https://github.com/caranathunge/promor
A comprehensive R package for label-free proteomics data analysis and modeling
https://github.com/caranathunge/promor
biomarkers differential-expression lfq machine-learning mass-spectrometry modeling proteomics r r-package rstats
Last synced: 22 days ago
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A comprehensive R package for label-free proteomics data analysis and modeling
- Host: GitHub
- URL: https://github.com/caranathunge/promor
- Owner: caranathunge
- License: lgpl-2.1
- Created: 2022-04-14T18:34:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-21T14:46:38.000Z (about 2 years ago)
- Last Synced: 2025-09-21T01:28:56.868Z (26 days ago)
- Topics: biomarkers, differential-expression, lfq, machine-learning, mass-spectrometry, modeling, proteomics, r, r-package, rstats
- Language: R
- Homepage: https://caranathunge.github.io/promor/
- Size: 161 MB
- Stars: 16
- Watchers: 2
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
Awesome Lists containing this project
README
---
output:
github_document:
number_sections: FALSE
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "70%"
)
```
# promor 
### Proteomics Data Analysis and Modeling Tools
[](https://CRAN.R-project.org/package=promor)
[](https://r-pkg.org/pkg/promor)
[](https://r-pkg.org/pkg/promor)
[](https://github.com/caranathunge/promor/actions/workflows/R-CMD-check.yaml)
[](https://github.com/caranathunge/promor/actions/workflows/test-coverage.yaml)
[](https://www.gnu.org/licenses/lgpl-2.1)
* `promor` is a user-friendly, comprehensive R package that combines proteomics
data analysis with machine learning-based modeling.
* `promor` streamlines differential expression analysis of
**label-free quantification (LFQ)** proteomics data and building predictive
models with top protein candidates.
* With `promor` we provide a range of quality control and visualization tools to analyze label-free proteomics data at the
protein level.
* Input files for `promor` are a [proteinGroups.txt ](https://raw.githubusercontent.com/caranathunge/promor_example_data/main/pg1.txt)
file produced by [**MaxQuant**](https://maxquant.org) or a [standard input file](https://raw.githubusercontent.com/caranathunge/promor_example_data/main/st.txt) containing a quantitative matrix of protein intensities and an [expDesign.txt](https://raw.githubusercontent.com/caranathunge/promor_example_data/main/ed1.txt) file containing the experimental design of your proteomics data.
* The standard input file should be a tab-delimited text file. Proteins or protein groups should be indicated by rows and samples by columns. Protein names should be listed in the first column and you may use a column name of your choice for the first column. The remaining sample column names should match the sample names indicated by the mq_label column in the expDesign.txt file.
:rotating_light:**Check out our R Shiny app:** [PROMOR App](https://sgrbnf.shinyapps.io/PROMOR_App/)
___
### Installation
Install the released version from CRAN
``` r
install.packages("promor")
```
Install development version from [GitHub](https://github.com/caranathunge/promor)
``` r
# install devtools, if you haven't already:
install.packages("devtools")
# install promor from github
devtools::install_github("caranathunge/promor")
```
---
### Proteomics data analysis with promor
{width=100%}
*Figure 1. A schematic diagram of suggested workflows for proteomics data analysis with promor.*
#### Example
Here is a minimal working example showing how to identify differentially
expressed proteins between two conditions using `promor` in five simple steps.
We use a previously published data set from [Cox et al. (2014)](https://europepmc.org/article/MED/24942700#id609082) (PRIDE ID: PXD000279).
```{r example, results = 'hide', warning=FALSE, eval = FALSE}
# Load promor
library(promor)
# Create a raw_df object with the files provided in this github account.
raw <- create_df(
prot_groups = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/pg1.txt",
exp_design = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/ed1.txt"
)
# Filter out proteins with high levels of missing data in either condition or group
raw_filtered <- filterbygroup_na(raw)
# Impute missing data and create an imp_df object.
imp_df <- impute_na(raw_filtered)
# Normalize data and create a norm_df object
norm_df <- normalize_data(imp_df)
# Perform differential expression analysis and create a fit_df object
fit_df <- find_dep(norm_df)
```
Lets take a look at the results using a volcano plot.
```{r volcanoplot, warning = FALSE, eval=FALSE}
volcano_plot(fit_df, text_size = 5)
```
{width=70%}
---
### Modeling with promor
{width=100%}
*Figure 2. A schematic diagram of suggested workflows for building predictive models with promor.*
#### Example
The following minimal working example shows you how to use your results from differential expression analysis to build machine learning-based predictive models using `promor`.
We use a previously published data set from [Suvarna et al. (2021)](https://www.frontiersin.org/articles/10.3389/fphys.2021.652799/full#h3) that used differentially expressed proteins between severe and non-severe COVID patients to build models to predict COVID severity.
```{r modeling_example, results = 'hide', warning = FALSE, message = F, eval = FALSE}
# First, let's make a model_df object of top differentially expressed proteins.
# We will be using example fit_df and norm_df objects provided with the package.
covid_model_df <- pre_process(
fit_df = covid_fit_df,
norm_df = covid_norm_df
)
# Next, we split the data into training and test data sets
covid_split_df <- split_data(model_df = covid_model_df)
# Let's train our models using the default list of machine learning algorithms
covid_model_list <- train_models(split_df = covid_split_df)
# We can now use our models to predict the test data
covid_prob_list <- test_models(
model_list = covid_model_list,
split_df = covid_split_df
)
```
Let's make ROC plots to check how the different models performed.
```{r rocplot, warning = FALSE, eval = FALSE, message = F}
roc_plot(
probability_list = covid_prob_list,
split_df = covid_split_df
)
```
{width=70%}
---
### Tutorials
You can choose a tutorial from the list below that best fits your experiment
and the structure of your proteomics data.
1. This README file can be accessed from RStudio as follows,
``` r
vignette("intro_to_promor", package = "promor")
```
2. If your data do NOT contain technical replicates:
[promor: No technical replicates](https://caranathunge.github.io/promor/articles/promor_no_techreps.html)
3. If your data contain technical replicates:
[promor: Technical replicates](https://caranathunge.github.io/promor/articles/promor_with_techreps.html)
4. If you would like to use your proteomics data to build predictive models:
[promor: Modeling](https://caranathunge.github.io/promor/articles/promor_for_modeling.html)