-
promoris a user-friendly, comprehensive R package that combines proteomics data analysis with machine learning-based modeling. -
promorstreamlines differential expression analysis of label-free quantification (LFQ) proteomics data and building predictive models with top protein candidates. -
With
promorwe provide a range of quality control and visualization tools to analyze label-free proteomics data at the protein level. -
Input files for
promorare a proteinGroups.txt file produced by MaxQuant or a standard input file containing a quantitative matrix of protein intensities and an expDesign.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.
🚨Check out our R Shiny app: PROMOR App
Install the released version from CRAN
install.packages("promor")Install development version from GitHub
# install devtools, if you haven't already:
install.packages("devtools")
# install promor from github
devtools::install_github("caranathunge/promor") 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
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.
volcano_plot(fit_df, text_size = 5)
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 buil
7B67
d 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.
roc_plot(
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.
- This README file can be accessed from RStudio as follows,
vignette("intro_to_promor", package = "promor")-
If your data do NOT contain technical replicates: promor: No technical replicates
-
If your data contain technical replicates: promor: Technical replicates
-
If you would like to use your proteomics data to build predictive models: promor: Modeling