| Type: | Package |
| Title: | Prediction-Based Kinase-Substrate Enrichment Analysis |
| Version: | 0.0.1 |
| Description: | A tool for inferring kinase activity changes from phosphoproteomics data. 'pKSEA' uses kinase-substrate prediction scores to weight observed changes in phosphopeptide abundance to calculate a phosphopeptide-level contribution score, then sums up these contribution scores by kinase to obtain a phosphoproteome-level kinase activity change score (KAC score). 'pKSEA' then assesses the significance of changes in predicted substrate abundances for each kinase using permutation testing. This results in a permutation score (pKSEA significance score) reflecting the likelihood of a similarly high or low KAC from random chance, which can then be interpreted in an analogous manner to an empirically calculated p-value. 'pKSEA' contains default databases of kinase-substrate predictions from 'NetworKIN' (NetworKINPred_db) http://networkin.info Horn, et. al (2014) <doi:10.1038/nmeth.2968> and of known kinase-substrate links from 'PhosphoSitePlus' (KSEAdb) https://www.phosphosite.org/ Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>. |
| Depends: | R (≥ 3.3.0) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| Maintainer: | Peter Liao <pll21@case.edu> |
| NeedsCompilation: | no |
| Packaged: | 2017-12-21 19:56:06 UTC; pll21 |
| Author: | Peter Liao [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2017-12-22 18:46:08 UTC |
KSEAdb
Description
A data table containing all known kinase-substrate links known in PhosphoSitePlus.
Usage
KSEAdb
Format
An object of class data.frame with 240749 rows and 6 columns.
Source
https://www.phosphosite.org/staticDownloads.action
References
Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015 43:D512-20.
Filter matched data to remove positive IDs from KSEA
Description
Filter matched data to remove positive IDs from KSEA
Usage
KSEAfilter(matched_data, kseadb, reverse = F)
Arguments
matched_data |
Results of get_matched_data function |
kseadb |
KSEA database containing substrate gene name "SUB_GENE" and phosphorylated residue "SUB_MOD_RSD" in standard form (ie. T302). |
Value
Result of get_matched_data function with features existing in KSEA database removed.
NetworKINPred_db
Description
A data table containing all precalculated NetworKIN predictions performed on known ensembl sequences.
Usage
NetworKINPred_db
Format
An object of class data.frame with 450418 rows and 4 columns.
Source
http://networkin.info/download.shtml
References
Horn et al., KinomeXplorer: an integrated platform for kinome biology studies. Nature Methods 2014 Jun;11(6):603–4.
Running pKSEA::compare() on multiple files
Description
For running compare() on multiple CSV data files in the same directory and for writing results to a folder in the
designated data directory. Can receive various arguments to be passed on to downstream functions. Writes to tempdir()
unless outputpath variable is specified by user (argument passed on to results_write).
Usage
batchrun(summaryfiledir, commonfilestring = ".csv",
predictionDB, results_folder = NULL, ...)
Arguments
summaryfiledir |
Directory containing summary statistic CSV files. Required data file columns: GN = gene name identifier that will be matched with prediction database, Peptide = unique peptide identifier (for example, sequence with modifications), Phosphosites = comma-separated phosphorylation sites (eg. "T102,S105"), pval= pairwise test p-value, fc= mean fold change, t= pairwise test t-statistic. pval and fc are used for results reporting only, all others are important for database searching, calculation, and permutation testing. |
commonfilestring |
Common string identifying all files to be included in analysis |
predictionDB |
Input database whose prediction scores will be used for calculations. Required columns: substrate_name= name of substrate corresponding to GN in summary_data, kinase_id = identifiers for kinase predictors, position= phosphorylated residue number, score = numeric score for strength of prediction. |
results_folder |
if desired, a single output folder. Else each run performed on each file will have a separate output folder identified by run initiation time. |
... |
parameters to be passed on to downstream functions, including(default): outputpath (tempdir())
n_permutations (1000), seed (123), kseadb (NULL), kin_ens_table (NULL).
See |
Examples
#point to data directory that contains summary .csv files
datapath <- system.file("extdata", package = "pKSEA")
#run batchrun function to analyze all files in that folder, with options
batchrun(datapath, predictionDB=NetworKINPred_db, kseadb = KSEAdb, n_permutations = 5)
Calculate score contributions by phosphorylation site
Description
Calculate score contributions by phosphorylation site
Usage
calc_contribution(matched_data)
Arguments
matched_data |
Input |
Value
matched_data with contribution scores calculated
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
#Calculate contributions
calc_ex <- calc_contribution(matched_data_ex)
Running analysis runs on known substrates, predicted substrates, and both.
Description
Performs up to three run_on_matched() runs on summary-prediction matcheddata from get_matched_data(),
returning permutation significance score results.
If a KSEA database is provided for filtering and comparison, one full analysis will be performed on all
phosphosites, one on data with all known kinase substrates removed according to the provided KSEA database,
and one on known kinase substrates only.
Usage
compare(matched_data, predictionDB, kseadb, ...)
Arguments
matched_data |
File path to summary statistic phosphoproteomics CSV data file with an entry for each phosphopeptide. Required data file columns: GN = gene name identifier that will be matched with prediction database, Peptide = unique peptide identifier (for example, sequence with modifications), Phosphosites = comma-separated phosphorylation sites (eg. "T102,S105"), pval= pairwise test p-value, fc= mean fold change, t= pairwise test t-statistic. pval and fc are used for results reporting only, all others are important for database searching, calculation, and permutation testing. |
predictionDB |
Input database whose prediction scores will be used for calculations. Required columns: substrate_name= name of substrate corresponding to GN in summary_data, kinase_id = identifiers for kinase predictors, position= phosphorylated residue number, score = numeric score for strength of prediction. |
kseadb |
Optional KSEA database for filtering purposes. Containing substrate gene name "SUB_GENE" and phosphorylated residue "SUB_MOD_RSD" in standard form (ie. T302). |
... |
optional parameters to be passed on to downstream functions, including (default):
n_permutations (1000), seed (123), kin_ens_table (NULL). See |
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
#Perform comparative analysis using provided KSEAdb as filter
## Not run:
compare_results_ex <- compare(matched_data_ex, kseadb = KSEAdb, n_permutations = 10)
## End(Not run)
Filtering data to matched predictions
Description
This function reformats summary statistic phosphoproteomicdata to single observations for each phosphorylation site, duplicating other fields for multiple sites on the same peptide. Next, it attempts to find predictions for each phosphorylation site in the provided database. It returns observations (phosphorylation sites) for which a prediction is detected in the database, matching based on HUGO gene name and phosphorylated residue.
Usage
get_matched_data(datafull, predictionDB)
Arguments
datafull |
Statistical summary data with an entry for each phosphopeptide. Required columns: GN = gene name identifier that will be matched with prediction database, Peptide = unique peptide identifier (for example, sequence with modifications), Phosphosites = comma-separated phosphorylation sites (eg. "T102,S105"), pval= pairwise test p-value, fc= mean fold change, t= pairwise test t-statistic. pval and fc are used for results reporting only, all others are important for database searching, calculation, and permutation testing. |
predictionDB |
Input database whose prediction scores will be used for calculations. Required columns: substrate_name= name of substrate corresponding to GN in datafull, kinase_id = identifiers for kinase predictors, position= phosphorylated residue number, score = numeric score for strength of prediction. |
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
Sum score contributions for each kinase across all phosphopeptides
Description
Sum score contributions for each kinase across all phosphopeptides
Usage
getscores(matched_data)
Arguments
matched_data |
Input with calculated contributions |
Value
A dataframe with each kinase as a row and raw kinase activity change score (KAC) calculated
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
#Calculate contributions
calc_ex <- calc_contribution(matched_data_ex)
#Calculate kinase activity change scores
kac_ex <- getscores(calc_ex)
Extract summary table with pertinent columns related to included substrates
Description
Extract summary table with pertinent columns related to included substrates
Usage
getsubs(matched_data)
Arguments
matched_data |
Input with calculated contributions |
mk_runlabel()
Description
Utility function for generating new identifiers for each run, labeled by time run was initiated and custom suffix
Usage
mk_runlabel(parentdir = tempdir(), customsuffix)
Arguments
parentdir |
parent directory |
customsuffix |
additional suffix to run identifier |
Get percentile ranks across permutations
Description
Get percentile ranks across permutations
Usage
perc.permutation(results, permutations)
Arguments
results |
Results of kinase scoring |
permutations |
Results of permutations |
Obtain percentile rank comparing a single value to set
Description
Obtain percentile rank comparing a single value to set
Usage
perc.rank(set, value)
Arguments
set |
Set of values to which given value will be compared |
value |
Value for which percentile score will be calculated |
Perform permutation test
Description
Returns a table that has permuted the relationship between phosphopeptides and summary statistics (ie. fold change, t-score)
Usage
permtest(matched_data, perms = 1000, seed = 1)
Arguments
matched_data |
Input with calculated contributions |
perms |
Number of permutations to run, default = 1000 |
Value
dataframe with kinases as rows, each column as KAC scores calculated from one permutation
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
#Calculate contributions
calc_ex <- calc_contribution(matched_data_ex)
#Perform 5 permutations
perm_ex <- permtest(calc_ex, perms= 5, seed= 123)
Output writing of pKSEA compare() results
Description
Output only: uses results from compare(), outputs up to three files labeled full.csv and no_ksea.csv and ksea_only.csv appended to an output name (KSEA-filtered results only if KSEA database was provided to compare()).
Usage
results_write(full_ksea.results, outputpath = tempdir(), outputname,
singlefolder = NULL)
Arguments
full_ksea.results |
results from compare() including full and optional KSEA excluded and exclusive results |
outputpath |
parent directory for output, defaults to tempdir() unless defined by user |
outputname |
file name of output |
singlefolder |
if desired, name of output folder within output directory. Default is separate folders for each compare() run |
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
#Perform single run of pKSEA analysis
single_run_results_ex <- run_on_matched(matched_data_ex, n_permutations = 10)
#Export results to R session temporary directory
## Not run:
results_write(single_run_results_ex, outputpath= tempdir(), outputname= "example")
## End(Not run)
Runs pKSEA analysis on a dataset result from get_matched_data.
Description
Calculates score contributions from summary statistics (tscore) and prediction scores, and sums contribution scores by kinase to calculate raw kinase activity change scores (KAC scores). Performs permutation test on summary statistic data to assess significance of kinase activity change scores, and reports significance as a percentile score (pKSEA significance score).
Usage
run_on_matched(matched_data, n_permutations = 1000, seed = 123,
kin_ens_table = NULL)
Arguments
matched_data |
data after filtering against predictions (results from get_matched_data()) |
n_permutations |
number of mutations to perform (default 1000) |
seed |
seed used for permutation testing |
kin_ens_table |
optional table for inclusion of matched ensembl ids for kinases, with columns: ens = ensembl id, kinases = kinase_id as otherwise used |
Examples
#Read in example summary statistics dataset from csv
summarydata_ex <- read.csv(system.file("extdata", "example_data1.csv", package="pKSEA"))
#Get matched data using predictions from NetworKIN
matched_data_ex <- get_matched_data(summarydata_ex, NetworKINPred_db)
#Perform single run of pKSEA analysis
single_run_results_ex <- run_on_matched(matched_data_ex, n_permutations = 10)