Proteomics Data Analysis and Modeling Tools

CRAN status CRAN RStudio mirror downloads CRAN RStudio mirror downloads R-CMD-check test-coverage License: LGPL v2.1

:rotating_light:Check out our R Shiny app: PROMOR App


Install the released version from CRAN


Install development version from GitHub

# install devtools, if you haven't already:

# install promor from github

Proteomics data analysis with promor

promor prot analysis flow chart by caranathunge Figure 1. A schematic diagram of suggested workflows for proteomics data analysis with promor.


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) (PRIDE ID: PXD000279).

# Load promor

# Create a raw_df object with the files provided in this github account.
raw <- create_df(
  prot_groups = "",
  exp_design = ""

# 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.

volcano_plot(fit_df, text_size = 5)

Modeling with promor

promor flowchart-modeling by caranathunge Figure 2. A schematic diagram of suggested workflows for building predictive models with promor.


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) that used differentially expressed proteins between severe and non-severe COVID patients to build models to predict COVID severity.

# 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.

  probability_list = covid_prob_list,
  split_df = covid_split_df


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,
vignette("intro_to_promor", package = "promor")
  1. If your data do NOT contain technical replicates: promor: No technical replicates

  2. If your data contain technical replicates: promor: Technical replicates

  3. If you would like to use your proteomics data to build predictive models: promor: Modeling