proteoDA

proteoDA: a package for quantitative proteomics - Published in JOSS (2023)

https://github.com/byrumlab/proteoda

Scientific Fields

Repository

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  eval = F
)
```

# proteoDA


[![R-CMD-check](https://github.com/ByrumLab/proteoDA/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ByrumLab/proteoDA/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/github/ByrumLab/proteoDA/branch/main/graph/badge.svg)](https://app.codecov.io/github/ByrumLab/proteoDA?branch=main)


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:

``` {r, eval = F}
install.packages("devtools")
```

Then install `proteoDA`:

```{r, eval = F}
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](https://github.com/ByrumLab/proteoDA).

Once `proteoDA` is installed, load it into R:

```{r, eval = F}
library(proteoDA)
```

## Workflow

![proteoDA workflow flowchart](./data-raw/proteoDA_flowchart.png?raw=true)

## 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.

```{r, eval = F}
# 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](https://github.com/ByrumLab/proteoDA). Additional information can be found in the documentation for each function. If you need further assistance, [file an issue on GitHub](https://github.com/ByrumLab/proteoDA/issues).

## Reporting issues

If you find any bugs or unexpected behaviors, [file an issue on GitHub](https://github.com/ByrumLab/proteoDA/issues). It is helpful if you can include a minimal reproducible example (reprex) that triggers the issue, check out the `reprex` [R package](https://reprex.tidyverse.org/) for more information and tools on creating reproducible examples.  

## Contributing

We welcome code contributions from users. To contribute, [open a pull request](https://github.com/ByrumLab/proteoDA/pulls) against the main branch. Please note that the proteoDA project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

Owner

JOSS Publication

proteoDA: a package for quantitative proteomics

Published

May 30, 2023

DOI

10.21105/joss.05184

Volume 8, Issue 85, Page 5184

Authors

Timothy J. Thurman
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Charity L. Washam
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Duah Alkam
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Jordan T. Bird
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Allen Gies
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Kalyani Dhusia
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Michael S. Robeson
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Stephanie D. Byrum
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA, Arkansas Children's Research Institute, Little Rock, AR, USA

Editor

Jacob Schreiber

Tags

mass spectrometry intensity data normalization linear models

View PDF Review Thread Software Archive

Citation (CITATION.cff)

proteoDA-version: 1.0.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Timothy J.
    given-names: Thurman
    orcid: https://orcid.org/0000-0002-9602-6226
    - family-names: Charity L.
    given-names: Washam
    orcid: https://orcid.org/0000-0001-5761-9304
    - family-names: Duah
    given-names: Alkam
    orcid: https://orcid.org/0000-0002-5965-7694
    - family-names: Jordan T.
    given-names: Bird
    orcid: https://orcid.org/0000-0001-5753-6058
    - family-names: Allen 
    given-names: Gies
    orcid: https://orcid.org/0000-0003-2492-0429
    - family-names: Kalyani
    given-names: Dhusia
    orcid: https://orcid.org/0000-0002-8803-1295
    - family-names: Michael S. 
    given-names: Robeson, II
    orcid: https://orcid.org/0000-0001-7119-6301
    - family-names: Stephanie D.
    given-names: Byrum
    orcid: https://orcid.org/0000-0002-1783-3610
title: proteoDA: a package for quantitative proteomics
version: 1.0.0
date-released: 2023-05-23
doi: 10.5281/zenodo.7962306
url: "https://doi.org/10.5281/zenodo.7962306"

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