| Type: | Package |
| Title: | Analysis of Hydrogen-Deuterium Exchange Mass-Spectrometry Data |
| Version: | 0.0.2 |
| Author: | Maria K. Janowska 
[aut, cre],
Katherine Reiter [ctb],
Pearl Magala [ctb],
Miklos Guttman [ctb],
Rachel E. Klevit [ctb] |
| Maintainer: | Maria K. Janowska <mka.janowska@gmail.com> |
| Description: | A protocol that facilitates the processing and analysis of Hydrogen-Deuterium Exchange Mass Spectrometry data using p-value statistics and Critical Interval analysis. It provides a pipeline for analyzing data from 'HDXExaminer' (Sierra Analytics, Trajan Scientific), automating matching and comparison of protein states through Welch's T-test and the Critical Interval statistical framework. Additionally, it simplifies data export, generates 'PyMol' scripts, and ensures calculations meet publication standards. 'HDXBoxeR' assists in various aspects of hydrogen-deuterium exchange data analysis, including reprocessing data, calculating parameters, identifying significant peptides, generating plots, and facilitating comparison between protein states. For details check papers by Hageman and Weis (2019) <doi:10.1021/acs.analchem.9b01325> and Masson et al. (2019) <doi:10.1038/s41592-019-0459-y>. 'HDXBoxeR' citation: Janowska et al. (2024) <doi:10.1093/bioinformatics/btae479>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | dplyr, graphics, grDevices, RColorBrewer, stats, stringr, tidyr, utils, methods, wrapr |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr, rmarkdown |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2024-08-23 19:16:44 UTC; mkaja |
| Repository: | CRAN |
| Date/Publication: | 2024-08-24 07:30:10 UTC |
Global confidence interval treshold from experimental standard deviation for 2 samples.
Description
Calculation of global confidence interval using approach by: Reliable Identification of Significant Differences in Differential Hydrogen Exchange-Mass Spectrometry Measurements Using a Hybrid Significance Testing Approach Tyler S. Hageman and David D. Weis Analytical Chemistry 2019 91 (13), 8008-8016 DOI: 10.1021/acs.analchem.9b01325 calculations for alpha 0.99
Usage
CI_2pts(s1, s2, replicates = 3)
Arguments
s1 |
standard deviation from one sample |
s2 |
standard deviation from seconda sample |
replicates |
number of replicates. Default set to 3. |
Value
treshold for determining significance.
Examples
sd1<-data.frame(c(0.1, 0.12, 0.13, 0.09, 0.11, 0.10))
sd2<-data.frame(c(0.18, 0.11, 0.13, 0.08, 0.11, 0.06))
CI_2pts(s1=sd1, s2=sd2, replicates=3)
Global confidence interval treshold from experimental standard deviation for 1 sample
Description
Calculation of global confidence interval using approach by: Reliable Identification of Significant Differences in Differential Hydrogen Exchange-Mass Spectrometry Measurements Using a Hybrid Significance Testing Approach Tyler S. Hageman and David D. Weis Analytical Chemistry 2019 91 (13), 8008-8016 DOI: 10.1021/acs.analchem.9b01325 calculations for alpha 0.99
Usage
CI_single(s1, replicates = 3)
Arguments
s1 |
standard deviation from one sample |
replicates |
number of replicates. Default set to 3. |
Value
treshold for determining significance.
Examples
sd1<-data.frame(c(0.1, 0.12, 0.13, 0.09, 0.11, 0.10))
CI_single(s1=sd1, replicates=3)
Critial interval calculation two sets of timecourses
Description
Preparatory function for calculation of pvalue between sets.
Usage
CI_tc(sd_c, sd_v, replicates = 3, pv_cutoff = 0.01)
Arguments
sd_c |
dataframe of control |
sd_v |
dataframe for variant |
replicates |
number of replicates. Default set to 3. |
pv_cutoff |
pvalue cutoff. Default set to 0.01 |
Value
Critical interval for 2 sets
Global confidence interval treshold from experimental standard deviation
Description
Calculation of global confidence interval using approach by for all protein states compared to first state in the data.frame. Reliable Identification of Significant Differences in Differential Hydrogen Exchange-Mass Spectrometry Measurements Using a Hybrid Significance Testing Approach Tyler S. Hageman and David D. Weis Analytical Chemistry 2019 91 (13), 8008-8016 DOI: 10.1021/acs.analchem.9b01325
Usage
CI_tp(df, replicates = 3, alpha = 0.01)
Arguments
df |
standard deviation dataframe. |
replicates |
number of replicates. Default set to 3. |
alpha |
significance level. Set as default to 0.01 |
Value
treshold for determining significance.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(file_nm, seq_match=FALSE)
sd<-sd_timepoint(df=a, replicates=3)
CI_tp(df=sd, replicates=3, alpha=0.01 )
CI_tp(sd)
Returns full summary table.
Description
Returns summary data. Function returns: Protein states, timepoints, number of replicates, # peptides, % coveregae, average peptide length and redundancy. backexchange calculations (average and range), Critical interval and standard deviation. Function requires undeuterated and Fully deuterated sets marked in Deut.time as 0s and FD respectively.
Usage
all_summary(filepath, replicates = 3, Dfact = 0.85)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
replicates |
number of replicates. Default set to 3. |
Dfact |
Dfact is the fraction of D/H in the labeling buffer used. Default set up to 0.85 |
Value
Returns summary table.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- all_summary(file_nm, replicates=3, Dfact=0.85)
Returns initially processed data.frame from the export from the HDXExaminer
Description
Function used as internal function
Usage
arg_UN_FD(filepath)
Arguments
filepath |
input file location |
Value
Data.frame for further processing
Returns initially processed data.frame from the export from the HDXExaminer
Description
Function used as internal function
Usage
arg_df(filepath)
Arguments
filepath |
input file location |
Value
Data.frame for further processing
Returns default arguments for the output_tp functions. States
Description
Function used as internal function
Usage
arguments_call1(filepath)
Arguments
filepath |
input file location |
Value
The default arguments to output_tp functions.
Returns default arguments for the output_tp functions. Deut.Time
Description
Function used as internal function
Usage
arguments_call2(filepath, states)
Arguments
filepath |
input file location |
states |
states used |
Value
The default arguments to output_tp functions.
Returns default arguments for the output_tp functions. # replicates
Description
Function used as internal function
Usage
arguments_call3(filepath, states, times)
Arguments
filepath |
input file location |
states |
states used |
times |
deuteration times |
Value
The default arguments to output_tp functions.
Preparatory function for average plot for timecourses
Description
Returns plots with average deuteration at each peptide.
Usage
av_tc(df, cola)
Arguments
df |
output from functions output_tp or output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
plots of averages
Preparatory function for average plot
Description
Returns plots with average deuteration at each peptide.
Usage
av_tp(df, cola)
Arguments
df |
output from functions output_tp or output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
plots of averages
Returns average value for either uptake of procent data.
Description
Calculates average of uptake or procent data. Returns data frame with average values. Default for the number of replicates is 3.
Usage
ave_timepoint(df, replicates = 3)
Arguments
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates used. Default is set to replicates=3 |
Value
Data.frame with average values
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
ave<-ave_timepoint(df=a) ##if number of replicates is equal 3
ave<-ave_timepoint(df=a, replicates=4) ##if number of replicates is equal 4
Calculates average for time course data.
Description
Calculates average for time course data.
Usage
average_timecourse(filepath)
Arguments
filepath |
filepath to the All_results input file. |
Value
data frame with average deuteration uptake data.
Summary of backexchange summary
Description
Returns average and ranges of backexchange. Function calculates as: 1- (m100%-m0%)/N/Dfact. m0% is the non-deuterated peptide centroid mass, m100% is the maximally labeled peptide centroid mass, N is the theoretical number of backbone amides in the peptide and Dfrac is the fraction of D/H in the labeling buffer used. Function requires undeuterated and Fully deuterated sets marked in Deut.time as 0s and FD respectively.
Usage
backHX_calculations(filepath, Dfact = 0.85)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Dfact |
is the fraction of D/H in the labeling buffer used. Default set up to 0.85 |
Value
Returns summary table for backexchange.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- backHX_calculations(filepath=file_nm, Dfact=0.85)
Plots boxplots for all the averages in the set
Description
Returns boxplots to compare sets between each other
Usage
boxplot_tp(df, replicates = 3, ...)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
replicates |
number of replicates in sample. Default set to 3. |
... |
inherited boxplot parameters |
Value
boxplots for average deuterium uptake per set.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
boxplot_tp(df=a, replicates=3)
Returns color pallete from red to blue with number of colors for defined ranges
Description
Returns color pallete from red to blue with number of colors for defined ranges
Usage
color_ranges_Blue_Red_heat_map(ranges, colors_initial)
Arguments
ranges |
vector of numbers. Should have the same mumber of positive and negative values and contain 0. |
colors_initial |
additional color that should be first in the pallette. |
Value
color scheme for number
Examples
color_ranges_Blue_Red_heat_map(ranges=c(-Inf, -100, -50, 0, 50, 100, Inf), colors_initial="white")
Returns Spectral pallette with colors matching defined ranges
Description
Spectral pallette for timecourse data
Usage
color_ranges_Spectral(ranges, colors_initial)
Arguments
ranges |
vector of numbers. Should have the same mumber of positive and negative values and contain 0. |
colors_initial |
additional color that should be first in the pallette. |
Value
color scheme for number
Examples
color_ranges_Spectral(ranges=c(-Inf, -100, -50, 0, 50, 100, Inf), colors_initial="white")
Returns coverage per residue
Description
returns vector with coverage information
Usage
coverage_residue(df1, start_col, end_col)
Arguments
df1 |
output from functions output_tp or output_tp_proc. |
start_col |
number of "Start" column in data.frame |
end_col |
number of "Start" column in data.frame |
Value
vector with coverage per residue
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
coverage_residue(df1=a,start_col=2, end_col=3 )
Return woods plots for the timecourse
Description
All the peptides are plotted based on their uptake.
Usage
deuteration_woods_timecourse(
input_data,
states,
replicates = 3,
ylim = c(0, 120),
...
)
Arguments
input_data |
output from function output_tc(..., percent=TRUE) |
states |
states, if missing all states used |
replicates |
replicates |
ylim |
y axis limits |
... |
other parameters |
Value
Woods plots for the timecourse
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(file_nm, percent=TRUE)
deuteration_woods_timecourse(a)
Return woods plots for the timepoints
Description
All the peptides are plotted based on their uptake.
Usage
deuteration_woods_timepoints(
input_data,
times,
replicates = 3,
cola = NA,
ylim = c(0, 120),
...
)
Arguments
input_data |
output from function output_tp(..., percent=TRUE) |
times |
Deuteration times, if missing all deuteration times used |
replicates |
replicates |
cola |
colors, default NA |
ylim |
y axis limits |
... |
other parameters |
Value
Woods plots for the timepoints
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm, percent=TRUE)
deuteration_woods_timepoints(a[1:12,])
Returns data frame with difference of averages between State1 and other states provided.
Description
Returns average difference data.frame. Sets are compared to the first state in the input file. If other order of the sets is required use Default for the number of replicates is 3.
Usage
dif_ave(df)
Arguments
df |
output from functions output_tp, output_tp_proc, output_tp_states or output_tp_proc_states. |
Value
Data.frame with difference values btw control and other protein states.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
pv<-pv_timepoint(df=a) ##if number of replicates is equal 3
pv1<-pv_timepoint(df=a, replicates=3) ##if number of replicates is equal 4
#b<-output_tp_states(file_nm, states=c("4EHP", "State2", "State3" ))
#pv_states<-pv_timepoint(df=b) ### here means of State4, will be compared to State2 and State4
Preparatory function for difference plot
Description
Returns plots with difference deuteration at each peptide.
Usage
dif_tp(df, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
plots of difference in average
Preparatory function for difference plot
Description
Returns plots with difference deuteration at each peptide.
Usage
dif_tp_proc(df, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
plots of difference in average
Duplicate set function
Description
Internal function
Usage
duplicate_sets(df)
Arguments
df |
dataframe |
Value
duplicate sets
Makes input for Extreme for bimodal analysis.
Description
Makes input for Extreme for bimodal analysis.
Usage
extreme_input_gap(hm_dir, replicates, timepoints, output_path = "NA")
Arguments
hm_dir |
directory in which all the folders which needs to be processed are |
replicates |
number of replicates in sample |
timepoints |
lists timepoints used in experiments. |
output_path |
directory where the output files will be saved, hm_dir default |
Value
Inputs for extreme for all data prepared.
Examples
path_to_folders<-system.file("extdata", package = "HDXBoxeR")
extreme_input_gap(hm_dir =path_to_folders, replicates = 3,
timepoints =c(3, 60, 1800, 72000), output_path=tempdir())
Makes input for Extreme for bimodal analysis.
Description
If data is missing it returns non-deuterated data in these columns.
Usage
extreme_input_undeut(hm_dir, replicates, timepoints, output_path = "NA")
Arguments
hm_dir |
directory in which all the folders which needs to be processed are |
replicates |
number of replicates in sample |
timepoints |
lists timepoints used in experiments. |
output_path |
directory where output should be written |
Value
Inputs for extreme for all data prepared.
Examples
path_to_folders<-system.file("extdata", package = "HDXBoxeR")
extreme_input_undeut(hm_dir=path_to_folders, replicates = 3,
timepoints =c(3, 60, 1800, 72000), output_path=tempdir())
Provides summary table for all data.sets.
Description
Returns data frame sumamrizing general information about the data sets. Function returns: Protein states, timepoints, number of replicates, # peptides, % coveregae, average peptide length and redundancy.
Usage
general_info(filepath)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Value
Returns summary table.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- general_info(file_nm)
function from plotfunctions package
Description
Margin coordinates
Usage
getCoords1(pos = 1.1, side = 1, input = "p")
Arguments
pos |
position |
side |
side of plot |
input |
plot or figure position |
Value
coordinates of margins
Plots heat maps for time courses.
Description
Returns heat map on timecourses with raw data.
Usage
heat_map_tc(df, ranges = c(seq(0, 100, by = 10), Inf))
Arguments
df |
timecourse input |
ranges |
ranges for coloring scheme. Default set to c(seq(0, 100, by=10), Inf) |
Value
heat map for timecourses
Preparatory function for heat map
Description
Returns heat map
Usage
heat_map_tp(
df,
pv,
sd,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
pv |
pvalues dataframes calculated using pv_timepoint() function |
sd |
standard deviation data.frame generated using sd_timepoint function |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
heat map for timepoints
Preparatory function for heat map of maximum uptake per residue.
Description
Returns heat map
Usage
heat_map_tp_maxuptake(
df,
pv,
sd,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
pv |
pvalues dataframes calculated using pv_timepoint() function |
sd |
standard deviation data.frame generated using sd_timepoint function |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
maxiumum uptake heat map for timepoints
Preparatory function for heat map of maximum procent deuteration per residue.
Description
Returns heat map
Usage
heat_map_tp_maxuptake_proc(
df,
dfup,
pv,
sd,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame for procent deuteration. Generated using ave_timepoint() function. |
dfup |
average data frame for deuteration uptake. Generated using ave_timepoint() function. |
pv |
pvalues dataframes calculated using pv_timepoint() function |
sd |
standard deviation data.frame generated using sd_timepoint function |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
Maximum uptake heat map for timepoints
Preparatory function for heat map for procent deuteration
Description
Returns heat map
Usage
heat_map_tp_proc(
df,
dfup,
pv,
sd,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame for procent deuteration. Generated using ave_timepoint() function. |
dfup |
average data frame for deuteration uptake. Generated using ave_timepoint() function. |
pv |
pvalues dataframes calculated using pv_timepoint() function |
sd |
standard deviation data.frame generated using sd_timepoint function |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
heat map for timepoints
Checks for NaN is data.frame
Description
Function by Hong Ooi; https://stackoverflow.com/questions/18142117/how-to-replace-nan-value-with-zero-in-a-huge-data-frame
Usage
## S3 method for class 'data.frame'
is.nan(x)
Arguments
x |
Data frame to be checked for NaN |
Value
logical. Returns info if data.frame contains NaNs.
Examples
## this function will overwrite the is.nan function that works only on vectors and matrices
df<-data.frame(c(0,NaN), c(1, 2))
is.nan(df)
df[is.nan(df)]<- 0
Legend for difference in averages plot.
Description
Returns legend for difference in average plots. Preparatory function.
Usage
lab_dif(df, cola)
Arguments
df |
output from functions average difference |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
legend for difference in average plot for time points
Preparatory function for difference plot for procent deuteration
Description
Returns legends for plots procent deuteration at each peptide.
Usage
lab_dif_proc(df, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from RColorBrewer is used. |
Value
legends for procent deuteration plots
Preparatory function for volcano plot legends
Description
Returns volcano plots
Usage
lab_vol(df, cola)
Arguments
df |
output from functions output_tp |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
legends for volcano plots
Legend for the heatmaps prep function.
Description
Returns names for legend for the heatmaps
Usage
legend_heat_map(ranges = c(-Inf, seq(-30, 30, by = 10), Inf))
Arguments
ranges |
ranges that are to be colored in the legend. Default ranges=c(-Inf,seq(-30, 30, by=10), Inf ) |
Value
legend for the heatmap
Legend for the heatmaps for timecourses.
Description
Returns names for legend for the heatmaps. Extracts names from data.frame
Usage
legend_heat_map_tc(df)
Arguments
df |
generated using output_tcourse() |
Value
legend for the heatmap
Legend for the heatmaps prep function for timecourses.
Description
Returns names for legend for the heatmaps
Usage
legend_heat_map_timecourse(ranges = c(-Inf, seq(0, 100, by = 10), Inf))
Arguments
ranges |
ranges that are to be colored in the legend. Default ranges=c(-Inf,seq(-30, 30, by=10), Inf ) |
Value
legend for the heatmap
Legend for the heatmaps.Extracts names from data.frame
Description
Returns names for legend for the heatmaps
Usage
legend_heat_map_tp(df)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
Value
legend for the heatmap
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
legend_heat_map_tp(df=a)
Legend for the heatmaps percent.Extracts names from data.frame
Description
Returns names for legend for the heatmaps
Usage
legend_heat_map_tp_proc(df)
Arguments
df |
average data frame. |
Value
legend for the heatmap prercent
Legend, bottom of the plots
Description
Internal function
Usage
legend_nm_bottom(names, cols)
Arguments
names |
labels |
cols |
colors |
Value
legend at the bottom of the plot
Legend for average plot.
Description
Returns legend with average plots. Preparatory function.
Usage
legend_raw_ave(df, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
legend for average plot for time points
Preparatory function to draw legends for average procent
Description
Returns legend with average procent deuteration at each peptide.
Usage
legend_raw_ave_proc(df, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
legend for average deuteration procent for timepoints
Legend for average deuteration plot for timecourse.
Description
Returns legend with average plots. Preparatory function.
Usage
legend_raw_ave_tc(df, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
legend for average plot for time course
Legend for the significant peptides
Description
Returns names for legend for the significant peptides plots.
Usage
legend_sig_peptides(ranges = c(-Inf, seq(-30, 30, by = 10), Inf))
Arguments
ranges |
ranges that are to be colored in the legend. Default ranges=c(-Inf,seq(-30, 30, by=10), Inf ) |
Value
legend for the heatmap
Legend, bottom of the plots
Description
Internal function
Usage
legend_states_PerD_bottom(df, cols)
Arguments
df |
dataframe |
cols |
colors |
Value
legend at the bottom of the plot
Preparatory function returns legends for the timecourses.
Description
Preparatory function
Usage
legend_tc_bottom(df, cols)
Arguments
df |
data frame from which names will be extracted |
cols |
colors to be used in legend |
Value
legend at the bottom of the plot
Number of exchangeable protons
Description
Provides a vector with number of exchangeable protons, calculated from the input table. Number of protons calculated as peptide_length - 2 - number of Prolines in the peptide that are not in the first position
Usage
nb_exch_deut(df)
Arguments
df |
standard deviation from one sample |
Value
vector with number of exchangeable protons
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
nb_exch_deut(a)
Lists names of states in data sets
Description
Returns vector with name of states used for choosing states for input functions generation.
Usage
nm_states(filepath)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Value
list of Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
names_states<- nm_states(file_nm)
Prepares output for HDX-MS Full deuteration data
Description
Returns a data frame for Full deuteration set
Usage
output_FD(filepath)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Value
data frame with reorganized data where in columns is uptake data for Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<-output_FD(file_nm)
Prepares output for HDX-MS Full deuteration data for procent deuteration.
Description
Returns a data frame for Full deuteration set
Usage
output_FD_proc(filepath)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Value
data frame with reorganized data where in columns is procent deuteration for Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_FD_proc(file_nm)
Prepares output for HDX-MS Undeuterated sample data.
Description
Returns a data frame for Full deuteration set
Usage
output_UD(filepath)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Value
data frame with reorganized data where in columns is uptake data for Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_UD(file_nm)
Prepares output for HDX-MS Undeuterated data for procent deuteration.
Description
Returns a data frame for Undeuterated control set
Usage
output_UD_proc(filepath)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Value
data frame with reorganized data where in columns is procent deuteration for Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_UD_proc(file_nm)
Prepares output with HDX-MS data for publications
Description
Format prepared based of example from: Masson, G.R., Burke, J.E., Ahn, N.G. et al. Recommendations for performing, interpreting and reporting hydrogen deuterium exchange mass spectrometry (HDX-MS) experiments. Nat Methods 16, 595–602 (2019). https://doi.org/10.1038/s41592-019-0459-y It generates csv file in format ready for publication of the data.
Usage
output_prep(filepath, output_name, states, replicates, times, percent = FALSE)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
output_name |
Name of output file. It has to be csv file |
states |
function allows to choose what states should be used for analysis. Default all states are used. |
replicates |
number of replicates to be used in analysis. The function takes number of replicates up to specified number. If no argument provided number maximal common number of replicates it used. |
times |
lists the deuteration times to be used in analysis. Default all states used. |
percent |
return either uptake or percent deuteration, default=FALSE, return uptake |
Value
Returns&saves data.frame in format that is accepted for the publications.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
output_prep(filepath=file_nm, output_name=tempfile())
Prepares output for HDX-MS for the deuteration uptake or percent deuteration for the time courses.
Description
Returns a data frame organized for additional analysis. In columns are deuteration uptake or percent deuteration data for the given protein states. Function allows for writing csv with data, matching sequences of peptide. Protein.States, Deut.times, or number of replicates can be specified.
Usage
output_tc(
filepath,
replicates,
states,
times,
seq_match = FALSE,
csv = "NA",
percent = FALSE
)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
replicates |
number of replicates to be used in analysis. The function takes number of replicates up to specified number. If no argument provided number maximal common number of replicates it used. |
states |
function allows to choose what states should be used for analysis. Default all states are used. |
times |
lists the deuteration times to be used in analysis. Default all states used. |
seq_match |
Flag allows to choose if the peptide sequences should be matched between states. seq_match=FALSE signifies no sequence matching, seq_match=TRUE states that the sequences are matched between the sets. |
csv |
Flag allowing saving the output as csv. With default csv="NA", data is not saved. If csv output is desided, provide output name. |
percent |
Flag allowing to choose output as deteuration uptake (FALSE) or percent deuteration (TRUE). Default deuteration uptake. |
Value
data frame with reorganized data where in columns is the deuteration uptake for Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(filepath=file_nm) ###all default parameters used
# all possible flags listed & percent deuteration output,
#with sequences matching for protein states.
a<-output_tc(filepath=file_nm, replicates=3, states=c("bound", "Unbound"),
times=c("3.00s", "72000.00s"), seq_match=TRUE, csv="NA", percent=TRUE)
Prepares output for HDX-MS for the deuteration uptake or percent deuteration for the time points.
Description
Returns a data frame organized for additional analysis. In columns are deuteration uptake or percent deuteration data for the given protein states. Function allows for writing csv with data, matching sequences of peptide. Protein.States, Deut.times, or number of replicates can be specified.
Usage
output_tp(
filepath,
replicates,
states,
times,
seq_match = FALSE,
csv = "NA",
percent = FALSE
)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
replicates |
number of replicates to be used in analysis. The function takes number of replicates up to specified number. If no argument provided number maximal common number of replicates it used. |
states |
function allows to choose what states should be used for analysis. Default all states are used. |
times |
lists the deuteration times to be used in analysis. Default all states used. |
seq_match |
Flag allows to choose if the peptide sequences should be matched between states. seq_match=FALSE signifies no sequence matching, seq_match=T states that the sequences are matched between the sets. |
csv |
Flag allowing saving the output as csv. With default csv="NA", data is not saved. If csv output is desided, provide output name. |
percent |
Flag allowing to choose output as deteuration uptake (FALSE) or percent deuteration (TRUE). Default deuteration uptake. |
Value
data frame with reorganized data where in columns is the deuteration uptake for Protein States.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(filepath=file_nm) ###all default parameters used
# all possible flags listed & percent deuteration output,
# with sequences matching for protein states.
a<-output_tp(filepath=file_nm, replicates=3, states=c("bound", "Unbound"),
times=c("3.00s", "72000.00s"), seq_match=TRUE, csv="NA", percent=TRUE)
Color scheme using heatmap. Legend Extracts names from data.frame
Description
Returns names for legend for the heatmaps
Usage
pallette_legend(col_pallette)
Arguments
col_pallette |
pallette to be used in the heat map |
Value
legend for the heatmap
Color scheme using heatmap. Legend extracts names from data frame
Description
Returns names for legend for the heatmaps
Usage
pallette_ll(pallette, lab)
Arguments
pallette |
pallette to be used in the heat map |
lab |
labels to be used in pallette |
Value
legend for the heatmap
Preparatory function for significant peptide plots
Description
Returns plot where significant peptides are colored in blue-red scheme.
Usage
peptide_pv_tp(
df,
pv,
sd,
nb_row,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
pv |
pvalues dataframes calculated using pv_timepoint() function |
sd |
standard deviation data.frame generated using sd_timepoint function |
nb_row |
number of peptides in each row. Plotting parameter. |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
plot with peptides which are significantly different between sets.
Preparatory function for showing peptides with significant differences between sets.
Description
Returns plot where significantly different peptides are colored in blue-red scheme.
Usage
peptide_pv_tp_proc(
df,
dfup,
pv,
sd,
nb_row = 100,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame for procent deuteration. Generated using ave_timepoint() function. |
dfup |
average data frame for deuteration uptake. Generated using ave_timepoint() function. |
pv |
pvalues dataframes calculated using pv_timepoint() function |
sd |
standard deviation data.frame generated using sd_timepoint function |
nb_row |
number of peptides in each row. Plotting parameter. |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
plot with peptides which are significantly different between sets.
Prepares the plot window for the woods functions
Description
Internal function
Usage
pl_gen_ch2(df, ddlab = 1, ...)
Arguments
df |
dataframe |
ddlab |
label |
... |
other |
Value
Plot window
Prepares the plot window for the woods functions
Description
Internal function
Usage
pl_gen_uptake(df, timepoints, ddlab = 1, ...)
Arguments
df |
dataframe |
timepoints |
deuteration times used |
ddlab |
label |
... |
other |
Value
Plot window
Plots heat maps for maximum uptake per residue.
Description
Returns heat map with maximum uptake per residue.
Usage
plot_heat_map_max_uptake_tp(
df,
replicates = 3,
mar_x = 3.5,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01
)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
replicates |
number of replicates in sample. Default set to 3. |
mar_x |
margin x width. Default=3.5 |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
Value
heat map for maximum uptake per residue
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
plot_heat_map_max_uptake_tp(df=a, replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf) )
plot_heat_map_max_uptake_tp(df=a)
Plots heat maps for maximum procent deuteration per residue.
Description
Returns heat map with maximum precent_deuteration per residue.
Usage
plot_heat_map_max_uptake_tp_proc(
input_proc,
input_up,
mar_x = 3.5,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
input_proc |
Dataframe with organized procent deuteration data. Input generated using output_tp_proc() function. |
input_up |
Dataframe with organized deuteration uptake. Input generated using output_tp() function. |
mar_x |
margin x width. Default=3.5 |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
heat map for average uptake per residue for significant peptides.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a_up<- output_tp(file_nm)
a_proc<- output_tp(file_nm, percent=TRUE)
plot_heat_map_max_uptake_tp_proc(input_proc=a_proc, input_up=a_up, replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf) )
plot_heat_map_max_uptake_tp_proc(input_proc=a_proc, input_up=a_up)
Plots heat maps for time courses.
Description
Returns heat map on timecourses with raw data.
Usage
plot_heat_map_tc(
df,
replicates = 3,
mar_x = 3.5,
ranges = c(-Inf, seq(0, 100, by = 10), Inf)
)
Arguments
df |
output from function output_tcourse |
replicates |
number of replicates in sample. Default set to 3. |
mar_x |
margin x width. Default=3.5 |
ranges |
ranges for coloring scheme. Default set to c(seq(0, 100, by=10), Inf) |
Value
heat map for time courses
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(file_nm)
plot_heat_map_tc(df=a, replicates=3, ranges=c(seq(0, 100, by=5), Inf))
plot_heat_map_tc(df=a)
Plots heat maps for significant peptides.
Description
Returns heat map with average values for significant uptake per residue.
Usage
plot_heat_map_tp(
df,
mar_x = 3.5,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
mar_x |
margin x width. Default=3.5 |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
heat map for average uptake per residue for significant peptides.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
plot_heat_map_tp(df=a, replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf) )
plot_heat_map_tp(df=a)
Plots heat maps for significant peptides.
Description
Returns heat map with average values for significant uptake per residue.
Usage
plot_heat_map_tp_proc(
input_proc,
input_up,
mar_x = 3.5,
ranges = c(-Inf, -3, -2, -1, 0, 1, 2, 3, Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
input_proc |
Dataframe with organized procent deuteration data. Input generated using output_tp_proc() function. |
input_up |
Dataframe with organized deuteration uptake. Input generated using output_tp() function. |
mar_x |
margin x width. Default=3.5 |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
heat map for average uptake per residue for significant peptides.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a_up<- output_tp(file_nm)
a_proc<- output_tp(file_nm, percent=TRUE)
plot_heat_map_tp_proc(input_proc=a_proc, input_up=a_up, replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf) )
plot_heat_map_tp_proc(input_proc=a_proc, input_up=a_up)
Significant peptide plots.
Description
Returns plot where significant peptides are colored in blue-red scheme.
Usage
plot_peptide_sig_tp(
df1,
replicates = 3,
nb_pep_row = 100,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01
)
Arguments
df1 |
average data frame. Generated using ave_timepoint() function. |
replicates |
number of replicates in sample. Default set to 3. |
nb_pep_row |
number of peptides in each row. Plotting parameter. Default set to 100. |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
Value
plot with peptides which are significantly different between sets.
Draws peptides with significant difefrences between sets.
Description
Returns plot where significant peptides are colored in blue-red scheme.
Usage
plot_peptide_sig_tp_proc(
input_proc,
input_up,
nb_pep_row = 100,
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
input_proc |
Dataframe with organized procent deuteration data. Input generated using output_tp_proc() function. |
input_up |
Dataframe with organized deuteration uptake. Input generated using output_tp() function. |
nb_pep_row |
number of peptides in each row. Plotting parameter. Default set to 100. |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
plot with peptides which are significantly different between sets.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a_up<- output_tp(file_nm)
a_proc<- output_tp(file_nm, percent=TRUE)
plot_peptide_sig_tp_proc(input_proc=a_proc, input_up=a_up, replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf), nb_pep_row=40 )
Generates average deuteration plot for the time-course.
Description
Returns plots with average deuteration at each peptide.
Usage
plots_av_tcourse(df, replicates = 3, cola)
Arguments
df |
output from functions output_tcourse or output_tcourse_proc. |
replicates |
number of replicates in set as default set to 3. |
cola |
color pallette for different Protein States. As default Paired pallette from RColorBrewer is used. |
Value
average deuteration plots
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(file_nm)
plots_av_tcourse(df=a, replicates=3, cola=c(1:4))
plots_av_tcourse(df=a)
Returns average deuteration plot for timepoints in the data frame
Description
Returns plots with average deuteration at each peptide.
Usage
plots_av_tp(df, replicates = 3, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates in set as default set to 3. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
average deuteration plots
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
plots_av_tp(df=a, replicates=3, cola=c(1:4))
plots_av_tp(df=a)
Returns average procent deuteration plot for time points
Description
Returns plots with average procent deuteration at each peptide.
Usage
plots_av_tp_proc(df, replicates = 3, cola)
Arguments
df |
output from functions output_tp_proc. |
replicates |
number of replicates in set as default set to 3. |
cola |
color pallette for different Protein States. As default Paired pallette from RColorBrewer is used. |
Value
average deuteration plots
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm, percent=TRUE)
plots_av_tp_proc(df=a, replicates=3, cola=c(1:4))
plots_av_tp_proc(df=a)
Returns difference in average plot for timepoints in the data frame
Description
Returns plots with difference in avarage for each peptide.
Usage
plots_diff_tp(df, replicates = 3, cola)
Arguments
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates in set as default set to 3. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
plots of difference of averages
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
plots_diff_tp(df=a, replicates=3, cola=c(1:4))
plots_diff_tp(df=a)
Returns difference in average procent deuteration plot for timepoints in the data frame
Description
Returns plots with difference in procent deuteration for each peptide.
Usage
plots_diff_tp_proc(df, replicates = 3, cola)
Arguments
df |
output from functions output_tp_proc. |
replicates |
number of replicates in set as default set to 3. |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
plots of difference of average procent deuteration
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm, percent=TRUE)
plots_diff_tp_proc(df=a, replicates=3, cola=c(1:4))
plots_diff_tp_proc(df=a)
Returns volcano plots for timepoints in the data frame
Description
Returns volcano plots for each peptide. Critical interval is calculated according to #' Reliable Identification of Significant Differences in Differential Hydrogen Exchange-Mass Spectrometry Measurements Using a Hybrid Significance Testing Approach Tyler S. Hageman and David D. Weis Analytical Chemistry 2019 91 (13), 8008-8016 DOI: 10.1021/acs.analchem.9b01325 calculations for alpha 0.99 pvalues calculated using Welch t-test.
Usage
plots_vol_tp(df, replicates = 3, pv_cutoff = 0.01, cola)
Arguments
df |
output from functions output_tp |
replicates |
number of replicates in set as default set to 3. |
pv_cutoff |
p-value cutoff here set up to 0.01 |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
volcano plots
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
plots_vol_tp(df=a, replicates=3, cola=c(1:4), pv_cutoff=0.01 )
plots_vol_tp(df=a, pv_cutoff=0.05)
Preparation of figure window.
Description
Prepares a plotting window with specified margins with specific number of figure row and columns.
Usage
ppar(mfrow2)
Arguments
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
Value
modified par function with adjusted parameters
Examples
ppar(c(2,1))
Preparation of figure window. small margins
Description
Prepares a plotting window with specified margins with specific number of figure row and columns.
Usage
pparLM(mfrow2)
Arguments
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
Value
modified par function with adjusted parameters
Examples
pparLM(c(2,1))
Preparation of figure window with area for figure at the bottom.
Description
Prepares a plotting window with specified margins with specific number of figure row and columns.
Usage
ppar_bottom_legend(mfrow2)
Arguments
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
Value
modified par function with adjusted parameters
Examples
ppar_bottom_legend(c(2,3))
Preparation of figure window with more area on west side of plot.
Description
Prepares a plotting window with specified margins with specific number of figure row and columns.
Usage
ppar_wider(mfrow2)
Arguments
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
Value
default plotting window
Examples
ppar_wider(c(2,1))
Prepares function for plotting averages in timecourse
Description
Preparatory function
Usage
prep_timecourse_plot_ave(control_df, variant_df, replicates = 3)
Arguments
control_df |
dataframe of control |
variant_df |
dataframe for variant |
replicates |
number of replicates. Default set to 3. |
Value
dataframes with matched peptides in time course
Prepares function for Critical interval for timecourses
Description
Preparatory function
Usage
prep_timecourse_plot_sd(
control_df_up,
variant_df_up,
replicates = 3,
pv_cutoff = 0.01
)
Arguments
control_df_up |
dataframe of control |
variant_df_up |
dataframe for variant |
replicates |
number of replicates. Default set to 3. |
pv_cutoff |
cut off of pvalue used in calculation of critical interval. Default set to 0.01 |
Value
Critial interval for all sets
pvalue calculation between two sets of the data at certain timepoint
Description
Preparatory function for calculation of pvalue between sets.
Usage
pv_timecourse(df_c, df_v, replicates = 3)
Arguments
df_c |
dataframe of control |
df_v |
dataframe for variant |
replicates |
number of replicates. Default set to 3. |
Value
pvalue comparisons between two sets.
Calculation of pvalue between first protein state and any other state from all_states file
Description
Compares means of sets of uptake data and return dataframe with pvalues. Welch t.test is used for analysis. Sets are compared to the first state in the input file. If other order of the sets is required use Default for the number of replicates is 3.
Usage
pv_timepoint(df, replicates = 3)
Arguments
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates used. Default is set to replicates=3 |
Value
Data.frame with p-values
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
pv<-pv_timepoint(df=a) ##if number of replicates is equal 3
# pv1<-pv_timepoint(df=a, replicates=4) ##if number of replicates is equal 4
#b<-output_tp_states(file_nm, states=c("State4", "State2", "State3" ))
#pv_states<-pv_timepoint(df=b) ### here means of State4, will be compared to State2 and State4
Writes a text files with pymol scripts to list significant residues.
Description
Function write a script that can be used in pymol to color structure. Number of colors and corresponding to them ranges can be defined by user. Residues are being colored by average uptake values from the significant peptides per residues.
Usage
pymol_script_average_residue(
df,
path = "",
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
output from functions output_tp |
path |
output folder location |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
pymol script with residues colored based on average of uptake per residue.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
pymol_script_average_residue(df=a, replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf), path=tempdir() )
pymol_script_average_residue(df=a, path=tempdir())
Writes a text files with pymol scripts to list significant peptides
Description
Function write a script that can be used in pymol to color structure. Number of colors and corresponding to them ranges can be defined by user.
Usage
pymol_script_significant_peptide(
df,
path = "",
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3,
order.pep = TRUE
)
Arguments
df |
output from functions output_tp |
path |
location where the scripts will be saved |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
order.pep |
flag allowing to either order peptide acccording to the peptide length (default), or to position in the protein sequence. |
Value
pymol script with colors assigned per peptide
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
pymol_script_significant_peptide(df=a, replicates=3, path=tempdir(), pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf), order.pep=TRUE )
pymol_script_significant_peptide(df=a, path=tempdir())
Writes a text files with pymol scripts to list significant peptides
Description
Function write a script that can be used in pymol to color structure. Number of colors and corresponding to them ranges can be defined by user.
Usage
pymol_script_significant_peptide_proc(
input_proc,
input_up,
path = "",
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3,
order.pep = TRUE
)
Arguments
input_proc |
Dataframe with organized procent deuteration data. Input generated using output_tp(, percent=T) function. |
input_up |
Dataframe with organized deuteration uptake. Input generated using output_tp() function. |
path |
location where the Pymol scripts will be saved |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
order.pep |
flag allowing to either order peptide acccording to the peptide length (default), or to position in the protein sequence. |
Value
pymol script with colors assigned per peptide
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a_up<- output_tp(file_nm)
a_proc<- output_tp(file_nm, percent=TRUE)
pymol_script_significant_peptide_proc(input_proc=a_proc,
input_up=a_up, path=tempdir(),replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf), order.pep=TRUE)
Writes a text files with pymol scripts to list significant residues.
Description
Function write a script that can be used in pymol to color structure. Number of colors and corresponding to them ranges can be defined by user. Residues are being colored by maximum uptake from significant peptides per residues.
Usage
pymol_script_significant_residue(
df,
path = "",
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
df |
average data frame. Generated using ave_timepoint() function. |
path |
location where the Pymol scripts will be saved |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
pymol script with colors assigned per residues by maximum uptake per residue
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
pymol_script_significant_residue(df=a, path=tempdir(), replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf) )
pymol_script_significant_residue(df=a, path=tempdir())
Writes a text files with pymol scripts to list significant residues.
Description
Function write a script that can be used in pymol to color structure. Number of colors and corresponding to them ranges can be defined by user. Residues are colored by average procent_deuteration from the significant peptides per residues.
Usage
pymol_script_significant_residue_proc(
input_up,
input_proc,
path = "",
ranges = c(-Inf, seq(-30, 30, by = 10), Inf),
pv_cutoff = 0.01,
replicates = 3
)
Arguments
input_up |
Dataframe with organized deuteration uptake. Input generated using output_tp() function. |
input_proc |
Dataframe with organized procent deuteration data. Input generated using output_tp_proc() function. |
path |
location where the Pymol scripts will be saved |
ranges |
ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf) |
pv_cutoff |
p-value cutoff here set up to 0.01 |
replicates |
number of replicates in sample. Default set to 3. |
Value
pymol script with residues colored based on average of procent deuteration per residue.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a_up<- output_tp(file_nm)
a_proc<- output_tp(file_nm, percent=TRUE)
pymol_script_significant_residue_proc(input_proc=a_proc,
input_up=a_up, path=tempdir(), replicates=3, pv_cutoff=0.01,
ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf))
Preparatory function writing pymol scripts
Description
Function rearrange vector to string by adding + sign between the numbers.
Usage
pymol_str(ind1)
Arguments
ind1 |
vector of numbers (residues) |
Value
string with + as a separator.
Examples
res<-c(1,5, 19, 100, 109)
pymol_str(res)
Hidden function from qpcR package, typical usage as qpcR:::cbind.na
Description
Combine data of unequal row length avoiding repetition or errors by filling with NAs. In contrast to classical cbind, cbind.na can be used to combine data such as
Usage
qpcr.cbind.na(..., deparse.level = 1)
Arguments
... |
vectors |
deparse.level |
set to 1 as default |
Value
data frame with NA
Examples
qpcr.cbind.na(1:10, 1:3)
Gives ranges for the averages
Description
Function used as internal function to get ranges in the function.
Usage
ranges_function(df_ave, values_df)
Arguments
df_ave |
average per residues |
values_df |
data frame with values. |
Value
ranges per set
Gives ranges for the averages for time course analysis
Description
Function used as internal function to get ranges in the function.
Usage
ranges_function_tc(df_ave, values_df)
Arguments
df_ave |
average per residues |
values_df |
data frame with values. |
Value
ranges per set
bind non equal row
Description
kmezhoud/canceR: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC https://rdrr.io/github/kmezhoud/canceR/src/R/rbind.na.R
Usage
rbind_na(..., deparse.level = 1)
Arguments
... |
(generalized) vectors or matrices. |
deparse.level |
integer controlling the construction of labels in the case of non-matrix-like arguments (for the default method): deparse.level = 0 constructs no labels; the default, deparse.level = 1 or 2 constructs labels from the argument names. |
Value
a data frame with merged rows
Examples
row1 <- c("a","b","c","d")
row2 <- c("A", "B", "C")
row3 <- rbind_na(row1, row2)
Reset plotting window parameters to default
Description
function by Farid Cheraghi, https://stackoverflow.com/questions/9292563/reset-the-graphical-parameters-back-to-default-values-without-use-of-dev-off function resets plotting window parameters
Usage
reset_par()
Value
default plotting window parameters
Examples
reset_par()
Returns a robot plot for selected peptides for 2 protein states.
Description
Modification of butterfly plot. x axis residues. y axis % deuteration for one variant above the axis and for second peptide below the axis. Peptides are compared between the sets for the significance change between sets. If there is significant change beteween sets peptides are plotted for all timepoints. Significanty different timepoints for the peptides are colored. Peptides ranges are plotted as a line at corresponding % deuteration values.
Usage
robot_2states_indexes(
thP,
th,
indexes,
states,
replicates = 3,
pvalue = 0.01,
ylim,
xlim,
CI_factor = 1
)
Arguments
thP |
output of output_tcourse_proc() function. Raw data for procent deuteration for time courses |
th |
output of output_tcourse() function. Raw data for uptake deuteration for time courses |
indexes |
indexes of peptides to be drawn. |
states |
Need to choose only two protein states |
replicates |
number of replicates in sample. Default set to 3. |
pvalue |
p-value cutoff here set up to 0.01 |
ylim |
y-axis range |
xlim |
x-axis range. Set as default from max and minimum residues for the protein |
CI_factor |
Multiplication factor for Critical Interval. Allows for more restrictive selection of Critial interval. |
Value
Robot maps for timecourses for 2 protein states and selected indexes.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
tm_df<-output_tc(filepath=file_nm)
tmP_df<-output_tc(filepath=file_nm, percent=TRUE)
names_states<- nm_states(file_nm) ### returns states names
ind1<-robot_indexes(thP = tmP_df, th=tm_df, pvalue=0.001, CI_factor=3, states=names_states[1:2])
robot_2states_indexes(thP = tmP_df, th=tm_df,
states=names_states[1:2],indexes =ind1, pvalue=0.001, CI_factor=3)
Returns indexes for peptides with significant difference between two sets
Description
Function to help decide which peptides will be drawn on Robot plots.
Usage
robot_indexes(thP, th, replicates = 3, pvalue = 0.01, states, CI_factor = 1)
Arguments
thP |
output of output_tcourse_proc() function. Raw data for procent deuteration for time courses |
th |
output of output_tcourse() function. Raw data for uptake deuteration for time courses |
replicates |
number of replicates in sample. Default set to 3. |
pvalue |
p-value cutoff. Default set up to 0.01 |
states |
Protein states from the set. As default all states are chosen. |
CI_factor |
Multiplication factor for Critical Interval. Allows for more restrictive selection of Critial interval. |
Value
Returns indexes of significant peptides
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
tm_df<-output_tc(filepath=file_nm)
tmP_df<-output_tc(filepath=file_nm, percent=TRUE)
# more restictive peptide selection
robot_indexes(thP = tmP_df, th=tm_df, pvalue=0.01, CI_factor=1.5)
Returns dataframe with peptides which exhibit significant difference between two sets
Description
Function to help decide which peptides will be drawn on Robot plots.
Usage
robot_indexes_df(thP, th, replicates = 3, pvalue = 0.01, states, CI_factor = 1)
Arguments
thP |
output of output_tcourse_proc() function. Raw data for procent deuteration for time courses |
th |
output of output_tcourse() function. Raw data for uptake deuteration for time courses |
replicates |
number of replicates in sample. Default set to 3. |
pvalue |
p-value cutoff. Default set up to 0.01 |
states |
Protein states from the set. As default all states are chosen. |
CI_factor |
Multiplication factor for Critical Interval. Allows for more restrictive selection of Critial interval. |
Value
Returns dataframe listing peptides that are significantly different between sets.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
tm_df<-output_tc(filepath=file_nm)
tmP_df<-output_tc(filepath=file_nm, percent=TRUE)
# more restictive peptide selection
robot_indexes_df(thP = tmP_df, th=tm_df, pvalue=0.01, CI_factor=1.5)
Returns a robot plot for comparisons of the timepoints samples
Description
Modification of butterfly plot. x axis residues. y axis % deuteration for one variant above the axis and for second peptide below the axis. Peptides are compared between the sets for the significance change between sets. If there is significant change beteween sets peptides are plotted for all timepoints. Significanty different timepoints for the peptides are colored. Peptides ranges are plotted as a line at corresponding % deuteration values.
Usage
robot_plot_All(
thP,
th,
replicates = 3,
pv_cutoff = 0.01,
states,
CI_factor = 1
)
Arguments
thP |
output of output_tcourse_proc() function. Raw data for procent deuteration for time courses |
th |
output of output_tcourse() function. Raw data for uptake deuteration for time courses |
replicates |
number of replicates in sample. Default set to 3. |
pv_cutoff |
p-value cutoff here set up to 0.01 |
states |
Protein states from the set. As default all states are chosen. |
CI_factor |
Multiplication factor for Critical Interval. Allows for more restrictive selection of Critial interval. |
Value
Robot maps for timecourses
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
tm_df<-output_tc(filepath=file_nm)
tmP_df<-output_tc(filepath=file_nm, percent=TRUE)
robot_plot_All(thP = tmP_df, th=tm_df, pv_cutoff=0.001)
# more restrictive peptide selection
robot_plot_All(thP = tmP_df, th=tm_df, pv_cutoff=0.001, CI_factor=3)
Returns standard deviation for uptake data for timecourses.
Description
Calculates standard deviation for timecourse data.
Usage
sd_timecourse(filepath)
Arguments
filepath |
filepath to the All_results input file. |
Value
Data.frame with standard deviation.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
sd_timecourse(filepath=file_nm)
Returns standard deviation for percent deuteration data for timecourses.
Description
Calculates standard deviation for time course data.
Usage
sd_timecourse_proc(filepath)
Arguments
filepath |
filepath to the All_results input file. |
Value
Data.frame with standard deviation.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
sd_timecourse(filepath=file_nm)
Returns standard deviation for dataframe.
Description
Calculates standard deviation for the number of replicates in the function.
Usage
sd_timepoint(df, replicates = 3)
Arguments
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates used. Default is set to replicates=3 |
Value
Data.frame with standard deviation.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
sd<-sd_timepoint(df=a, replicates=3)
Allows for selecting some peptide from input data
Description
Function allows for picking indices from the inputs based on: peptide start or end residue, length, state or timepoint. If parameters set to NA, condition is skipped.
Usage
select_indices(df, start = NA, end = NA, length = NA, times = NA, states = NA)
Arguments
df |
input file (output of output_tc or output_tp) |
start |
provide number for the staring residue, default NA |
end |
provide number for the end residue, default NA |
length |
provide max length of the peptide |
times |
timepoints, only for the output_tp functions |
states |
states, only for the output_tc functions |
Value
Row indices of the peptides that are fulfilling the conditions required.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tp(file_nm)
indb<-select_indices(a,length=12, start=100, end=200)
smaller_df<-a[indb,]
Function returns which peptides are significantly based of pv_cutoff and Critial interval
Description
Returns data frame with significant peptides.
Usage
significant_peptide_uptake(df_av, pv, sd, pv_cutoff = 0.01, replicates = 3)
Arguments
df_av |
data.frame with averages created using ave_timepoint() function |
pv |
data.frame with pvalues created using pv_timepoint() function |
sd |
data.frame with standard deviations created using sd_timepoint() function |
pv_cutoff |
cuttoff for Critical interval. Default=0.01 |
replicates |
number of replicates as default set to 3. |
Value
ranges per set
Provides summary table with Critical interval and standard deviation within the set.
Description
Returns summary data. Function returns: Protein states, timepoints, number of replicates, # peptides, % coveregae, average peptide length and redundancy.
Usage
summary_sd_CI(filepath, replicates = 3)
Arguments
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
replicates |
number of replicates. Default set to 3. |
Value
Returns summary table.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- summary_sd_CI(file_nm, replicates=3)
Uptake plots
Description
Uptake plots per peptide
Usage
uptake_plots(
input_data,
timepoints,
replicates = 3,
cola = NA,
seq_match = TRUE
)
Arguments
input_data |
output from function output_tp(..., percent=T) |
timepoints |
the labeling times |
replicates |
replicates |
cola |
colors, default NA |
seq_match |
Flag TRUE or FALSE, default TRUE, match sequence of the protein states |
Value
Uptake plots
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(file_nm, percent=TRUE)
x=c(3,60, 1800, 72000)
uptake_plots(a, x)
Returns csv with averages from analysis for procent deuteration file, standard deviation for time courses.
Description
Returns information from analysis and save it as csv file. Sets are compared to the first state in the input file.
Usage
verbose_timecourse_output(filepath, output_name, replicates = 3, ...)
Arguments
filepath |
path to All.Data.csv input from HDX-Examiner. |
output_name |
name of the output in csv format. |
replicates |
number of replicates used |
... |
other variables for output_tc |
Value
csv with analysis for procent deuteration: standard deviation, for all protein states for time courses.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
verbose_timecourse_output(file_nm,tempfile(), replicates=3)
names_states<- nm_states(file_nm)
verbose_timecourse_output(file_nm, tempfile(), seq_match=TRUE, percent=TRUE,
states=names_states, replicates=3, times="3.00s")
Returns csv with averages from analysis for uptake file, standard deviation, p-values.
Description
Returns information from analysis and save it as csv file. Sets are compared to the first state in the input file.
Usage
verbose_timepoint_output(filepath, output_name, replicates = 3, ...)
Arguments
filepath |
path to All.Data.csv input from HDX-Examiner. |
output_name |
name of the output in csv format. |
replicates |
number of replicates used |
... |
other variables for output_tp |
Value
csv with analysis for uptake file, standard deviation, p-values for all protein states.
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
verbose_timepoint_output(file_nm, tempfile())
names_states<- nm_states(file_nm)
verbose_timepoint_output(file_nm, tempfile(), seq_match=TRUE, percent=TRUE,
states=names_states, replicates=3, times="3.00s")
Preparatory function for volcano plot
Description
Returns volcano plots
Usage
vol_tp(df1, pv, CI, pv_cutoff = 0.01, cola)
Arguments
df1 |
differences in averages data.frame calculated using diff_ave function |
pv |
pvalues dataframes calculated using pv_timepoint function |
CI |
critical interval, here is multiple sets are using maximun CI is used. |
pv_cutoff |
p-value cutoff here set up to 0.01 |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
Value
volcano plots
Returns a woods plot for comparisons of the timepoints samples
Description
Modification of butterfly plot. x axis residues. y axis % deuteration for Peptides are compared between the sets for the significance change between sets. If there is significant change beteween sets peptides are plotted for all timepoints. Significanty different timepoints for the peptides are colored. Peptides ranges are plotted as a line at corresponding % deuteration values.
Usage
woods_CI_plot(
thP,
th,
replicates = 3,
pv_cutoff = 0.01,
states,
CI_factor = 1,
ylim = c(0, 120),
...
)
Arguments
thP |
output of output_tcourse_proc() function. Raw data for procent deuteration for time courses |
th |
output of output_tcourse() function. Raw data for uptake deuteration for time courses |
replicates |
number of replicates in sample. Default set to 3. |
pv_cutoff |
p-value cutoff here set up to 0.01 |
states |
Protein states from the set. As default all states are chosen. |
CI_factor |
Multiplication factor for Critical Interval. Allows for more restrictive selection of Critial interval. |
ylim |
y axis limit |
... |
other variables |
Value
Woods plots with chosen statistically different peptides
Examples
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
a<- output_tc(file_nm)
b<-output_tc(file_nm, percent=TRUE)
woods_CI_plot(thP=b, th=a, pv_cutoff = 0.001, CI_factor = 1, replicates=3)