Tools for visualization of results from top-down proteomics studies of prefractionated biological samples. Based on novel visualizations developed for evaluation of the PEPPI-MS prefractionation method. Also suitable for visualization of samples fractionated using GELFrEE or comparison of biological or technical replicates.
Input files for make_UpSet_plot() and make_intersection_degree_plot() should have column names corresponding to fraction/replicate designations and row values corresponding to unique protein/proteoform identifiers, e.g. UniProt accession numbers or CTDP proteoform record numbers.
An input file for make_heatmap() should have a column providing molecular weights and a column providing the fraction/replicate number. Default column names are “mass” and “fraction” but can be specified in the function arguments.
An input file for waffle_iron() should have a column providing the fraction/replicate number and columns providing subcellular localization counts. Column names are used for legend labels, so I recommend naming them “Cytosol”, “Membrane”, etc.
Example input files for each visualization type can be found in the extdata folder in the package directory.
Load an input spreadsheet file as an R object using an appropriate function, e.g. readxl::read_xlsx() for XLSX files or readr::read_csv() for CSV files. Then, pass the object to the appropriate visualization function:
# Read an XLSX
df <-
readxl::read_xlsx(
"C:\Users\YourName\Documents\protein_data.xlsx"
)
# Read a CSV
df <-
readr::read_csv(
"C:\Users\YourName\Documents\protein_data.csv"
)
# Use data frame as argument for a visualization function
make_UpSet_plot(df)Plots created using viztools can be saved by setting the argument savePDF = TRUE:
UpSetplot <- make_UpSet_plot(df, savePDF = TRUE)
With the exception of UpSet plots, they can also be saved using the ggplot2::ggsave() function:
make_heatmap(df) ggplot2::ggsave( "heatmap.png", dpi = 300, height = 5, width = 8 )
viztools utilizes the package UpSetR for generating UpSet plots and waffle for generating Waffle plots. Other visualizations are generated using ggplot2. Additional functions are imported from dplyr, tibble, purrr, glue, tidyr, magrittr, assertthat, and scales.
Package developed by David S. Butcher and licensed under CC BY 4.0. Imported packages are licensed separately.