In mvazquezs/chmitools: Package to manage & obtain results of the CHMI Study

knitr::opts_chunk$set(comment = NA,
  fig.path = '02_figures_aim1_f/',
  fig.width = 20,
  fig.height = 12,
  results = 'markup',
  tidy = FALSE,
  message = FALSE,
  warning = FALSE,
  echo = FALSE)

alpha <- 0.05

r packages

library(devtools)
library(prettydoc)

# avoid 'objects' in workspace
rm(list = ls())

setwd(Sys.getenv('HOME'))


if(Sys.info()[["nodename"]] == 'mbp-de-csc.csc.es'){

load_all('~/Documents/git/chmitools')  

} else if(Sys.info()[["nodename"]] != 'mbp-de-csc.csc.es'){

load_all('~/git/chmitools')
}

load data `fold_change`

Main bullet points in load data section.
* chmi.phen() load data by group and aim, plus argument fold_change.

# load data
dat_f <- chmi.phen(
  study_chmi = 'antibodies',
  aim_ab_chmi = 'aim_1',
  fold_change = TRUE)


# list
  n_igg <- sort(unique(dat_f$t_igg))
  l_group <- c('t_igg', 'antigen_ag', 't2_point_ag')

# list of adjust
  l_pval <- c('raw_pval', 'adjust_pval')

differences in `mfi` vs `status_ag` in `fold change`

Main bullet points in load data section.
chmi.stat.oneway_test() obtain raw and adjusted p-values.
filter data by t_igg, antigen_ag and t2_point_ag columns.
we are used p.adjust() function to adjust p-values by Benjamini & Hochberg method.
tab_1 filtered by t2_point_ag D7, D11_D13 & D28.

# p_value tab_fold_change
  tab_1 <- chmi.stat.oneway_test(
    phen = dat_f,
    l_group = l_group,
    t_var_1 = 'log10_rmfi',
    t_var_2 = 'status_ag',
    padj_method = 'BH',
        fold_change = TRUE)

## datatable `tab_1`
  datatable(tab_1,
    class = 'cell-border stripe',
    filter = 'top',
    extensions = c('Buttons', 'FixedColumns'),
    options = list(dom = 'Bfrtip',
      buttons = c('csv', 'copy'),
      pageLength = 10,
      scrollX = TRUE,
      text = 'Download')) %>%
    formatRound(l_pval, 5)

# update y_max to plot `antigen_ag ~ t_igg`
  tab_1 <- tab_1 %>%
        mutate(p_ymax = as.numeric(y_max + 1.2))

boxplot `log10_rmfi` vs `status_ag` by fold_change

# plots
bp_1 <-  chmi.stat_plot.pval_boxfold(
  phen = dat_f,
  pval_phen = tab_1,
  l_group = l_group[1:2],
  l_igg = n_igg,
  var_y = log10_rmfi,
  aes_color = status_ag)

# print
  print(bp_1)

save in `r_data` folder

# save

if(Sys.info()[["nodename"]] == 'mbp-de-csc.csc.es'){

save.image(file = '~/Documents/git/chmitools/projects/01_aim1/r_data/01_aim1_f.RData')

} else if(Sys.info()[["nodename"]] != 'mbp-de-csc.csc.es'){

  save.image(file = '~/git/chmitools/projects/01_aim1/r_data/01_aim1_f.RData')
}