chuanping-zhao / ksear

KSEAr

KSEAr is an R package developed as an adaptation of the KSEA Shiny app, originally created by casecpb. This version has been transformed from a Shiny application into a set of R functions for batch processing, making it more suitable for automated workflows and large-scale analyses.

Acknowledgments

We would like to acknowledge the original work done by casecpb in the KSEA project. The source code and example data in KSEAr have been adapted from the KSEA Shiny app, and we are thankful to the original authors for their foundational contributions to the field.

Key Modifications

• Conversion to R Functions: The original Shiny app interface has been restructured into R functions, allowing for direct and flexible scripting in R. This change facilitates batch processing and integration into automated data analysis pipelines.
• Updated Visualization Style: We have updated the visual presentation of the KSEA results, offering a more contemporary and insightful visualization approach that enhances the interpretability of the analysis.

Purpose

KSEAr is designed to perform Kinase-Substrate Enrichment Analysis (KSEA) in a more programmatically accessible manner, making it easier to integrate KSEA into larger bioinformatics workflows and to perform the analysis on a larger scale.

How to Install

install.packages("ggplot2")# ggplot2 v3.5.0
install.packages("remotes")
remotes::install_github("Chuanping-Zhao/KSEAr")

use

rm(list = ls())
library(tidyverse)
library(KSEAr)

#loading data
files_input <- fs::dir_ls("inputfile",recurse = TRUE,glob = "*.csv")
input <-  map_dfr(files_input,read_csv)
head(input,3)

#A tibble: 3 × 6
  Protein Gene  Peptide           Residue.Both     p    FC
  <chr>   <chr> <chr>             <chr>        <dbl> <dbl>
1 Q15019  SEPT2 IYHLPDAESDEDEDFK  S218         0.324 1.06 
2 Q9UHD8  SEPT9 RSFEVEEVETPNSTPPR S30          0.258 1.43 
3 Q9UHD8  SEPT9 SFEVEEVETPNSTPPRR T42;S30      0.297 0.630

result <- KSEA(input_data = input,#a dataframe
               networkin_score_cutoff = 1,# The filter score of networkin datsets(if selected)
               substrate_count_cutoff = 4,# The counts of substrate
               threshold_type = "fdr",# The threshold used: 'fdr' or 'p'
               threshold_cutoff = 0.05, The cutoff of 'fdr' or 'p' 
               dataset = "PSP_networkin")#datasets:'PSP_networkin' or 'PSP'
enrichresults <- result[[1]]#resulst
enrich_plot <- result[[2]]#plotting