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pKSEA  

Prediction-Based Kinase-Substrate Enrichment Analysis
View on CRAN: Click here


Download and install pKSEA package within the R console
Install from CRAN:
install.packages("pKSEA")

Install from Github:
library("remotes")
install_github("cran/pKSEA")

Install by package version:
library("remotes")
install_version("pKSEA", "0.0.1")



Attach the package and use:
library("pKSEA")
Maintained by
Peter Liao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-12-22
Latest Update: 2017-12-22
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) Horn, et. al (2014) and of known kinase-substrate links from 'PhosphoSitePlus' (KSEAdb) Hornbeck PV, et. al (2015) .
How to cite:
Peter Liao (2017). pKSEA: Prediction-Based Kinase-Substrate Enrichment Analysis. R package version 0.0.1, https://cran.r-project.org/web/packages/pKSEA. Accessed 07 Nov. 2024.
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Complete documentation for pKSEA
Functions, R codes and Examples using the pKSEA R package
Some associated functions: KSEAdb . KSEAfilter . NetworKINPred_db . batchrun . calc_contribution . compare . get_matched_data . getscores . getsubs . mk_runlabel . perc.permutation . perc.rank . permtest . results_write . run_on_matched . 
Some associated R codes: datadoc.R . internal_functions.R . user_level_functions.R .  Full pKSEA package functions and examples
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