README.md

In frhl/genoppi-v4: genoppi

NOTE: This is a legacy version and is out of date. See https://github.com/lagelab/Genoppi for the newest version.

Overview

Genoppi is an open-source software for performing quality control and analyzing quantitative proteomic data. In particular, it streamlines the integration of proteomic data with external datasets such as known protein-protein interactions in published literature, data from genetic studies, gene set annotations, or other user-defined inputs.

This README provides instructions for locally installing the Genoppi software in R (>= 3.6), which consists of two main components: an R package and an interactive shiny application. The application is also available remotely at http://www.lagelab.org/genoppi/.

In addition, we provide a welcome guide to describe the user interface of the application. The guide also describes the accepted format of various input files.

Installation

# download and install Genoppi using the devtools package:
install.packages("devtools")
library(devtools)
devtools::install_github('frhl/genoppi-v4') # UPDATE WHEN UPLOAD TO LAGELAB

Launching shiny application

library(genoppi)
launch_genoppi()

R package usage example

library(genoppi)

# load example proteomic data
data("example_data")


### ------------------------------------------------------------------
### (1) Basic analyses

# perform moderated t-test
stats_df <- calc_mod_ttest(example_data)

# identify enriched proteins
sig_df <- id_enriched_proteins(stats_df)

# generate volcano plot with bait protein labeled
basic_volcano <- plot_volcano_basic(sig_df)
bait_volcano <- plot_overlay(basic_volcano,as.bait('BCL2'))
print(bait_volcano)

# generate correlation scatter plot for two replicates
basic_scatter <- plot_scatter_basic(sig_df,"rep1","rep2")
bait_scatter <- plot_overlay(basic_scatter,as.bait('BCL2'))
print(bait_scatter)

# NOTE: the piping (%>%) command can be used to streamline steps, e.g.: 
example_data %>%
  calc_mod_ttest() %>%
  id_enriched_proteins() %>%
  plot_volcano_basic() %>%
  plot_overlay(as.bait('BCL2'))


# interactive volcano plot
make_interactive(bait_volcano) %>%
  add_layout_html_axes_volcano()


### ------------------------------------------------------------------
### (2) Integrated analyses (using InWeb data as example)

# query InWeb interactors for a bait protein (e.g. BCL2)
inweb_df <- data.frame(listName="InWeb",get_inweb_list('BCL2'))

# overlaid volcano plot labeling InWeb interactors
inweb_list <- list(InWeb=inweb_df[inweb_df$significant, ])
plot_overlay(bait_volcano,inweb_list)

# assess overlap b/w enriched proteins and InWeb interactors
overlap_results <- calc_hyper(sig_df, inweb_df,
  data.frame(listName="InWeb",intersectN=T), bait='BCL2')

# Venn diagram of overlap
venn_list <- list(Enriched=overlap_results$genes$InWeb$success_genes,
  InWeb=overlap_results$genes$InWeb$sample_genes)
venn_diagram <- draw_genoppi_venn(venn_list)
library(grid)
grid.newpage()
grid.draw(venn_diagram)

frhl/genoppi-v4 documentation built on May 5, 2020, 7:16 a.m.