pKSEA (version 0.0.1)

batchrun: 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 run_on_matched, compare for details.

Examples

# NOT RUN {
#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)
# }