scplainer: scplainer: linear models to understand mass...

In UCLouvain-CBIO/scp: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis

scplainer: linear models to understand mass spectrometry-based single-cell proteomics data

Description

scplainer, standing for SCP-based Linear modelling Approach for Interpretable aNd Explorable Results, is a principled and standardised approach for extracting meaningful insights from SCP data. At its core, the approach performs statistical modelling using linear regression.

The workflow starts from a SingleCellExperiment object containing SCP data. The data is assumed to be log-transformed. We advise to perform cell and feature quality control to avoid that failed or outlying cells/feature distort the results. We also recommend starting at the precursor or the peptide-level, but the workflow also allows protein-level data. Similarly, the workflow is robust against for missing values, but it also allows for data where missing values are imputed.

To learn how to import your data, we suggest reading the vignette: vignette("read_scp", package = "scp")

To learn how to process your data, we suggest reading the vignette: vignette("scp", package = "scp")

Outline of the workflow

1. scpModel-Workflow: performs the data modelling and filtering using linear regression.

2. ScpModel-VarianceAnalysis: investigate the contribution of each model variable to the data

3. ScpModel-DifferentialAnalysis: assess the statistical significance of the differences observed between group of samples of interest.

4. ScpModel-ComponentAnalysis: visually explore the data captured by each model variable.

Once the data are modelled and explored, the filtered, normalised and batch-corrected data can be retrieved for further downstream analysis, such as clustering or trajectory inference.

You can find a demonstration of the scplainer workflow in a dedicated vignette: vignette("scp_data_modelling", package = "scp")

Author(s)

Christophe Vanderaa, Laurent Gatto

References

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data Christophe Vanderaa, Laurent Gatto bioRxiv 2023.12.14.571792; doi: https://doi.org/10.1101/2023.12.14.571792.

UCLouvain-CBIO/scp documentation built on July 3, 2025, 6:02 p.m.