, Katherine Reiter
[ctb], Pearl Magala [ctb], Miklos Guttman [ctb], Rachel E.
Klevit [ctb]| Title: | Analysis of Hydrogen-Deuterium Exchange Mass-Spectrometry Data |
|---|---|
| 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>. |
| Authors: | Maria K. Janowska [aut, cre]
, Katherine Reiter
[ctb], Pearl Magala [ctb], Miklos Guttman [ctb], Rachel E.
Klevit [ctb] |
| Maintainer: | Maria K. Janowska <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.0.2 |
| Built: | 2025-02-15 05:10:35 UTC |
| Source: | https://github.com/mkajano/hdxboxer |
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.
all_summary(filepath, replicates = 3, Dfact = 0.85)all_summary(filepath, replicates = 3, Dfact = 0.85)
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 |
Returns summary table.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- all_summary(file_nm, replicates=3, Dfact=0.85)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- all_summary(file_nm, replicates=3, Dfact=0.85)
Function used as internal function
arg_df(filepath)arg_df(filepath)
filepath |
input file location |
Data.frame for further processing
Function used as internal function
arg_UN_FD(filepath)arg_UN_FD(filepath)
filepath |
input file location |
Data.frame for further processing
Function used as internal function
arguments_call1(filepath)arguments_call1(filepath)
filepath |
input file location |
The default arguments to output_tp functions.
Function used as internal function
arguments_call2(filepath, states)arguments_call2(filepath, states)
filepath |
input file location |
states |
states used |
The default arguments to output_tp functions.
Function used as internal function
arguments_call3(filepath, states, times)arguments_call3(filepath, states, times)
filepath |
input file location |
states |
states used |
times |
deuteration times |
The default arguments to output_tp functions.
Returns plots with average deuteration at each peptide.
av_tc(df, cola)av_tc(df, cola)
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. |
plots of averages
Returns plots with average deuteration at each peptide.
av_tp(df, cola)av_tp(df, cola)
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. |
plots of averages
Calculates average of uptake or procent data. Returns data frame with average values. Default for the number of replicates is 3.
ave_timepoint(df, replicates = 3)ave_timepoint(df, replicates = 3)
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates used. Default is set to replicates=3 |
Data.frame with average values
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 4file_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.
average_timecourse(filepath)average_timecourse(filepath)
filepath |
filepath to the All_results input file. |
data frame with average deuteration uptake data.
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.
backHX_calculations(filepath, Dfact = 0.85)backHX_calculations(filepath, Dfact = 0.85)
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 |
Returns summary table for backexchange.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- backHX_calculations(filepath=file_nm, Dfact=0.85)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- backHX_calculations(filepath=file_nm, Dfact=0.85)
Returns boxplots to compare sets between each other
boxplot_tp(df, replicates = 3, ...)boxplot_tp(df, replicates = 3, ...)
df |
average data frame. Generated using ave_timepoint() function. |
replicates |
number of replicates in sample. Default set to 3. |
... |
inherited boxplot parameters |
boxplots for average deuterium uptake per set.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) boxplot_tp(df=a, replicates=3)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) boxplot_tp(df=a, replicates=3)
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
CI_2pts(s1, s2, replicates = 3)CI_2pts(s1, s2, replicates = 3)
s1 |
standard deviation from one sample |
s2 |
standard deviation from seconda sample |
replicates |
number of replicates. Default set to 3. |
treshold for determining significance.
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)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)
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
CI_single(s1, replicates = 3)CI_single(s1, replicates = 3)
s1 |
standard deviation from one sample |
replicates |
number of replicates. Default set to 3. |
treshold for determining significance.
sd1<-data.frame(c(0.1, 0.12, 0.13, 0.09, 0.11, 0.10)) CI_single(s1=sd1, replicates=3)sd1<-data.frame(c(0.1, 0.12, 0.13, 0.09, 0.11, 0.10)) CI_single(s1=sd1, replicates=3)
Preparatory function for calculation of pvalue between sets.
CI_tc(sd_c, sd_v, replicates = 3, pv_cutoff = 0.01)CI_tc(sd_c, sd_v, replicates = 3, pv_cutoff = 0.01)
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 |
Critical interval for 2 sets
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
CI_tp(df, replicates = 3, alpha = 0.01)CI_tp(df, replicates = 3, alpha = 0.01)
df |
standard deviation dataframe. |
replicates |
number of replicates. Default set to 3. |
alpha |
significance level. Set as default to 0.01 |
treshold for determining significance.
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)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 color pallete from red to blue with number of colors for defined ranges
color_ranges_Blue_Red_heat_map(ranges, colors_initial)color_ranges_Blue_Red_heat_map(ranges, colors_initial)
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. |
color scheme for number
color_ranges_Blue_Red_heat_map(ranges=c(-Inf, -100, -50, 0, 50, 100, Inf), colors_initial="white")color_ranges_Blue_Red_heat_map(ranges=c(-Inf, -100, -50, 0, 50, 100, Inf), colors_initial="white")
Spectral pallette for timecourse data
color_ranges_Spectral(ranges, colors_initial)color_ranges_Spectral(ranges, colors_initial)
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. |
color scheme for number
color_ranges_Spectral(ranges=c(-Inf, -100, -50, 0, 50, 100, Inf), colors_initial="white")color_ranges_Spectral(ranges=c(-Inf, -100, -50, 0, 50, 100, Inf), colors_initial="white")
returns vector with coverage information
coverage_residue(df1, start_col, end_col)coverage_residue(df1, start_col, end_col)
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 |
vector with coverage per residue
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 )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 )
All the peptides are plotted based on their uptake.
deuteration_woods_timecourse( input_data, states, replicates = 3, ylim = c(0, 120), ... )deuteration_woods_timecourse( input_data, states, replicates = 3, ylim = c(0, 120), ... )
input_data |
output from function output_tc(..., percent=TRUE) |
states |
states, if missing all states used |
replicates |
replicates |
ylim |
y axis limits |
... |
other parameters |
Woods plots for the timecourse
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tc(file_nm, percent=TRUE) deuteration_woods_timecourse(a)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tc(file_nm, percent=TRUE) deuteration_woods_timecourse(a)
All the peptides are plotted based on their uptake.
deuteration_woods_timepoints( input_data, times, replicates = 3, cola = NA, ylim = c(0, 120), ... )deuteration_woods_timepoints( input_data, times, replicates = 3, cola = NA, ylim = c(0, 120), ... )
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 |
Woods plots for the timepoints
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm, percent=TRUE) deuteration_woods_timepoints(a[1:12,])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 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.
dif_ave(df)dif_ave(df)
df |
output from functions output_tp, output_tp_proc, output_tp_states or output_tp_proc_states. |
Data.frame with difference values btw control and other protein states.
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 State4file_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
Returns plots with difference deuteration at each peptide.
dif_tp(df, cola)dif_tp(df, cola)
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. |
plots of difference in average
Returns plots with difference deuteration at each peptide.
dif_tp_proc(df, cola)dif_tp_proc(df, cola)
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. |
plots of difference in average
Internal function
duplicate_sets(df)duplicate_sets(df)
df |
dataframe |
duplicate sets
Makes input for Extreme for bimodal analysis.
extreme_input_gap(hm_dir, replicates, timepoints, output_path = "NA")extreme_input_gap(hm_dir, replicates, timepoints, output_path = "NA")
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 |
Inputs for extreme for all data prepared.
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())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())
If data is missing it returns non-deuterated data in these columns.
extreme_input_undeut(hm_dir, replicates, timepoints, output_path = "NA")extreme_input_undeut(hm_dir, replicates, timepoints, output_path = "NA")
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 |
Inputs for extreme for all data prepared.
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())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())
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.
general_info(filepath)general_info(filepath)
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
Returns summary table.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- general_info(file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- general_info(file_nm)
Margin coordinates
getCoords1(pos = 1.1, side = 1, input = "p")getCoords1(pos = 1.1, side = 1, input = "p")
pos |
position |
side |
side of plot |
input |
plot or figure position |
coordinates of margins
Returns heat map on timecourses with raw data.
heat_map_tc(df, ranges = c(seq(0, 100, by = 10), Inf))heat_map_tc(df, ranges = c(seq(0, 100, by = 10), Inf))
df |
timecourse input |
ranges |
ranges for coloring scheme. Default set to c(seq(0, 100, by=10), Inf) |
heat map for timecourses
Returns heat map
heat_map_tp( df, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )heat_map_tp( df, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
heat map for timepoints
Returns heat map
heat_map_tp_maxuptake( df, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )heat_map_tp_maxuptake( df, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
maxiumum uptake heat map for timepoints
Returns heat map
heat_map_tp_maxuptake_proc( df, dfup, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )heat_map_tp_maxuptake_proc( df, dfup, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
Maximum uptake heat map for timepoints
Returns heat map
heat_map_tp_proc( df, dfup, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )heat_map_tp_proc( df, dfup, pv, sd, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
heat map for timepoints
Function by Hong Ooi; https://stackoverflow.com/questions/18142117/how-to-replace-nan-value-with-zero-in-a-huge-data-frame
## S3 method for class 'data.frame' is.nan(x)## S3 method for class 'data.frame' is.nan(x)
x |
Data frame to be checked for NaN |
logical. Returns info if data.frame contains NaNs.
## 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## 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
Returns legend for difference in average plots. Preparatory function.
lab_dif(df, cola)lab_dif(df, cola)
df |
output from functions average difference |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
legend for difference in average plot for time points
Returns legends for plots procent deuteration at each peptide.
lab_dif_proc(df, cola)lab_dif_proc(df, cola)
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. |
legends for procent deuteration plots
Returns volcano plots
lab_vol(df, cola)lab_vol(df, cola)
df |
output from functions output_tp |
cola |
color pallette for different Protein States. As default Paired pallette from color.Brewer is used. |
legends for volcano plots
Returns names for legend for the heatmaps
legend_heat_map(ranges = c(-Inf, seq(-30, 30, by = 10), Inf))legend_heat_map(ranges = c(-Inf, seq(-30, 30, by = 10), Inf))
ranges |
ranges that are to be colored in the legend. Default ranges=c(-Inf,seq(-30, 30, by=10), Inf ) |
legend for the heatmap
Returns names for legend for the heatmaps. Extracts names from data.frame
legend_heat_map_tc(df)legend_heat_map_tc(df)
df |
generated using output_tcourse() |
legend for the heatmap
Returns names for legend for the heatmaps
legend_heat_map_timecourse(ranges = c(-Inf, seq(0, 100, by = 10), Inf))legend_heat_map_timecourse(ranges = c(-Inf, seq(0, 100, by = 10), Inf))
ranges |
ranges that are to be colored in the legend. Default ranges=c(-Inf,seq(-30, 30, by=10), Inf ) |
legend for the heatmap
Returns names for legend for the heatmaps
legend_heat_map_tp(df)legend_heat_map_tp(df)
df |
average data frame. Generated using ave_timepoint() function. |
legend for the heatmap
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) legend_heat_map_tp(df=a)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) legend_heat_map_tp(df=a)
Returns names for legend for the heatmaps
legend_heat_map_tp_proc(df)legend_heat_map_tp_proc(df)
df |
average data frame. |
legend for the heatmap prercent
Internal function
legend_nm_bottom(names, cols)legend_nm_bottom(names, cols)
names |
labels |
cols |
colors |
legend at the bottom of the plot
Returns legend with average plots. Preparatory function.
legend_raw_ave(df, cola)legend_raw_ave(df, cola)
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. |
legend for average plot for time points
Returns legend with average procent deuteration at each peptide.
legend_raw_ave_proc(df, cola)legend_raw_ave_proc(df, cola)
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. |
legend for average deuteration procent for timepoints
Returns legend with average plots. Preparatory function.
legend_raw_ave_tc(df, cola)legend_raw_ave_tc(df, cola)
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. |
legend for average plot for time course
Returns names for legend for the significant peptides plots.
legend_sig_peptides(ranges = c(-Inf, seq(-30, 30, by = 10), Inf))legend_sig_peptides(ranges = c(-Inf, seq(-30, 30, by = 10), Inf))
ranges |
ranges that are to be colored in the legend. Default ranges=c(-Inf,seq(-30, 30, by=10), Inf ) |
legend for the heatmap
Internal function
legend_states_PerD_bottom(df, cols)legend_states_PerD_bottom(df, cols)
df |
dataframe |
cols |
colors |
legend at the bottom of the plot
Preparatory function
legend_tc_bottom(df, cols)legend_tc_bottom(df, cols)
df |
data frame from which names will be extracted |
cols |
colors to be used in legend |
legend at the bottom of the plot
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
nb_exch_deut(df)nb_exch_deut(df)
df |
standard deviation from one sample |
vector with number of exchangeable protons
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) nb_exch_deut(a)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) nb_exch_deut(a)
Returns vector with name of states used for choosing states for input functions generation.
nm_states(filepath)nm_states(filepath)
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
list of Protein States.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") names_states<- nm_states(file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") names_states<- nm_states(file_nm)
Returns a data frame for Full deuteration set
output_FD(filepath)output_FD(filepath)
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
data frame with reorganized data where in columns is uptake data for Protein States.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<-output_FD(file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<-output_FD(file_nm)
Returns a data frame for Full deuteration set
output_FD_proc(filepath)output_FD_proc(filepath)
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
data frame with reorganized data where in columns is procent deuteration for Protein States.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_FD_proc(file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_FD_proc(file_nm)
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.
output_prep(filepath, output_name, states, replicates, times, percent = FALSE)output_prep(filepath, output_name, states, replicates, times, percent = FALSE)
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 |
Returns&saves data.frame in format that is accepted for the publications.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") output_prep(filepath=file_nm, output_name=tempfile())file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") output_prep(filepath=file_nm, output_name=tempfile())
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.
output_tc( filepath, replicates, states, times, seq_match = FALSE, csv = "NA", percent = FALSE )output_tc( filepath, replicates, states, times, seq_match = FALSE, csv = "NA", percent = FALSE )
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. |
data frame with reorganized data where in columns is the deuteration uptake for Protein States.
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)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)
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.
output_tp( filepath, replicates, states, times, seq_match = FALSE, csv = "NA", percent = FALSE )output_tp( filepath, replicates, states, times, seq_match = FALSE, csv = "NA", percent = FALSE )
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. |
data frame with reorganized data where in columns is the deuteration uptake for Protein States.
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)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)
Returns a data frame for Full deuteration set
output_UD(filepath)output_UD(filepath)
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
data frame with reorganized data where in columns is uptake data for Protein States.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_UD(file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_UD(file_nm)
Returns a data frame for Undeuterated control set
output_UD_proc(filepath)output_UD_proc(filepath)
filepath |
filepath to the input file. Input file is All_results table from HDX_Examiner, where all the fields are marked for export. |
data frame with reorganized data where in columns is procent deuteration for Protein States.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_UD_proc(file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_UD_proc(file_nm)
Returns names for legend for the heatmaps
pallette_legend(col_pallette)pallette_legend(col_pallette)
col_pallette |
pallette to be used in the heat map |
legend for the heatmap
Returns names for legend for the heatmaps
pallette_ll(pallette, lab)pallette_ll(pallette, lab)
pallette |
pallette to be used in the heat map |
lab |
labels to be used in pallette |
legend for the heatmap
Returns plot where significant peptides are colored in blue-red scheme.
peptide_pv_tp( df, pv, sd, nb_row, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )peptide_pv_tp( df, pv, sd, nb_row, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
plot with peptides which are significantly different between sets.
Returns plot where significantly different peptides are colored in blue-red scheme.
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 )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 )
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. |
plot with peptides which are significantly different between sets.
Internal function
pl_gen_ch2(df, ddlab = 1, ...)pl_gen_ch2(df, ddlab = 1, ...)
df |
dataframe |
ddlab |
label |
... |
other |
Plot window
Internal function
pl_gen_uptake(df, timepoints, ddlab = 1, ...)pl_gen_uptake(df, timepoints, ddlab = 1, ...)
df |
dataframe |
timepoints |
deuteration times used |
ddlab |
label |
... |
other |
Plot window
Returns heat map with maximum uptake per residue.
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 )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 )
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 |
heat map for maximum uptake per residue
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)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)
Returns heat map with maximum precent_deuteration per residue.
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 )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 )
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. |
heat map for average uptake per residue for significant peptides.
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)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)
Returns heat map on timecourses with raw data.
plot_heat_map_tc( df, replicates = 3, mar_x = 3.5, ranges = c(-Inf, seq(0, 100, by = 10), Inf) )plot_heat_map_tc( df, replicates = 3, mar_x = 3.5, ranges = c(-Inf, seq(0, 100, by = 10), Inf) )
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) |
heat map for time courses
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)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)
Returns heat map with average values for significant uptake per residue.
plot_heat_map_tp( df, mar_x = 3.5, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )plot_heat_map_tp( df, mar_x = 3.5, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
heat map for average uptake per residue for significant peptides.
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)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)
Returns heat map with average values for significant uptake per residue.
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 )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 )
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. |
heat map for average uptake per residue for significant peptides.
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)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)
Returns plot where significant peptides are colored in blue-red scheme.
plot_peptide_sig_tp( df1, replicates = 3, nb_pep_row = 100, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01 )plot_peptide_sig_tp( df1, replicates = 3, nb_pep_row = 100, ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01 )
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 |
plot with peptides which are significantly different between sets.
Returns plot where significant peptides are colored in blue-red scheme.
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 )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 )
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. |
plot with peptides which are significantly different between sets.
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 )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 )
Returns plots with average deuteration at each peptide.
plots_av_tcourse(df, replicates = 3, cola)plots_av_tcourse(df, replicates = 3, cola)
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. |
average deuteration plots
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)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 plots with average deuteration at each peptide.
plots_av_tp(df, replicates = 3, cola)plots_av_tp(df, replicates = 3, cola)
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. |
average deuteration plots
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)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 plots with average procent deuteration at each peptide.
plots_av_tp_proc(df, replicates = 3, cola)plots_av_tp_proc(df, replicates = 3, cola)
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. |
average deuteration plots
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)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 plots with difference in avarage for each peptide.
plots_diff_tp(df, replicates = 3, cola)plots_diff_tp(df, replicates = 3, cola)
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. |
plots of difference of averages
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)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 plots with difference in procent deuteration for each peptide.
plots_diff_tp_proc(df, replicates = 3, cola)plots_diff_tp_proc(df, replicates = 3, cola)
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. |
plots of difference of average procent deuteration
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)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 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.
plots_vol_tp(df, replicates = 3, pv_cutoff = 0.01, cola)plots_vol_tp(df, replicates = 3, pv_cutoff = 0.01, cola)
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. |
volcano plots
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)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)
Prepares a plotting window with specified margins with specific number of figure row and columns.
ppar(mfrow2)ppar(mfrow2)
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
modified par function with adjusted parameters
ppar(c(2,1))ppar(c(2,1))
Prepares a plotting window with specified margins with specific number of figure row and columns.
ppar_bottom_legend(mfrow2)ppar_bottom_legend(mfrow2)
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
modified par function with adjusted parameters
ppar_bottom_legend(c(2,3))ppar_bottom_legend(c(2,3))
Prepares a plotting window with specified margins with specific number of figure row and columns.
ppar_wider(mfrow2)ppar_wider(mfrow2)
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
default plotting window
ppar_wider(c(2,1))ppar_wider(c(2,1))
Prepares a plotting window with specified margins with specific number of figure row and columns.
pparLM(mfrow2)pparLM(mfrow2)
mfrow2 |
mfrow: number of Multiple Figures (use ROW-wise). |
modified par function with adjusted parameters
pparLM(c(2,1))pparLM(c(2,1))
Preparatory function
prep_timecourse_plot_ave(control_df, variant_df, replicates = 3)prep_timecourse_plot_ave(control_df, variant_df, replicates = 3)
control_df |
dataframe of control |
variant_df |
dataframe for variant |
replicates |
number of replicates. Default set to 3. |
dataframes with matched peptides in time course
Preparatory function
prep_timecourse_plot_sd( control_df_up, variant_df_up, replicates = 3, pv_cutoff = 0.01 )prep_timecourse_plot_sd( control_df_up, variant_df_up, replicates = 3, pv_cutoff = 0.01 )
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 |
Critial interval for all sets
Preparatory function for calculation of pvalue between sets.
pv_timecourse(df_c, df_v, replicates = 3)pv_timecourse(df_c, df_v, replicates = 3)
df_c |
dataframe of control |
df_v |
dataframe for variant |
replicates |
number of replicates. Default set to 3. |
pvalue comparisons between two sets.
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.
pv_timepoint(df, replicates = 3)pv_timepoint(df, replicates = 3)
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates used. Default is set to replicates=3 |
Data.frame with p-values
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 State4file_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
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.
pymol_script_average_residue( df, path = "", ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )pymol_script_average_residue( df, path = "", ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
pymol script with residues colored based on average of uptake per residue.
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())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())
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.
pymol_script_significant_peptide( df, path = "", ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3, order.pep = TRUE )pymol_script_significant_peptide( df, path = "", ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3, order.pep = TRUE )
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. |
pymol script with colors assigned per peptide
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())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())
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.
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 )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 )
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. |
pymol script with colors assigned per peptide
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)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)
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.
pymol_script_significant_residue( df, path = "", ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )pymol_script_significant_residue( df, path = "", ranges = c(-Inf, seq(-30, 30, by = 10), Inf), pv_cutoff = 0.01, replicates = 3 )
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. |
pymol script with colors assigned per residues by maximum uptake per residue
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())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())
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.
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 )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 )
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. |
pymol script with residues colored based on average of procent deuteration per residue.
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))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))
Function rearrange vector to string by adding + sign between the numbers.
pymol_str(ind1)pymol_str(ind1)
ind1 |
vector of numbers (residues) |
string with + as a separator.
res<-c(1,5, 19, 100, 109) pymol_str(res)res<-c(1,5, 19, 100, 109) pymol_str(res)
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
qpcr.cbind.na(..., deparse.level = 1)qpcr.cbind.na(..., deparse.level = 1)
... |
vectors |
deparse.level |
set to 1 as default |
data frame with NA
qpcr.cbind.na(1:10, 1:3)qpcr.cbind.na(1:10, 1:3)
Function used as internal function to get ranges in the function.
ranges_function(df_ave, values_df)ranges_function(df_ave, values_df)
df_ave |
average per residues |
values_df |
data frame with values. |
ranges per set
Function used as internal function to get ranges in the function.
ranges_function_tc(df_ave, values_df)ranges_function_tc(df_ave, values_df)
df_ave |
average per residues |
values_df |
data frame with values. |
ranges per set
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
rbind_na(..., deparse.level = 1)rbind_na(..., deparse.level = 1)
... |
(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. |
a data frame with merged rows
row1 <- c("a","b","c","d") row2 <- c("A", "B", "C") row3 <- rbind_na(row1, row2)row1 <- c("a","b","c","d") row2 <- c("A", "B", "C") row3 <- rbind_na(row1, row2)
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
reset_par()reset_par()
default plotting window parameters
reset_par()reset_par()
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.
robot_2states_indexes( thP, th, indexes, states, replicates = 3, pvalue = 0.01, ylim, xlim, CI_factor = 1 )robot_2states_indexes( thP, th, indexes, states, replicates = 3, pvalue = 0.01, ylim, xlim, CI_factor = 1 )
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. |
Robot maps for timecourses for 2 protein states and selected indexes.
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)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)
Function to help decide which peptides will be drawn on Robot plots.
robot_indexes(thP, th, replicates = 3, pvalue = 0.01, states, CI_factor = 1)robot_indexes(thP, th, replicates = 3, pvalue = 0.01, states, CI_factor = 1)
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. |
Returns indexes of significant peptides
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)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)
Function to help decide which peptides will be drawn on Robot plots.
robot_indexes_df(thP, th, replicates = 3, pvalue = 0.01, states, CI_factor = 1)robot_indexes_df(thP, th, replicates = 3, pvalue = 0.01, states, CI_factor = 1)
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. |
Returns dataframe listing peptides that are significantly different between sets.
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)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)
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.
robot_plot_All( thP, th, replicates = 3, pv_cutoff = 0.01, states, CI_factor = 1 )robot_plot_All( thP, th, replicates = 3, pv_cutoff = 0.01, states, CI_factor = 1 )
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. |
Robot maps for timecourses
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)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)
Calculates standard deviation for timecourse data.
sd_timecourse(filepath)sd_timecourse(filepath)
filepath |
filepath to the All_results input file. |
Data.frame with standard deviation.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") sd_timecourse(filepath=file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") sd_timecourse(filepath=file_nm)
Calculates standard deviation for time course data.
sd_timecourse_proc(filepath)sd_timecourse_proc(filepath)
filepath |
filepath to the All_results input file. |
Data.frame with standard deviation.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") sd_timecourse(filepath=file_nm)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") sd_timecourse(filepath=file_nm)
Calculates standard deviation for the number of replicates in the function.
sd_timepoint(df, replicates = 3)sd_timepoint(df, replicates = 3)
df |
output from functions output_tp or output_tp_proc. |
replicates |
number of replicates used. Default is set to replicates=3 |
Data.frame with standard deviation.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) sd<-sd_timepoint(df=a, replicates=3)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- output_tp(file_nm) sd<-sd_timepoint(df=a, replicates=3)
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.
select_indices(df, start = NA, end = NA, length = NA, times = NA, states = NA)select_indices(df, start = NA, end = NA, length = NA, times = NA, states = NA)
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 |
Row indices of the peptides that are fulfilling the conditions required.
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,]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,]
Returns data frame with significant peptides.
significant_peptide_uptake(df_av, pv, sd, pv_cutoff = 0.01, replicates = 3)significant_peptide_uptake(df_av, pv, sd, pv_cutoff = 0.01, replicates = 3)
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. |
ranges per set
Returns summary data. Function returns: Protein states, timepoints, number of replicates, # peptides, % coveregae, average peptide length and redundancy.
summary_sd_CI(filepath, replicates = 3)summary_sd_CI(filepath, replicates = 3)
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. |
Returns summary table.
file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- summary_sd_CI(file_nm, replicates=3)file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR") a<- summary_sd_CI(file_nm, replicates=3)
Uptake plots per peptide
uptake_plots( input_data, timepoints, replicates = 3, cola = NA, seq_match = TRUE )uptake_plots( input_data, timepoints, replicates = 3, cola = NA, seq_match = TRUE )
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 |
Uptake plots
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)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 information from analysis and save it as csv file. Sets are compared to the first state in the input file.
verbose_timecourse_output(filepath, output_name, replicates = 3, ...)verbose_timecourse_output(filepath, output_name, replicates = 3, ...)
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 |
csv with analysis for procent deuteration: standard deviation, for all protein states for time courses.
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")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 information from analysis and save it as csv file. Sets are compared to the first state in the input file.
verbose_timepoint_output(filepath, output_name, replicates = 3, ...)verbose_timepoint_output(filepath, output_name, replicates = 3, ...)
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 |
csv with analysis for uptake file, standard deviation, p-values for all protein states.
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")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")
Returns volcano plots
vol_tp(df1, pv, CI, pv_cutoff = 0.01, cola)vol_tp(df1, pv, CI, pv_cutoff = 0.01, cola)
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. |
volcano plots
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.
woods_CI_plot( thP, th, replicates = 3, pv_cutoff = 0.01, states, CI_factor = 1, ylim = c(0, 120), ... )woods_CI_plot( thP, th, replicates = 3, pv_cutoff = 0.01, states, CI_factor = 1, ylim = c(0, 120), ... )
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 |
Woods plots with chosen statistically different peptides
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)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)