Experiment quality control

Checks how the uncertainty changes in a function of `out_time`.

quality_control(dat, state_first, state_second, chosen_time, in_time)

Arguments

dat	data read by read_hdx
state_first	state of the first peptide
state_second	state of the second peptide
chosen_time	chosen time point
in_time	`in` time

Value

data.frame with mean uncertainty per different `out_time` value

Details

The function calculates mean uncertainty of all peptides and its uncertainty (standard error) based on given `in_time` and `chosen_time` as a function of `out_time`. Both theoretical and experimental results for each state and their difference are supplied for comparison but only experimental calculations depends on `out_time` variable. The results are either in form of relative or absolute values depending on the `relative` parameter supplied by the user. This data can be useful for general overview of the experiment and analyse of the chosen time parameters.

See also

read_hdx

Examples

# load example data
dat <- read_hdx(system.file(package = "HaDeX", "HaDeX/data/KD_180110_CD160_HVEM.csv"))

# calculate mean uncertainty 
(result <- quality_control(dat = dat,
                           state_first = "CD160",
                           state_second = "CD160_HVEM", 
                           chosen_time = 1, 
                           in_time = 0.001))    
#>   out_time avg_err_state_first sd_err_state_first avg_err_state_second
#> 1        5         0.002925674        0.002207625          0.003811229
#> 2       25         0.002086275        0.001636480          0.003375989
#> 3      120         0.002124354        0.001284390          0.002759393
#> 4     1440         0.001841383        0.001032972          0.002532581
#>   sd_err_state_second    avg_diff     sd_diff
#> 1         0.004561764 0.004920756 0.004952401
#> 2         0.003690424 0.004053767 0.003949308
#> 3         0.001976257 0.003470818 0.002088545
#> 4         0.001120593 0.003200211 0.001369362
                           
# load extra libraries
library(ggplot2)
library(tidyr)
library(dplyr)

# example of data visualization 
gather(result, 2:7, key = 'type', value = 'value') %>%
filter(startsWith(type, "avg")) %>%
  ggplot(aes(x = factor(out_time), y = value, group = type)) +
  geom_line(aes(color = type)) +
  labs(x = "Out time", 
       y = "Mean uncertainty")