https://github.com/MangiolaLaboratory/sccomp
Testing differences in cell type proportions from single-cell data.
https://github.com/MangiolaLaboratory/sccomp
batch-correction bayesian composition cytof differential-proportion microbiome multilevel proportions r-package random-effects single-cell unwanted-variation
Last synced: 4 months ago
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Testing differences in cell type proportions from single-cell data.
- Host: GitHub
- URL: https://github.com/MangiolaLaboratory/sccomp
- Owner: MangiolaLaboratory
- License: gpl-3.0
- Created: 2021-02-08T10:01:14.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-01-23T11:58:48.000Z (4 months ago)
- Last Synced: 2025-01-23T12:26:46.764Z (4 months ago)
- Topics: batch-correction, bayesian, composition, cytof, differential-proportion, microbiome, multilevel, proportions, r-package, random-effects, single-cell, unwanted-variation
- Language: R
- Homepage: https://mangiolalaboratory.github.io/sccomp
- Size: 95.8 MB
- Stars: 96
- Watchers: 8
- Forks: 7
- Open Issues: 14
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
title: "sccomp - Tests differences in cell type proportions and variability from single-cell data"
output: github_document
always_allow_html: true
editor_options:
markdown:
wrap: 72
---
[](https://www.tidyverse.org/lifecycle/#maturing)
[](https://github.com/MangiolaLaboratory/sccomp/actions/)
Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression in conditions such as cancer and pathogen infections.
For cellular omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce **sccomp**, a generalised method for differential composition and variability analyses capable of jointly modelling data count distribution, compositionality, group-specific variability, and proportion mean-variability association, while being robust to outliers.
**sccomp** is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. We demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial modeling and providing essential principles for differential variability analysis.
### Comparison with other methods
- **I**: Data are modelled as counts.
- **II**: Group proportions are modelled as compositional.
- **III**: The proportion variability is modelled as cell-type specific.
- **IV**: Information sharing across cell types, mean–variability association.
- **V**: Outlier detection or robustness.
- **VI**: Differential variability analysis.
| Method | Year | Model | I | II | III | IV | V | VI |
|--------------|------|------------------------------|---|----|-----|----|---|----|
| **sccomp** | 2023 | Sum-constrained Beta-binomial | ● | ● | ● | ● | ● | ● |
| **scCODA** | 2021 | Dirichlet-multinomial | ● | ● | | | | |
| **quasi-binom.** | 2021 | Quasi-binomial | ● | | ● | | | |
| **rlm** | 2021 | Robust-log-linear | | ● | | | ● | |
| **propeller** | 2021 | Logit-linear + limma | | ● | ● | ● | | |
| **ANCOM-BC** | 2020 | Log-linear | | ● | ● | | | |
| **corncob** | 2020 | Beta-binomial | ● | | ● | | | |
| **scDC** | 2019 | Log-linear | | ● | ● | | | |
| **dmbvs** | 2017 | Dirichlet-multinomial | ● | ● | | | | |
| **MixMC** | 2016 | Zero-inflated Log-linear | | ● | ● | | | |
| **ALDEx2** | 2014 | Dirichlet-multinomial | ● | ● | | | | |
```{r echo=FALSE}
knitr::opts_chunk$set( fig.path = "inst/figures/")
```
### Cite
Mangiola, Stefano, Alexandra J. Roth-Schulze, Marie Trussart, Enrique Zozaya-Valdés, Mengyao Ma, Zijie Gao, Alan F. Rubin, Terence P. Speed, Heejung Shim, and Anthony T. Papenfuss. 2023. “Sccomp: Robust Differential Composition and Variability Analysis for Single-Cell Data.” Proceedings of the National Academy of Sciences of the United States of America 120 (33): e2203828120. https://doi.org/10.1073/pnas.2203828120
[PNAS - sccomp: Robust differential composition and variability analysis for single-cell data](https://www.pnas.org/doi/full/10.1073/pnas.2203828120)
### Talk
```{r child="man/fragments/intro.Rmd"}
```
## The old framework
The new tidy framework was introduced in 2024, two, understand the differences and improvements. Compared to the old framework, please read this [blog post](https://tidyomics.github.io/tidyomicsBlog/post/2023-12-07-tidy-sccomp/).