoda Output Analysis

The packges will take pre-processed copy of raw baracoda files and calculate estimated frequency

Installation and background

The packges will take pre-processed copy of raw baracoda files and mupexi files and illustrate the characteristics of the immunugenic neopeptides. The packgaes works only with murine data, The human data input is in progress.

# install.packages("devtools")
library(devtools)
devtools::install_github("SRHgroup/BaracodaOutputAnalysis", force = T)
library(BaracodaOutputAnalysis)

BaracodaOutputAnalysis is built with tidyverse and is required for running. ## required packages

library(tidyverse)
library(readxl)
library(openxlsx)
library(ggplot2)
library(ggrepel)
library(ggbeeswarm)
library(knitr)

Load data

Baracoda files

# name of fold change file
baracoda_output <- "test_data/fold_change_example_file_new.xlsx"

# output directory
results_figures_path <- "results/figures/"
results_tables_path <- "results/tabels/"

# if you allready have PE table 
percent_FU <- "test_data/percent_PE_modi.xlsx"
# else follow the guidance and run the function "generate_FU_template_tab" after loading data 

log fold change files

Load data

# load data
my_barrcoda_data <- BaracodaOutputAnalysis::load_data(data = baracoda_output)

Add percent flourchrome information

generate template

# iF you want a template run function
BaracodaOutputAnalysis::generate_FU_template_tab(data = my_barrcoda_data, outfile = "test_data/percent_PE.xlsx")

Then change template with corresponding PE generate template

# load file
percent_FU <- "test_data/percent_PE_modi.xlsx"
FU_data <- read.xlsx(percent_FU)

# load file
head(FU_data)
#>   sample percent_pe
#> 1   1849        1.0
#> 2   2389        0.3
#> 3   7577        0.5
#> 4   7729        0.8
#> 5   9517        1.2

responses and estimated frequency

Add column with response

my_barrcoda_data <- BaracodaOutputAnalysis::add_response_by_threshold(data = my_barrcoda_data,
         FC = 2, p_val = 0.001)

my_barrcoda_data %>% group_by(sample,response) %>% tally()
#> # A tibble: 10 × 3
#> # Groups:   sample [5]
#>    sample response     n
#>     <dbl> <chr>    <int>
#>  1   1849 no        1362
#>  2   1849 yes         87
#>  3   2389 no        1858
#>  4   2389 yes         50
#>  5   7577 no        1025
#>  6   7577 yes         45
#>  7   7729 no        1204
#>  8   7729 yes         86
#>  9   9517 no        2001
#> 10   9517 yes        135

Add estimated frequency and normalized estimated frequency

my_barrcoda_data <- BaracodaOutputAnalysis::estimated_frequency(data = my_barrcoda_data, fluochrome_dat = percent_FU)

my_barrcoda_data %>% 
  select(sample,Peptide,HLA,estimated_frequency, estimated_frequency_normalised_responses)
#> # A tibble: 7,853 × 5
#> # Groups:   sample [5]
#>    sample Peptide        HLA   estimated_frequency estimated_frequency_normali…¹
#>     <dbl> <chr>          <chr>               <dbl>                         <dbl>
#>  1   1849 CMV pp65 YSE   A0101            0.0513                        0.0170  
#>  2   1849 CMV pp50 VTE   A0101            0.0365                        0.0121  
#>  3   1849 Patient1849_18 A0101            0.00344                       0.00114 
#>  4   1849 Patient1849_15 A0101            0.00283                       0.000939
#>  5   1849 Patient1849_1  A0101            0.00420                       0.00139 
#>  6   1849 Patient1849_28 A0101            0.00131                       0.000436
#>  7   1849 Patient1849_19 A0101            0.00189                       0.000627
#>  8   1849 Patient1849_13 A0101            0.00113                       0.000375
#>  9   1849 Patient1849_14 A0101            0.00110                       0.000366
#> 10   1849 Patient1849_27 A0101            0.000931                      0.000309
#> # ℹ 7,843 more rows
#> # ℹ abbreviated name: ¹​estimated_frequency_normalised_responses

Time for plotting

Overall responses

p <- BaracodaOutputAnalysis::barc_resp(data = my_barrcoda_data, est_freq = 'estimated_frequency')

p

BaracodaOutputAnalysis::save_figure(plot = p, name = "response_figure" , width = 18 , height = 8)

Write output table

write.table(my_barrcoda_data, file = "results/tabels/barracoda_output_final.tsv", sep = "\t", quote = F, row.names = F)

done