Package 'TPP2D'

Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP)
Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures.
Authors: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut]
Maintainer: Nils Kurzawa <[email protected]>
License: GPL-3
Version: 1.21.0
Built: 2024-07-25 05:15:05 UTC
Source: https://github.com/bioc/TPP2D

Help Index

Annotate imported data list using a config table

Description

Annotate imported data list using a config table

Usage

annotateDataList(dataList, geneNameVar, configLong, intensityStr, fcStr)

Arguments

dataList

list of datasets from different MS runs corresponding to a 2D-TPP dataset
geneNameVar

character string of the column name that describes the gene name of a given protein in the raw data files
configLong

long formatted data frame of a corresponding config table
intensityStr

character string indicating which columns contain raw intensities measurements
fcStr

character string indicating which columns contain the actual fold change values. Those column names containing the suffix fcStr will be regarded as containing fold change values.

Value

data frame containing all data annotated by information supplied in the config table

Examples

data("config_tab")
data("raw_dat_list")
dataList <- import2dMain(configTable = config_tab,
                         data = raw_dat_list,
                         idVar = "protein_id",
                         fcStr = "rel_fc_",
                         addCol = "gene_name",
                         naStrs = NA,
                         intensityStr = "signal_sum_",
                         nonZeroCols = "qusm",
                         qualColName = "qupm")
configLong <- configWide2Long(configWide = config_tab)
annotateDataList(dataList = dataList,
                 geneNameVar = "gene_name",
                 configLong = configLong,
                 intensityStr = "signal_sum_",
                 fcStr = "rel_fc_")

Bootstrap null distribution of F statistics for FDR estimation

Description

Bootstrap null distribution of F statistics for FDR estimation

Usage

bootstrapNull(
  df,
  maxit = 500,
  independentFiltering = FALSE,
  fcThres = 1.5,
  minObs = 20,
  optim_fun_h0 = .min_RSS_h0,
  optim_fun_h1 = .min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  ncores = 1,
  B = 20,
  byMsExp = TRUE
)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
maxit

maximal number of iterations the optimization should be given, default is set to 500
independentFiltering

boolean flag indicating whether independent filtering should be performed based on minimal fold changes per protein profile
fcThres

numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
minObs

numeric value of minimal number of observations that should be required per protein
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
ncores

numeric value of numbers of cores that the function should use to parallelize
B

numeric value of rounds of bootstrap, default: 20
byMsExp

boolean flag indicating whether resampling of residuals should be performed separately for data generated by different MS experiments, default TRUE, recommended

Value

data frame containing F statistics of proteins with permuted 2D thermal profiles that are informative on the Null distribution of F statistics

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:3)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
boot_df <- bootstrapNull(temp_df, B = 2/10)

Bootstrap null distribution of F statistics for FDR estimation based on resampling alternative model residuals

Description

Bootstrap null distribution of F statistics for FDR estimation based on resampling alternative model residuals

Usage

bootstrapNullAlternativeModel(
  df,
  params_df,
  maxit = 500,
  independentFiltering = FALSE,
  fcThres = 1.5,
  minObs = 20,
  optim_fun_h0 = TPP2D:::.min_RSS_h0,
  optim_fun_h1 = TPP2D:::.min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  BPPARAM = BiocParallel::SerialParam(progressbar = TRUE),
  B = 20,
  byMsExp = TRUE,
  verbose = FALSE
)

Arguments

df

tidy data frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
params_df

data frame listing all null and alternative model parameters as obtained by 'getModelParamsDf'
maxit

maximal number of iterations the optimization should be given, default is set to 500
independentFiltering

boolean flag indicating whether independent filtering should be performed based on minimal fold changes per protein profile
fcThres

numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
minObs

numeric value of minimal number of observations that should be required per protein
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
BPPARAM

BiocParallel parameter for optional parallelization of null distribution generation through bootstrapping, default: BiocParallel::SerialParam()
B

numeric value of rounds of bootstrap, default: 20
byMsExp

boolean flag indicating whether resampling of residuals should be performed separately for data generated by different MS experiments, default TRUE, recommended
verbose

logical indicating whether to print each protein while its profile is boostrapped

Value

data frame containing F statistics of proteins with permuted 2D thermal profiles that are informative on the Null distribution of F statistics

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:3)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
temp_params_df <- getModelParamsDf(temp_df)
boot_df <- bootstrapNullAlternativeModel(
  temp_df, params_df = temp_params_df, B = 2)

Bootstrap null distribution of F statistics for FDR estimation based on resampling alternative model residuals with only one round of model fitting on resampled data and subsequent resampling of thereby obtained residuals

Description

Bootstrap null distribution of F statistics for FDR estimation based on resampling alternative model residuals with only one round of model fitting on resampled data and subsequent resampling of thereby obtained residuals

Usage

bootstrapNullAlternativeModelFast(
  df,
  params_df,
  maxit = 500,
  independentFiltering = FALSE,
  fcThres = 1.5,
  minObs = 20,
  optim_fun_h0 = TPP2D:::.min_RSS_h0,
  optim_fun_h1 = TPP2D:::.min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  BPPARAM = BiocParallel::SerialParam(progressbar = TRUE),
  B = 20,
  byMsExp = TRUE,
  verbose = FALSE
)

Arguments

df

tidy data frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
params_df

data frame listing all null and alternative model parameters as obtained by 'getModelParamsDf'
maxit

maximal number of iterations the optimization should be given, default is set to 500
independentFiltering

boolean flag indicating whether independent filtering should be performed based on minimal fold changes per protein profile
fcThres

numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
minObs

numeric value of minimal number of observations that should be required per protein
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
BPPARAM

BiocParallel parameter for optional parallelization of null distribution generation through bootstrapping, default: BiocParallel::SerialParam()
B

numeric value of rounds of bootstrap, default: 20
byMsExp

boolean flag indicating whether resampling of residuals should be performed separately for data generated by different MS experiments, default TRUE, recommended
verbose

logical indicating whether to print each protein while its profile is boostrapped

Value

data frame containing F statistics of proteins with permuted 2D thermal profiles that are informative on the Null distribution of F statistics

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:3)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
temp_params_df <- getModelParamsDf(temp_df)
boot_df <- bootstrapNullAlternativeModelFast(
  temp_df, params_df = temp_params_df, B = 20)

Compete H0 and H1 models per protein and obtain F statistic

Description

Compete H0 and H1 models per protein and obtain F statistic

Usage

competeModels(
  df,
  fcThres = 1.5,
  independentFiltering = FALSE,
  minObs = 20,
  optim_fun_h0 = .min_RSS_h0,
  optim_fun_h1 = .min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  maxit = 750
)

Arguments

df

tidy data frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
fcThres

numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
independentFiltering

boolean flag indicating whether independent filtering should be performed based on minimal fold changes per protein profile
minObs

numeric value of minimal number of observations that should be required per protein
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
maxit

maximal number of iterations the optimization should be given, default is set to 500

Value

data frame summarising the fit characteristics of H0 and H1 models and therof resulting computed F statistics per protein

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:10)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
competeModels(temp_df)

Compute F statistic from H1 and H0 model characteristics

Description

Compute F statistic from H1 and H0 model characteristics

Usage

computeFstat(h0_df, h1_df)

Arguments

h0_df

data frame with H0 model characteristics for each protein
h1_df

data frame with H1 model characteristics for each protein

Value

data frame with H0 and H1 model characteristics for each protein and respectively computed F statistics

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:20)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
  
h0_df <- fitH0Model(temp_df)
h1_df <- fitH1Model(temp_df)
  
computeFstat(h0_df, h1_df)

Compute F statistics from paramter data frame

Description

Compute F statistics from paramter data frame

Usage

computeFStatFromParams(params_df)

Arguments

params_df

data frame listing all null and alternative model parameters as obtained by 'getModelParamsDf'

Value

data frame of all proteins and computed F statistics and parameters that were used for the computation

Examples

data("simulated_cell_extract_df")
params_df <- getModelParamsDf(simulated_cell_extract_df)
computeFStatFromParams(params_df)

Example config table for a import of a simulated 2D-TPP cell extract dataset

Description

Config table fot import of simulated example dataset obtained by 2D-TPP experiments for analysis by the TPP2D-package. It's a data frame with the columns "Compound" describing the compound used for the assay, "Experiment" listing MS experiment ids of the separate runs (typically comprising two multiplexed adjacent temperature), "Temperature": the temperature used for a given sub-experimet, the respective TMT labels "126"-"131L", RefCol referring to the label used as a reference label for computing relative fold changes (usually the label used for the control treatment). Please note that when the data is not supplied as a list of already imported data frames the config table for the import function should be a path to an txt, csv or xlsx file containing an additional column "Path" listing for each row the respective path to a searched protein output file.

Usage

data("config_tab")

Format

"Compound" describing the compound used for the assay, "Experiment" listing MS experiment ids of the separate runs (typically comprising two multiplexed adjacent temperature), "Temperature": the temperature used for a given sub-experimet, the respective TMT labels "126"-"131L", RefCol referring to the label used as a reference label for computing relative fold changes (usually the label used for the control treatment).

Tranform configuration table from wide to long

Description

Tranform configuration table from wide to long

Usage

configWide2Long(configWide)

Arguments

configWide

data frame containing a config table

Value

data frame containing config table in long format

Examples

data("config_tab")
configWide2Long(configWide = config_tab)

Filter out contaminants

Description

Filter out contaminants

Usage

filterOutContaminants(dataLong)

Arguments

dataLong

long format data frame of imported dataset

Value

data frame containing full dataset filtered to contain no contaminants

Examples

data("simulated_cell_extract_df")
filterOutContaminants(simulated_cell_extract_df)

Find hits according to FDR threshold

Description

Find hits according to FDR threshold

Usage

findHits(fdr_df, alpha)

Arguments

fdr_df

data frame obtained from computeFdr
alpha

significance threshold, default is set to 0.1

Value

data frame of significant hits at FDR <= alpha

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:5)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
example_out <- fitAndEvalDataset(temp_df)
example_null <- bootstrapNull(temp_df, B = 1)
fdr_df <- getFDR(example_out, example_null)
findHits(fdr_df, 0.1)

Fit H0 and H1 model to 2D thermal profiles of proteins and compute F statistic

Description

Fit H0 and H1 model to 2D thermal profiles of proteins and compute F statistic

Usage

fitAndEvalDataset(
  df,
  maxit = 500,
  optim_fun_h0 = .min_RSS_h0,
  optim_fun_h1 = .min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  ec50_lower_limit = NULL,
  ec50_upper_limit = NULL,
  slopEC50 = TRUE
)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
maxit

maximal number of iterations the optimization should be given, default is set to 500
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
ec50_lower_limit

lower limit of ec50 parameter
ec50_upper_limit

lower limit of ec50 parameter
slopEC50

logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures

Value

data frame with H0 and H1 model characteristics for each protein and respectively computed F statistics

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
fitAndEvalDataset(temp_df)

Fit H0 model and evaluate fit statistics

Description

Fit H0 model and evaluate fit statistics

Usage

fitH0Model(df, maxit = 500, optim_fun = .min_RSS_h0, gr_fun = NULL)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
maxit

maximal number of iterations the optimization should be given, default is set to 500
optim_fun

optimization function that should be used for fitting the H0 model
gr_fun

optional gradient function for optim_fun, default is NULL

Value

data frame with H0 model characteristics for each protein

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:5)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
  
fitH0Model(temp_df)

Fit H1 model and evaluate fit statistics

Description

Fit H1 model and evaluate fit statistics

Usage

fitH1Model(
  df,
  maxit = 500,
  optim_fun = .min_RSS_h1_slope_pEC50,
  optim_fun_2 = NULL,
  gr_fun = NULL,
  gr_fun_2 = NULL,
  ec50_lower_limit = NULL,
  ec50_upper_limit = NULL,
  slopEC50 = TRUE
)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
maxit

maximal number of iterations the optimization should be given, default is set to 500
optim_fun

optimization function that should be used for fitting the H0 model
optim_fun_2

optional secound optimization function for fitting the H1 model that should be used based on the fitted parameters of the optimizationfor based on optim_fun
gr_fun

optional gradient function for optim_fun, default is NULL
gr_fun_2

optional gradient function for optim_fun_2, default is NULL
ec50_lower_limit

lower limit of ec50 parameter
ec50_upper_limit

lower limit of ec50 parameter
slopEC50

logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures

Value

data frame with H1 model characteristics for each protein

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:5)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup
  
fitH1Model(temp_df)

Get FDR for given F statistics based on true and null dataset

Description

Get FDR for given F statistics based on true and null dataset

Usage

getFDR(df_out, df_null, squeezeDenominator = TRUE)

Arguments

df_out

data frame containing results from analysis by fitAndEvalDataset
df_null

data frame containing results from analysis by bootstrapNull
squeezeDenominator

logical indicating whether F statistic denominator should be shrinked using limma::squeezeVar

Value

data frame annotating each protein with a FDR based on it's F statistic and number of observations

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:5)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
example_out <- fitAndEvalDataset(temp_df)
example_null <- bootstrapNull(temp_df, B = 1)
getFDR(example_out, example_null)

Get H0 and H1 model parameters

Description

Get H0 and H1 model parameters

Usage

getModelParamsDf(
  df,
  minObs = 20,
  optim_fun_h0 = .min_RSS_h0,
  optim_fun_h1 = .min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  slopEC50 = TRUE,
  maxit = 500,
  qualColName = "qupm"
)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
minObs

numeric value of minimal number of observations that should be required per protein
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
slopEC50

logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures
maxit

maximal number of iterations the optimization should be given, default is set to 500
qualColName

name of column indicating quantification quality e.g. number of unique peptides used for quantification, default: "qupm"

Value

a data.frame with fitted null and alternative model parameters

Examples

data("simulated_cell_extract_df")
getModelParamsDf(simulated_cell_extract_df)

Get pEC50 for a protein of interest at a specific temperatures (optimally the melting point of the protein)

Description

Get pEC50 for a protein of interest at a specific temperatures (optimally the melting point of the protein)

Usage

getPEC504Temperature(fstat_df, protein, temperaturePEC50 = 60)

Arguments

fstat_df

data frame as obtained after calling getModelParamsDf, containing fitted null and alternative model parameters for each protein
protein

character string referring to the protein of interest
temperaturePEC50

temperature (numeric) at which pEC50 should be inferred

Value

numeric value specifying the pEC50 for the indicated protein and temperature

Examples

data("simulated_cell_extract_df")

model_params_df <- getModelParamsDf(
   df = filter(simulated_cell_extract_df, 
           clustername == "tp1"))

getPEC504Temperature(
    fstat_df = model_params_df, 
    protein = "tp1", 
    temperaturePEC50 = 60)

Compute p-values for given F statistics based on true and null dataset

Description

Compute p-values for given F statistics based on true and null dataset

Usage

getPvalues(df_out, df_null, pseudo_count = 1, squeezeDenominator = FALSE)

Arguments

df_out

data frame containing results from analysis by fitAndEvalDataset
df_null

data frame containing results from analysis by bootstrapNull
pseudo_count

numeric larger or equal to 0 added to both counts of protein with an F-statistic higher than a threshold theta of the true and bootstrapped datasets
squeezeDenominator

logical indicating whether F statistic denominator should be shrinked using limma::squeezeVar

Value

data frame annotating each protein with a FDR based on it's F statistic and number of observations

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:3)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
example_out <- fitAndEvalDataset(temp_df)
example_null <- bootstrapNull(temp_df, B = 2)
getPvalues(
  example_out, 
  example_null)

Plot qq-plot of true data and bootstrapped null with ggplot

Description

Plot qq-plot of true data and bootstrapped null with ggplot

Usage

gg_qq(
  x,
  y,
  xlab = "F-statistics from sampled Null distr.",
  ylab = "observed F-statistics",
  alpha = 0.25,
  gg_theme = theme_classic(),
  offset = 1,
  plot_diagonal = TRUE
)

Arguments

x

vector containing values of values of first distribution to compare
y

vector containing values of values of secound distribution to compare
xlab

x-axis label
ylab

y-axis label
alpha

transparency paramenter between 0 and 1
gg_theme

ggplot theme, default is theme_classic()
offset

offset for x and y axis on top of maximal values
plot_diagonal

logical parameter indicating whether an identity line should be plotted

Value

A ggplot displaying the qq-plot of a true and a a bootstrapped null distribution

Examples

data("simulated_cell_extract_df")
recomputeSignalFromRatios(simulated_cell_extract_df)

Import 2D-TPP dataset using a config table

Description

Import 2D-TPP dataset using a config table

Usage

import2dDataset(
  configTable,
  data,
  idVar = "representative",
  intensityStr = "sumionarea_protein_",
  fcStr = "rel_fc_protein_",
  nonZeroCols = "qssm",
  geneNameVar = "clustername",
  addCol = NULL,
  qualColName = "qupm",
  naStrs = c("NA", "n/d", "NaN"),
  concFactor = 1e+06,
  medianNormalizeFC = TRUE,
  filterContaminants = TRUE
)

Arguments

configTable

character string of a file path to a config table
data

possible list of datasets from different MS runs corresponding to a 2D-TPP dataset, circumvents loading datasets referencend in config table, default is NULL
idVar

character string indicating which data column provides the unique identifiers for each protein.
intensityStr

character string indicating which columns contain raw intensities measurements
fcStr

character string indicating which columns contain the actual fold change values. Those column names containing the suffix fcStr will be regarded as containing fold change values.
nonZeroCols

column like default qssm that should be imported and requested to be non-zero in analyzed data
geneNameVar

character string of the column name that describes the gene name of a given protein in the raw data files
addCol

character string indicating additional column to import
qualColName

character string indicating which column can be used for additional quality criteria when deciding between different non-unique protein identifiers.
naStrs

character vector indicating missing values in the data table. When reading data from file, this value will be passed on to the argument na.strings in function read.delim.
concFactor

numeric value that indicates how concentrations need to be adjusted to yield total unit e.g. default mmol - 1e6
medianNormalizeFC

perform median normalization (default: TRUE).
filterContaminants

boolean variable indicating whether data should be filtered to exclude contaminants (default: TRUE).

Value

tidy data frame representing a 2D-TPP dataset

Examples

data("config_tab")
data("raw_dat_list")
import_df <- import2dDataset(configTable = config_tab, 
                             data = raw_dat_list,
                             idVar = "protein_id",
                             intensityStr = "signal_sum_",
                             fcStr = "rel_fc_",
                             nonZeroCols = "qusm",
                             geneNameVar = "gene_name",
                             addCol = NULL,
                             qualColName = "qupm",
                             naStrs = c("NA", "n/d", "NaN"),
                             concFactor = 1e6,
                             medianNormalizeFC = TRUE,
                             filterContaminants = TRUE)

Import 2D-TPP dataset main function

Description

Import 2D-TPP dataset main function

Usage

import2dMain(
  configTable,
  data,
  idVar,
  fcStr,
  addCol,
  naStrs,
  intensityStr,
  qualColName,
  nonZeroCols
)

Arguments

configTable

character string of a file path to a config table
data

possible list of datasets from different MS runs corresponding to a 2D-TPP dataset, circumvents loading datasets referencend in config table, default is NULL
idVar

character string indicating which data column provides the unique identifiers for each protein.
fcStr

character string indicating which columns contain the actual fold change values. Those column names containing the suffix fcStr will be regarded as containing fold change values.
addCol

character string indicating additional column to import
naStrs

character vector indicating missing values in the data table. When reading data from file, this value will be passed on to the argument na.strings in function read.delim.
intensityStr

character string indicating which columns contain raw intensities measurements
qualColName

character string indicating which column can be used for additional quality criteria when deciding between different non-unique protein identifiers.
nonZeroCols

column like default qssm that should be imported and requested to be non-zero in analyzed data

Value

list of data frames containing different datasets

Examples

data("config_tab")
data("raw_dat_list")
dataList <- import2dMain(configTable = config_tab,
                         data = raw_dat_list,
                         idVar = "protein_id",
                         fcStr = "rel_fc_",
                         addCol = "gene_name",
                         naStrs = NA,
                         intensityStr = "signal_sum_",
                         nonZeroCols = "qusm",
                         qualColName = "qupm")

Plot heatmap of 2D thermal profile fold changes of a protein of choice

Description

Plot heatmap of 2D thermal profile fold changes of a protein of choice

Usage

plot2dTppFcHeatmap(df, name, drug_name = "", midpoint = 1)

Arguments

df

tidy data frame of a 2D-TPP dataset
name

gene name (clustername) of protein that should be visualized
drug_name

character string of profiled drug name
midpoint

midpoint of fold changes for color scaling, default: 1

Value

A ggplot displaying the thermal profile as a heatmap of fold changes of a protein of choice in a dataset of choice

Examples

data("simulated_cell_extract_df")
plot2dTppFcHeatmap(simulated_cell_extract_df, 
 "tp2", drug_name = "drug1")

Plot H0 or H1 fit of 2D thermal profile intensities of a protein of choice

Description

Plot H0 or H1 fit of 2D thermal profile intensities of a protein of choice

Usage

plot2dTppFit(
  df,
  name,
  model_type = "H0",
  optim_fun = .min_RSS_h0,
  optim_fun_2 = NULL,
  maxit = 500,
  xlab = "-log10(conc.)",
  ylab = "log2(summed intensities)",
  dot_size = 1,
  line_type = "solid",
  fit_color = "gray30"
)

Arguments

df

tidy data frame of a 2D-TPP dataset
name

gene name (clustername) of protein that should be visualized
model_type

character string indicating whether the "H0" or the "H1" model should be fitted
optim_fun

optimization function that should be used for fitting either the H0 or H1 model
optim_fun_2

optional additional optimization function that will be run with paramters retrieved from optim_fun and should be used for fitting the H1 model with the trimmed sum model, default is NULL
maxit

maximal number of iterations the optimization should be given, default is set to 500
xlab

character string of x-axis label of plot
ylab

character string of y-axis label of plot
dot_size

numeric indicating the size of the data points to plot
line_type

character string defining the line type of the fitted curve, default "dashed"
fit_color

character string defining the color of the fitted curve

Value

A ggplot displaying the thermal profile of a protein of choice in a datset of choice

Examples

data("simulated_cell_extract_df")
plot2dTppProfile(simulated_cell_extract_df, "protein1")

Plot 2D thermal profile intensities of a protein of choice

Description

Plot 2D thermal profile intensities of a protein of choice

Usage

plot2dTppProfile(df, name)

Arguments

df

tidy data frame of a 2D-TPP dataset
name

gene name (clustername) of protein that should be visualized

Value

A ggplot displaying the thermal profile of a protein of choice in a datset of choice

Examples

data("simulated_cell_extract_df")
plot2dTppProfile(simulated_cell_extract_df, "protein1")

Plot 2D thermal profile ratios of a protein of choice

Description

Plot 2D thermal profile ratios of a protein of choice

Usage

plot2dTppRelProfile(df, name)

Arguments

df

tidy data frame of a 2D-TPP dataset
name

gene name (clustername) of protein that should be visualized

Value

A ggplot displaying the thermal profile ratios of a protein of choice in a datset of choice

Examples

data("simulated_cell_extract_df")
plot2dTppRelProfile(simulated_cell_extract_df, "protein1")

Plot Volcano plot of TPP2D results

Description

Plot Volcano plot of TPP2D results

Usage

plot2dTppVolcano(
  fdr_df,
  hits_df,
  alpha = 0.5,
  title_string = "",
  x_lim = NULL,
  y_lim = NULL,
  facet_by_obs = FALSE
)

Arguments

fdr_df

data frame obtained from 'getFDR'
hits_df

hits_df data frame obtained from 'findHits'
alpha

transparency level of plotted points
title_string

character argument handed over to ggtitle
x_lim

vector with two numerics indicating the x axis limits
y_lim

vector with two numerics indicating the y axis limits
facet_by_obs

logical indicating whether plot should be facetted by number of observations, default: FALSE

Value

a ggplot displaying a volcano plot of the results obtained after a TPP2D analysis

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>%
  filter(clustername %in% paste0("protein", 1:5)) %>%
  group_by(representative) %>%
  mutate(nObs = n()) %>%
  ungroup
example_params <- getModelParamsDf(temp_df)
example_fstat <- computeFStatFromParams(example_params)
example_null <- bootstrapNullAlternativeModel(
   df = temp_df, params_df = example_params,
   B = 2)
fdr_df <- getFDR(example_fstat, example_null)
hits_df <- findHits(fdr_df, 0.1)
plot2dTppVolcano(fdr_df = fdr_df, hits_df = hits_df)

Example raw data for a subset of a simulated 2D-TPP cell extract dataset

Description

Simulated example dataset obtained by 2D-TPP experiments for analysis by the TPP2D-package. It contains a list of data frames resembling raw data files returned from a MS database search with 200 simulated protein profiles (protein1-200) and 3 spiked-in true positives (TP1-3).

Usage

data("raw_dat_list")

Format

list of data frames with columns representative (protein id), clustername (gene name), temperature, log_conc, raw_value, rel_value, value and log2_value

Recompute robust signal intensities based on bootstrapped TMT channel ratios

Description

Recompute robust signal intensities based on bootstrapped TMT channel ratios

Usage

recomputeSignalFromRatios(df)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset

Value

A data_frame with recomputed signal intensities (columname: value) and log2 transformed signal intensities (columnanme: log2_value) that more reliably reflect relative ratios between the TMT channels

Examples

data("simulated_cell_extract_df")
recomputeSignalFromRatios(simulated_cell_extract_df)

Rename columns of imported data frame

Description

Rename columns of imported data frame

Usage

renameColumns(dataLong, idVar, geneNameVar)

Arguments

dataLong

long format data frame of imported dataset
idVar

character string indicating which data column provides the unique identifiers for each protein.
geneNameVar

character string of the column name that describes the gene name of a given protein in the raw data files

Value

data frame containing imported data with renamed columns

Examples

data("config_tab")
data("raw_dat_list")

dataList <- import2dMain(configTable = config_tab,
                         data = raw_dat_list,
                         idVar = "protein_id",
                         fcStr = "rel_fc_",
                         addCol = "gene_name",
                         naStrs = NA,
                         intensityStr = "signal_sum_",
                         nonZeroCols = "qusm",
                         qualColName = "qupm")
configLong <- configWide2Long(configWide = config_tab)
annoDat <- annotateDataList(dataList = dataList,
                            geneNameVar = "gene_name",
                            configLong = configLong,
                            intensityStr = "signal_sum_",
                            fcStr = "rel_fc_")
renameColumns(annoDat, 
              idVar = "protein_id", 
              geneNameVar = "gene_name")

Resolve ambiguous protein names

Description

Resolve ambiguous protein names

Usage

resolveAmbiguousProteinNames(df, includeIsoforms = FALSE)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset
includeIsoforms

logical indicating whether protein isoform should be kept for analysis

Value

data frame with resolved protein name ambiguity

Examples

tst_df <- bind_rows(tibble(representative = rep(1:3, each = 3), 
                           clustername = rep(letters[1:3], each = 3)), 
                    tibble(representative = rep(c(4, 5), c(3, 2)), 
                           clustername = rep(c("a", "b"), c(3, 2))))
                           
resolveAmbiguousProteinNames(tst_df)

Run complete TPP2D analysis

Description

Run complete TPP2D analysis

Usage

runTPP2D(
  df = NULL,
  configTable = NULL,
  data = NULL,
  idVar = "protein_id",
  intensityStr = "signal_sum_",
  fcStr = "rel_fc_",
  nonZeroCols = "qusm",
  geneNameVar = "gene_name",
  addCol = NULL,
  qualColName = "qupm",
  naStrs = c("NA", "n/d", "NaN"),
  concFactor = 1e+06,
  medianNormalizeFC = TRUE,
  filterContaminants = TRUE,
  recomputeSignalRatios = FALSE,
  minObs = 20,
  independentFiltering = FALSE,
  fcThres = 1.5,
  optim_fun_h0 = .min_RSS_h0,
  optim_fun_h1 = .min_RSS_h1_slope_pEC50,
  optim_fun_h1_2 = NULL,
  gr_fun_h0 = NULL,
  gr_fun_h1 = NULL,
  gr_fun_h1_2 = NULL,
  slopEC50 = TRUE,
  maxit = 750,
  BPPARAM = BiocParallel::SerialParam(progressbar = TRUE),
  B = 20,
  byMsExp = TRUE,
  alpha = 0.1
)

Arguments

df

tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
configTable

character string of a file path to a config table
data

possible list of datasets from different MS runs corresponding to a 2D-TPP dataset, circumvents loading datasets referencend in config table, default is NULL
idVar

character string indicating which data column provides the unique identifiers for each protein.
intensityStr

character string indicating which columns contain raw intensities measurements
fcStr

character string indicating which columns contain the actual fold change values. Those column names containing the suffix fcStr will be regarded as containing fold change values.
nonZeroCols

column like default qssm that should be imported and requested to be non-zero in analyzed data
geneNameVar

character string of the column name that describes the gene name of a given protein in the raw data files
addCol

character string indicating additional column to import
qualColName

character string indicating which column can be used for additional quality criteria when deciding between different non-unique protein identifiers.
naStrs

character vector indicating missing values in the data table. When reading data from file, this value will be passed on to the argument na.strings in function read.delim.
concFactor

numeric value that indicates how concentrations need to be adjusted to yield total unit e.g. default mmol - 1e6
medianNormalizeFC

perform median normalization (default: TRUE).
filterContaminants

logical variable indicating whether data should be filtered to exclude contaminants (default: TRUE).
recomputeSignalRatios

logical variable indicaiting whether signals should be recomputed from relative fold changes, recommended if Isobarquant was used for protein quantification
minObs

number of minimal observations per protein to include it in the analysis
independentFiltering

logical variable indicating whether independent filtering should be performed based on minimal fold changes per protein profile
fcThres

numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
optim_fun_h0

optimization function that should be used for fitting the H0 model
optim_fun_h1

optimization function that should be used for fitting the H1 model
optim_fun_h1_2

optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
gr_fun_h0

optional gradient function for optim_fun_h0, default is NULL
gr_fun_h1

optional gradient function for optim_fun_h1, default is NULL
gr_fun_h1_2

optional gradient function for optim_fun_h1_2, default is NULL
slopEC50

logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures
maxit

maximal number of iterations the optimization should be given, default is set to 500
BPPARAM

= BiocParallel::SerialParam(progressbar = TRUE),
B

numeric value indicating number of rounds of bootstraps that should be performed to estimate the null distribution
byMsExp

logical indicating whether bootstrapping should be performed within MS experiments
alpha

FDR level that should be controlled

Value

a tpp2dExperiment object

Examples

data("simulated_cell_extract_df")
runTPP2D(df = simulated_cell_extract_df %>% 
   filter(representative %in% 1:3),
   B = 1)

Example subset of a simulated 2D-TPP cell extract dataset

Description

Simulated example dataset obtained by 2D-TPP experiments for analysis by the TPP2D-package. It contains a tidy data frame after import and recomputing of robust signal intensities with 200 simulated protein profiles (protein1-200) and 3 spiked-in true positives (TP1-3)

Usage

data("simulated_cell_extract_df")

Format

data frame with columns representative (protein id), clustername (gene name), temperature, log_conc, raw_value, rel_value, value and log2_value

Import and chech configuration table

Description

Import and chech configuration table

Usage

TPP_importCheckConfigTable(infoTable, type = "2D")

Arguments

infoTable

character string of a file path to a config table (excel,txt or csv file) or data frame containing a config table
type

charater string indicating dataset type default is 2D

Value

data frame with config table

Examples

data("config_tab")
TPP_importCheckConfigTable(config_tab, type = "2D")

S4 TPP2D Experiment Class

Description

S4 TPP2D Experiment Class

Value

an object of class tpp2dExperiment

Slots

configTable

data.frame.

idVar

character.

intensityStr

character.

fcStr

character.

nonZeroCols

character.

geneNameVar

character.

qualColName

character.

naStrs

character.

concFactor

numeric.

medianNormalizeFC

logical.

filterContaminants

logical.

minObs

numeric.

independentFiltering

logical.

fcThres

numeric.

optim_fun_h0

function.

optim_fun_h1

function.

slopEC50

logical.

maxit

numeric.

BPPARAM

character.

B

numeric

byMsExp

logical.

alpha

numeric.

tidyDataTable

data.frame.

modelParamsDf

data.frame

resultTable

data.frame

bootstrapNullDf

data.frame

hitTable

data.frame

Examples

tpp2dObj <- new("tpp2dExperiment")