proteoDA

proteoDA is a streamlined, user-friendly R package for the analysis of high resolution mass spectrometry protein data. The package uses a custom S3 class that keeps the R objects consistent across the pipeline and is easily pipe-able, so minimal R knowledge is required.

Installation

proteoDA is not yet on CRAN, but it is available for install from GitHub via the devtools package. Install devtools if you haven’t already:

install.packages("devtools")

Then install proteoDA:

devtools::install_github("ByrumLab/proteoDA", 
                         dependencies = TRUE, 
                         build_vignettes = TRUE)

Using the build_vignettes = TRUE argument will build the tutorial vignette when you install, which you can access by running browseVignettes(package = "proteoDA"). However, building the vignettes requires some additional software dependencies. If you run into issues when installing the vignettes, you can set build_vignettes = FALSE and find a pre-built .html version of the tutorial in the vignettes folder on GitHub.

Once proteoDA is installed, load it into R:

library(proteoDA)

Workflow

proteoDA workflow
flowchart

Example pipeline

Here’s an example pipeline, going from data import to final results. For a detailed explanation of the pipeline, check out the tutorial vignette.

# Load data
input_data <- read.csv(system.file("extdata/DIA_data.csv.gz", package = "proteoDA"))
sample_metadata <- read.csv(system.file("extdata/metafile.csv", package = "proteoDA"))

# Split input data into protein intensity data and annotation data
intensity_data <- input_data[,5:21] # select columns 5 to 21
annotation_data <- input_data[,1:4] # select columns 1 to 4
# Match up row names of metadata with column names of data
rownames(sample_metadata) <- sample_metadata$data_column_name

# Assemble into DAList
raw <- DAList(data = intensity_data,
              annotation = annotation_data,
              metadata = sample_metadata)

# Filter out unneeded samples and proteins with too much missing data
filtered <- raw |>
  filter_samples(group != "Pool") |>
  zero_to_missing() |>
  filter_proteins_by_proportion(min_prop = 0.66,
                                grouping_column = "group")
# Make the normalization report
write_norm_report(filtered,
                  grouping_column = "group")

# Normalize
normalized <- normalize_data(filtered, 
                             norm_method = "cycloess")

# Make the quality control report
write_qc_report(normalized,
                color_column = "group")

# Turn metadata column into a factor with desired levels
normalized$metadata$group <- factor(normalized$metadata$group, 
                                    levels = c("normal", "cancer"))

# Add a statistical design, fit the model, and extract results
final <- normalized |>
  add_design(design_formula = ~ group) |>
  fit_limma_model() |>
  extract_DA_results()

# Export results
write_limma_tables(final)
write_limma_plots(final,
                  grouping_column = "group")

Getting help

For general help on using proteoDA, check out the tutorial vignette by running browseVignettes(package = "proteoDA"). If you did not build the vignette upon install, you can find a pre-built .html version of the vignette in the vignettes folder on GitHub. Additional information can be found in the documentation for each function. If you need further assistance, file an issue on GitHub.

Reporting issues

If you find any bugs or unexpected behaviors, file an issue on GitHub. It is helpful if you can include a minimal reproducible example (reprex) that triggers the issue, check out the reprex R package for more information and tools on creating reproducible examples.

Contributing

We welcome code contributions from users. To contribute, open a pull request against the main branch. Please note that the proteoDA project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

ByrumLab / proteoDA

readme