knitr::opts_chunk$set(comment = NA, fig.path = '01_figures_aim3/', fig.width = 20, fig.height = 12, results = 'markup', tidy = F, message = F, warning = F, echo = F)
alpha <- 0.05
library(moonBook) library(prettydoc)
setwd('~/Documents/git/chmitools') # mac source('~/Documents/git/myR_setup/myR.profile') # mac # source('~/git/myR_setup/myR.profile') # home
load data
section.
chmi.phen()
load data by group and aim.
filter data by isotype_igg
, antigen
and t_point
/t2_point
columns.
# load data dat <- chmi.phen(group = 'mfi', aim_chmi = 'aim_3') # list n_igg <- sort(unique(dat$isotype_igg))
mfi
vs gr_hbs
and t2_point
after/before challengeload data
section.
chmi.stat.oneway_test()
obtain raw and adjusted p-values.
we are used p.adjust()
function to adjust p-values by Benjamini & Hochberg
method.
tab_1 or before challenge all
: filtered by t_point
C-1.
tab_2 or after challenge within 3 t_point
: filtered by t2_point
D7, D13 & D28.
* tab_3 or after challenge within 4 t_point
: filtered by t2_point
C-1, D7, D13 & D28.
# list l_vars1 <- c('isotype_igg', 'antigen') l_vars2 <- c('isotype_igg', 'antigen', 't2_point') # filer data by 't_point' & 't2_point' dat_1 <- dat %>% filter(t2_point == 'C-1') dat_2 <- dat %>% filter(t2_point %in% c('D7', 'D13', 'D28')) dat_3 <- dat %>% filter(t2_point %in% c('C-1', 'D7', 'D13', 'D28')) # p_value tab_1 tab_1 <- chmi.stat.oneway_test(dat_1, group_by = l_vars1, test_var = 'gr_hbs') # p_value tab_2 tab_2 <- chmi.stat.oneway_test(dat_2, group_by = l_vars2, test_var = 'gr_hbs') # p_value tab_3 tab_3 <- chmi.stat.oneway_test(dat_3, group_by = l_vars2, test_var = 'gr_hbs') # datatable `tab_1` datatable(tab_1, class = 'cell-border stripe', extensions = c('Buttons','FixedColumns'), filter = 'top', options = list(dom = 'Bfrtip', pageLength = 30, buttons = c('copy', 'csv'), scrollX = TRUE, text = 'Download')) %>% formatSignif(c('raw_pval', 'adjust_pval'), 3) # datatable `tab_2` datatable(tab_2, class = 'cell-border stripe', extensions = c('Buttons','FixedColumns'), filter = 'top', options = list(dom = 'Bfrtip', pageLength = 30, buttons = c('copy', 'csv'), scrollX = TRUE, text = 'Download')) %>% formatSignif(c('raw_pval', 'adjust_pval'), 3) # datatable `tab_3` datatable(tab_3, class = 'cell-border stripe', extensions = c('Buttons','FixedColumns'), filter = 'top', options = list(dom = 'Bfrtip', pageLength = 30, buttons = c('copy', 'csv'), scrollX = TRUE, text = 'Download')) %>% formatSignif(c('raw_pval', 'adjust_pval'), 3)
mfi
vs gr_hbs
after/before challenget_point = C-1
# plots boxplot_1 <- chmi.stat_plot.pval_boxplot(dat_1, tab_1, l_igg = n_igg, aes_var = 'gr_hbs', aes_color = 'gr_hbs') # print print(boxplot_1)
t2_point = c(D7, D13, D28)
# plots boxplot_2 <- chmi.stat_plot.pval_boxplot(dat_2, tab_2, l_igg = n_igg, aes_var = 't2_point', aes_color = 'gr_hbs') # print print(boxplot_2)
t2_point = c('C-1', 'D7', 'D13', 'D28')
# plots boxplot_3 <- chmi.stat_plot.pval_boxplot(dat_3, tab_3, l_igg = n_igg, aes_var = 't2_point', aes_color = 'gr_hbs') # print print(boxplot_3)
mfi
vs gr_hbs
after/before challenget2_point = c(D7, D13, D28)
# plots lineplot_1 <- chmi.stat_plot.median_line(dat_2, n_igg, aes_color = 'gr_hbs') # print print(lineplot_1)
t2_point = c('C-1', 'D7', 'D13', 'D19', 'D28')
# plots lineplot_2 <- chmi.stat_plot.median_line(dat_3, n_igg, aes_color = 'gr_hbs') # print print(lineplot_2)
aim_3
chmi.stat_plot.median_line()
did not shown the same original format.
r_data
folder# save # save.image(file = '/Users/migvazquez/Documents/git/chmitools/projects/01_aim1/r_data/01_aim3.RData')
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