Package 'HaDeX' reference manual

Title:	Analysis and Visualisation of Hydrogen/Deuterium Exchange Mass Spectrometry Data
Description:	Functions for processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS) (10.1093/bioinformatics/btaa587). 'HaDeX' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the in-built 'Shiny' app.
Authors:	Weronika Puchala [cre, aut] , Michal Burdukiewicz [aut] , Dominik Rafacz [ctb]
Maintainer:	Weronika Puchala <[email protected]>
License:	GPL-3
Version:	1.2.2
Built:	2025-02-14 04:27:22 UTC
Source:	https://github.com/cran/HaDeX

HaDeX

Description

The HaDeX package is a toolbox for the analysis of HDX-MS data.

Author(s)

Weronika Puchala, Michal Burdukiewicz.

calc_dat extended by column specifying if given peptide is relevant in given confidence limit. The value of the confidence limit is added as an attribute - as well as parameters used to calculate (theoretical/relative)

Examples

#load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# prepate dataset for states `CD160` and `CD160_HVEM` in given time parameters 
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440") 
                            
# add calculated confidence limits for prepared data
add_stat_dependency(calc_dat, 
                    confidence_limit = 0.98, 
                    theoretical = FALSE, 
                    relative = TRUE)
                     

#load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# prepate dataset for states `CD160` and `CD160_HVEM` in given time parameters 
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440") 
                            
# add calculated confidence limits for prepared data
add_stat_dependency(calc_dat, 
                    confidence_limit = 0.98, 
                    theoretical = FALSE, 
                    relative = TRUE)

Calculate the value of confidence limit

Description

Calculates confidence limit values for prepared dataset, based on chosen parameters.

Usage

calculate_confidence_limit_values(
  calc_dat,
  confidence_limit = 0.98,
  theoretical = FALSE,
  relative = TRUE
)
calculate_confidence_limit_values(
  calc_dat,
  confidence_limit = 0.98,
  theoretical = FALSE,
  relative = TRUE
)

Arguments

`calc_dat`	processed data from DynamX file - using prepare_dataset
`confidence_limit`	confidence limit chosen by user - from range [0, 1].
`theoretical`	logical value to determine if plot is theoretical or not.
`relative`	logical value to determine if values are relative or absolute.

Details

...

Value

range of confidence limit interval

References

Houde, D., Berkowitz, S.A., and Engen, J.R. (2011). The Utility of Hydrogen/Deuterium Exchange Mass Spectrometry in Biopharmaceutical Comparability Studies. J Pharm Sci 100, 2071–2086.

Examples

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

# prepare dataset for states `CD160` and `CD160_HVEM` in given time parameters 
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440")   
                            
# calculates confidence limits for prepared data  
calculate_confidence_limit_values(calc_dat = calc_dat,
                                  confidence_limit = 0.99,
                                  theoretical = FALSE, 
                                  relative = TRUE)                          
                              
# load example data
dat <- read_hdx(system.file(package = "HaDeX", "HaDeX/data/KD_180110_CD160_HVEM.csv"))

# prepare dataset for states `CD160` and `CD160_HVEM` in given time parameters 
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440")   
                            
# calculates confidence limits for prepared data  
calculate_confidence_limit_values(calc_dat = calc_dat,
                                  confidence_limit = 0.99,
                                  theoretical = FALSE, 
                                  relative = TRUE)

Calculate kinetic data

Description

Calculate kinetics of the hydrogen-deuteration exchange for given peptide.

Usage

calculate_kinetics(
  dat,
  protein = dat[["Protein"]][1],
  sequence,
  state,
  start,
  end,
  time_in,
  time_out,
  deut_part = 1
)
calculate_kinetics(
  dat,
  protein = dat[["Protein"]][1],
  sequence,
  state,
  start,
  end,
  time_in,
  time_out,
  deut_part = 1
)

Arguments

`dat`	dat data read by `read_hdx`
`protein`	protein value for chosen peptide
`sequence`	sequence of the peptide for which the kinetics is calculated
`state`	state of given sequence
`start`	end of given sequence
`end`	end of given sequence
`time_in`	time in for experimental calculations
`time_out`	time out for experimental calculations
`deut_part`	percentage of deuterium the protein was exposed to, value in range [0, 1]

Details

The function calculates deuteration data for all available data points for given peptide. All four variants (relative & theoretical combinations) of deuteration computations are supported. Manual correction of percentage of deuterium the protein was exposed to during the exchange in theoretical calculations is provided. To visualize obtained data we recommend using plot_kinetics function. The first version doesn't support filled Modification and Fragment columns.

Value

data frame with deuteration calculated for all the data points between time_in and time_out. The chosen time point for which deuteration in all four variants is calculated is available in column 'time_chosen'. The rest of the returned structure is equivalent to structure returned by calculate_state_deuteration.

Examples

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

# calculate data for sequence INITSSASQEGTRLN in state CD160
(kin1 <- calculate_kinetics(dat, 
                   protein = "db_CD160",
                   sequence = "INITSSASQEGTRLN", 
                   state = "CD160",
                   start = 1, 
                   end = 15,
                   time_in = 0.001, 
                   time_out = 1440))
                   
# calculate data for sequence INITSSASQEGTRLN in state CD160_HVEM
(kin2 <- calculate_kinetics(dat, 
                   protein = "db_CD160",
                   sequence = "INITSSASQEGTRLN", 
                   state = "CD160_HVEM",
                   start = 1, 
                   end = 15,
                   time_in = 0.001, 
                   time_out = 1440))
                   
# load extra libraries
library(dplyr)
library(ggplot2)

# plot example - experimental and relative 
bind_rows(kin1, kin2) %>% 
 plot_kinetics(theoretical = FALSE, 
               relative = TRUE) +
 labs(title = "Kinetic plot for INITSSASQEGTRLN")
 
# plot example - theoretical and absolute
bind_rows(kin1, kin2) %>%
  plot_kinetics(theoretical = TRUE, 
               relative = FALSE) +
  labs(title = "Theoretical kinetics plot for INITSSASQEGTRLN")
       

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

# calculate data for sequence INITSSASQEGTRLN in state CD160
(kin1 <- calculate_kinetics(dat, 
                   protein = "db_CD160",
                   sequence = "INITSSASQEGTRLN", 
                   state = "CD160",
                   start = 1, 
                   end = 15,
                   time_in = 0.001, 
                   time_out = 1440))
                   
# calculate data for sequence INITSSASQEGTRLN in state CD160_HVEM
(kin2 <- calculate_kinetics(dat, 
                   protein = "db_CD160",
                   sequence = "INITSSASQEGTRLN", 
                   state = "CD160_HVEM",
                   start = 1, 
                   end = 15,
                   time_in = 0.001, 
                   time_out = 1440))
                   
# load extra libraries
library(dplyr)
library(ggplot2)

# plot example - experimental and relative 
bind_rows(kin1, kin2) %>% 
 plot_kinetics(theoretical = FALSE, 
               relative = TRUE) +
 labs(title = "Kinetic plot for INITSSASQEGTRLN")
 
# plot example - theoretical and absolute
bind_rows(kin1, kin2) %>%
  plot_kinetics(theoretical = TRUE, 
               relative = FALSE) +
  labs(title = "Theoretical kinetics plot for INITSSASQEGTRLN")

Calculate deuteration

Description

Calculates deuteration uptake based on supplied parameters.

Usage

calculate_state_deuteration(
  dat,
  protein,
  state,
  time_in,
  time_chosen,
  time_out,
  deut_part = 1
)
calculate_state_deuteration(
  dat,
  protein,
  state,
  time_in,
  time_chosen,
  time_out,
  deut_part = 1
)

Arguments

`dat`	data as imported by the `read_hdx` function
`protein`	protein included in calculations
`state`	state included in calculations
`time_in`	experimental 'time_in'
`time_chosen`	chosen time point
`time_out`	experimental 'time_out'
`deut_part`	percentage of deuterium the protein was exposed to, value in range [0, 1]

Details

The function calculate_state_deuteration calculates deuteration for peptides in given protein in given state based on supplied parameters: 'time_in', 'time_out' and 'time_chosen'. All four variants (combinations of theoretical & relative) are supplied (mean values and uncertainty). Manual correction of percentage of deuterium the protein was exposed to during the exchange in theoretical calculations is provided.

Methods of calculation and uncertainty are profoundly discussed in the vignette.

Value

a data.frame object

Examples

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

# calculate deuteration for state "CD160"
calculate_state_deuteration(dat, protein = "db_CD160", state = "CD160",
                            time_in = 0, time_chosen = 5.000, time_out = 1440.000)

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

# calculate deuteration for state "CD160"
calculate_state_deuteration(dat, protein = "db_CD160", state = "CD160",
                            time_in = 0, time_chosen = 5.000, time_out = 1440.000)

Plot comparison plot

Description

Produces comparison_plot based on previously processed data - theoretical or experimental. User can change labels if needed.

Usage

comparison_plot(
  calc_dat,
  theoretical = FALSE,
  relative = TRUE,
  state_first = "state_first",
  state_second = "state_second"
)
comparison_plot(
  calc_dat,
  theoretical = FALSE,
  relative = TRUE,
  state_first = "state_first",
  state_second = "state_second"
)

Arguments

`calc_dat`	processed data from DynamX file - using prepare_dataset
`theoretical`	logical value to determine if plot is theoretical or not. default : false
`relative`	logical value to determine if values are relative or absolute. default : true
`state_first`	first state name
`state_second`	second state name

Details

...

This is the first version - multi-state calculations are not supported.

Value

a ggplot object.

Examples

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

# prepare dataset for states `CD160` and `CD160_HVEM` in given time parameters
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440")       

# plot comparison plot - theoretical & relative                      
comparison_plot(calc_dat = calc_dat,
                theoretical = TRUE,
                relative = TRUE,
                state_first = "CD160",
                state_second = "CD160_HVEM")
                
# plot comparison plot - experimental & relative                      
comparison_plot(calc_dat = calc_dat,
                theoretical = FALSE,
                relative = TRUE,
                state_first = "CD160",
                state_second = "CD160_HVEM")  
                
# plot comparison plot - theoretical & absolute                      
comparison_plot(calc_dat = calc_dat,
                theoretical = TRUE,
                relative = FALSE,
                state_first = "CD160",
                state_second = "CD160_HVEM")

# plot comparison plot - experimental & absolute                      
comparison_plot(calc_dat = calc_dat,
                theoretical = FALSE,
                relative = FALSE,
                state_first = "CD160",
                state_second = "CD160_HVEM")                

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

# prepare dataset for states `CD160` and `CD160_HVEM` in given time parameters
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440")       

# plot comparison plot - theoretical & relative                      
comparison_plot(calc_dat = calc_dat,
                theoretical = TRUE,
                relative = TRUE,
                state_first = "CD160",
                state_second = "CD160_HVEM")
                
# plot comparison plot - experimental & relative                      
comparison_plot(calc_dat = calc_dat,
                theoretical = FALSE,
                relative = TRUE,
                state_first = "CD160",
                state_second = "CD160_HVEM")  
                
# plot comparison plot - theoretical & absolute                      
comparison_plot(calc_dat = calc_dat,
                theoretical = TRUE,
                relative = FALSE,
                state_first = "CD160",
                state_second = "CD160_HVEM")

# plot comparison plot - experimental & absolute                      
comparison_plot(calc_dat = calc_dat,
                theoretical = FALSE,
                relative = FALSE,
                state_first = "CD160",
                state_second = "CD160_HVEM")

HaDeX Graphical User Interface

Description

Launches graphical user interface.

Usage

HaDeX_gui(port = getOption("shiny.port"))
HaDeX_gui(port = getOption("shiny.port"))

Arguments

port

The TCP port. See runApp.

Warning

Any ad-blocking software may cause malfunctions.

Plot peptide coverage

Description

Plots the peptide coverage of the protein sequence.

Usage

plot_coverage(
  dat,
  protein = dat[["Protein"]][1],
  chosen_state = dat[["State"]][1]
)
plot_coverage(
  dat,
  protein = dat[["Protein"]][1],
  chosen_state = dat[["State"]][1]
)

Arguments

`dat`	data as imported by the `read_hdx` function
`protein`	protein to be included in plot
`chosen_state`	sequence states to be included in plot

Details

The function plot_coverage plots sequence coverage based on experimental data for chosen protein in chosen state. Only non-duplicated peptides are shown on the plot, next to each other.

The aim of this plot is to see the dependence between positions of the peptides on the protein sequence. Their position in y-axis does not contain any information.

Value

a ggplot object.

Examples

# load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# plot coverage with default parameters
plot_coverage(dat)

# plot coverage with explicit parameters
plot_coverage(dat, protein = "db_CD160", chosen_state = "CD160_HVEM")

# load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# plot coverage with default parameters
plot_coverage(dat)

# plot coverage with explicit parameters
plot_coverage(dat, protein = "db_CD160", chosen_state = "CD160_HVEM")

Plot kinetics data

Description

Plots kinetics of the hydrogen-deuterium exchange for specific peptides.

Usage

plot_kinetics(kin_dat, theoretical = FALSE, relative = TRUE)
plot_kinetics(kin_dat, theoretical = FALSE, relative = TRUE)

Arguments

`kin_dat`	calculated kinetic data by `calculate_kinetics` function
`theoretical`	`logical`, determines if plot shows theoretical values
`relative`	`logical`, determines if values are relative or absolute

Details

This function visualises the output of the calculate_kinetics function. Based on supplied parameters appropriate columns are chosen for the plot. The uncertainty associated with each peptide is shown as a ribbon. Axis are labeled according to the supplied parameters but no title is provided.

If you want to plot data for more then one peptide in one state, join calculated data by using bind_rows from dplyr package and pass the result as kin_dat.

Value

a ggplot object.

Examples

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

# calculate data for the sequence INITSSASQEGTRLN in the CD160 state 
(kin1 <- calculate_kinetics(dat, 
                           protein = "db_CD160",
                           sequence = "INITSSASQEGTRLN", 
                           state = "CD160",
                           start = 1, 
                           end = 15,
                           time_in = 0.001, 
                           time_out = 1440))

# calculate data for the sequence INITSSASQEGTRLN in the CD160_HVEM state 
(kin2 <- calculate_kinetics(dat, 
                           protein = "db_CD160",
                           sequence = "INITSSASQEGTRLN", 
                           state = "CD160_HVEM",
                           start = 1, 
                           end = 15,
                           time_in = 0.001, 
                           time_out = 1440)) 
                         
# load extra packages 
library(dplyr)
  
# plot a single peptide - theoretical and relative
plot_kinetics(kin_dat = kin1, 
              theoretical = TRUE, 
              relative = TRUE)
                
# plot joined data - experimental and absolute
bind_rows(kin1, kin2) %>%
  plot_kinetics(theoretical = FALSE, 
                relative = FALSE)
                
# load example data
dat <- read_hdx(system.file(package = "HaDeX", "HaDeX/data/KD_180110_CD160_HVEM.csv"))

# calculate data for the sequence INITSSASQEGTRLN in the CD160 state 
(kin1 <- calculate_kinetics(dat, 
                           protein = "db_CD160",
                           sequence = "INITSSASQEGTRLN", 
                           state = "CD160",
                           start = 1, 
                           end = 15,
                           time_in = 0.001, 
                           time_out = 1440))

# calculate data for the sequence INITSSASQEGTRLN in the CD160_HVEM state 
(kin2 <- calculate_kinetics(dat, 
                           protein = "db_CD160",
                           sequence = "INITSSASQEGTRLN", 
                           state = "CD160_HVEM",
                           start = 1, 
                           end = 15,
                           time_in = 0.001, 
                           time_out = 1440)) 
                         
# load extra packages 
library(dplyr)
  
# plot a single peptide - theoretical and relative
plot_kinetics(kin_dat = kin1, 
              theoretical = TRUE, 
              relative = TRUE)
                
# plot joined data - experimental and absolute
bind_rows(kin1, kin2) %>%
  plot_kinetics(theoretical = FALSE, 
                relative = FALSE)

Plot position frequency

Description

Plots the frequency of coverage of protein sequence.

Usage

plot_position_frequency(
  dat,
  protein = dat[["Protein"]][1],
  chosen_state = dat[["State"]][1]
)
plot_position_frequency(
  dat,
  protein = dat[["Protein"]][1],
  chosen_state = dat[["State"]][1]
)

Arguments

`dat`	data as imported by the `read_hdx` function
`protein`	protein to be included in plot
`chosen_state`	sequence states to be included in plot

Details

The function plot_position_frequency plots a histogram of the coverage frequency based on experimental data. The aim of this plot is to see how many times each position of the sequence was covered (by different peptides).

Value

a ggplot object.

Examples

# load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# function call with default parameters
plot_position_frequency(dat)

# function call with explicit parameters
plot_position_frequency(dat, chosen_state = "CD160_HVEM")

# load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# function call with default parameters
plot_position_frequency(dat)

# function call with explicit parameters
plot_position_frequency(dat, chosen_state = "CD160_HVEM")

Calculate data

Description

Calculates values for visualization from input data file - both experimental and theoretical. All parameters are needed.

Usage

prepare_dataset(
  dat,
  in_state_first,
  chosen_state_first,
  out_state_first,
  in_state_second,
  chosen_state_second,
  out_state_second
)
prepare_dataset(
  dat,
  in_state_first,
  chosen_state_first,
  out_state_first,
  in_state_second,
  chosen_state_second,
  out_state_second
)

Arguments

`dat`	data frame with data from DynamX file
`in_state_first`	string in form "state_time" for first state in in time
`chosen_state_first`	string in form "state_time" for chosen state in in time
`out_state_first`	string in form "state_time" for first state in out time
`in_state_second`	string in form "state_time" for second state in in time
`chosen_state_second`	string in form "state_time" for second state in chosen time
`out_state_second`	string in form "state_time" for second state in out time

Details

...

This is the first version - multi-state calculations are not supported.

Value

data frame with calculated values

Examples

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

# prepare dataset for states `CD160` and `CD160_HVEM` in given time parameters
prepare_dataset(dat,
                in_state_first = "CD160_0.001",
                chosen_state_first = "CD160_1",
                out_state_first = "CD160_1440",
                in_state_second = "CD160_HVEM_0.001",
                chosen_state_second = "CD160_HVEM_1",
                out_state_second = "CD160_HVEM_1440") 

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

# prepare dataset for states `CD160` and `CD160_HVEM` in given time parameters
prepare_dataset(dat,
                in_state_first = "CD160_0.001",
                chosen_state_first = "CD160_1",
                out_state_first = "CD160_1440",
                in_state_second = "CD160_HVEM_0.001",
                chosen_state_second = "CD160_HVEM_1",
                out_state_second = "CD160_HVEM_1440")

Experiment quality control

Description

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

Usage

quality_control(dat, state_first, state_second, chosen_time, in_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

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.

Value

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

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))    
                           
# 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")

# 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))    
                           
# 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")

Read HDX-MS data file

Description

Imports data from a HDX-MS file and validates its content.

Usage

read_hdx(filename)
read_hdx(filename)

Arguments

filename

a file supplied by the user. Formats allowed: .csv, .xlsx and .xls.

Details

First version accepts files produced by DynamX 3.0 and 2.0 in 'cluster data' format. The function checks if all necessary columns are provided in correct format. The file must include at least two repetitions of the measurement for the uncertainty to be calculated.

Value

dat - a data.frame with validated content.

Examples

# read example data
head(read_hdx(system.file(package = "HaDeX", 
                     "HaDeX/data/KD_180110_CD160_HVEM.csv")))

# read example data
head(read_hdx(system.file(package = "HaDeX", 
                     "HaDeX/data/KD_180110_CD160_HVEM.csv")))

Reconstruct protein sequence

Description

Reconstructs protein sequence from supplied file.

Usage

reconstruct_sequence(dat, protein = dat[["Protein"]][1])
reconstruct_sequence(dat, protein = dat[["Protein"]][1])

Arguments

`dat`	data read by `read_hdx`
`protein`	the protein of which the structure is to be reconstructed

Details

The function reconstructs protein sequence from supplied experimental data. If a position is not covered, x is shown. First version doesn't support manual sequence length correction.

Value

reconstructed sequence - character object.

Examples

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

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

Plot Woods' plot

Description

Produces Woods' plot based on theoretical or experimental HDX-MS data.

Usage

woods_plot(
  calc_dat,
  theoretical = FALSE,
  relative = TRUE,
  confidence_limit = 0.98,
  confidence_limit_2 = 0.99
)
woods_plot(
  calc_dat,
  theoretical = FALSE,
  relative = TRUE,
  confidence_limit = 0.98,
  confidence_limit_2 = 0.99
)

Arguments

`calc_dat`	data as imported by the `read_hdx` function and processed by the `prepare_dataset` function.
`theoretical`	`logical`, determines if plot shows theoretical values.
`relative`	`logical`, determines if values are relative or absolute.
`confidence_limit`	confidence limit.
`confidence_limit_2`	confidence limit 2.

Details

...

This is the first version - multi-state calculations are not supported.

Value

a ggplot object.

References

Woods, V.L., and Hamuro, Y. (2001). High resolution, high-throughput amide deuterium exchange-mass spectrometry (DXMS) determination of protein binding site structure and dynamics: utility in pharmaceutical design. J. Cell. Biochem. Suppl. Suppl 37, 89–98.

Examples

# load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# prepare dataset for states `CD160` and `CD160_HVEM` 
# in given time parameters                           
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440") 
                            
# plot Woods plot - theoretical & relative                             
woods_plot(calc_dat = calc_dat,
           theoretical = TRUE,
           relative = TRUE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)
           
# plot Woods plot - experimental  & relative                
woods_plot(calc_dat = calc_dat,
           theoretical = FALSE,
           relative = TRUE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)

# plot Woods plot - theoretical & absolute    
woods_plot(calc_dat = calc_dat,
           theoretical = TRUE,
           relative = FALSE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)

# plot Woods plot - experimental & absolute    
woods_plot(calc_dat = calc_dat,
           theoretical = FALSE,
           relative = FALSE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)
            
# load example data
dat <- read_hdx(system.file(package = "HaDeX", 
                            "HaDeX/data/KD_180110_CD160_HVEM.csv"))
                            
# prepare dataset for states `CD160` and `CD160_HVEM` 
# in given time parameters                           
calc_dat <- prepare_dataset(dat,
                            in_state_first = "CD160_0.001",
                            chosen_state_first = "CD160_1",
                            out_state_first = "CD160_1440",
                            in_state_second = "CD160_HVEM_0.001",
                            chosen_state_second = "CD160_HVEM_1",
                            out_state_second = "CD160_HVEM_1440") 
                            
# plot Woods plot - theoretical & relative                             
woods_plot(calc_dat = calc_dat,
           theoretical = TRUE,
           relative = TRUE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)
           
# plot Woods plot - experimental  & relative                
woods_plot(calc_dat = calc_dat,
           theoretical = FALSE,
           relative = TRUE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)

# plot Woods plot - theoretical & absolute    
woods_plot(calc_dat = calc_dat,
           theoretical = TRUE,
           relative = FALSE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)

# plot Woods plot - experimental & absolute    
woods_plot(calc_dat = calc_dat,
           theoretical = FALSE,
           relative = FALSE,
           confidence_limit = 0.98,
           confidence_limit_2 = 0.99)

Package 'HaDeX'

Help Index

HaDeX

Description

Author(s)

Calculates confidence limits

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate the value of confidence limit

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Calculate kinetic data

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate deuteration

Description

Usage

Arguments

Details

Value

See Also

Examples

Plot comparison plot

Description

Usage

Arguments

Details

Value

See Also

Examples

HaDeX Graphical User Interface

Description

Usage

Arguments

Warning

Plot peptide coverage

Description

Usage

Arguments

Details

Value

See Also

Examples

Plot kinetics data

Description

Usage

Arguments

Details

Value

See Also

Examples

Plot position frequency

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate data

Description

Usage

Arguments

Details