scCODA is a Bayesian model for compositional single-cell data analysis.
paper
Cited
Public
- Authors
- Büttner, M; Ostner, J; Müller, C L; Theis, F J; Schubert, B
- Year
- 2021
- Journal
- Nature communications
- PMID
- 34824236
- DOI
- 10.1038/s41467-021-27150-6
- PMCID
- PMC8616929
Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.
Compositional data analysis in single-cell RNA-sequencing data.a Single-cell analysis of control and disease states of a human tissue sample. Disease states reflect changes in the cell-type composition. b Exemplary realization of the tested scenarios with high compositional log-fold change and low replicate number (n = 2 samples per group). Colored horizontal lines indicate statistically detected compositional changes between case and control for different methods. The error bars denote the 95% confidence interval around the mean. c The scCODA model structure with hyperparameters. Blue variables are observed. DirMult indicates a Dirichlet-Multinomial, N a Normal, logitN a Logit-Normal, and HC a Half-Cauchy distribution.
Comparison of scCODA’s benchmark performance to other differential abundance testing methods.Bayesian models (red), non-standard compositional models (blue), compositional tests/regression (green), non-compositional methods (purple). Shaded areas represent 95% confidence intervals. a Receiver-operating curve (n >1 samples per group). AUC scores are reported in (Supplementary Table 1). b Precision-recall curve (n >1 samples per group). Average precision scores are reported in (Supplementary Table 1). c–e Performance metrics with increasing number of replicates per group over all tested scenarios. In the case of n = 1 sample per group, only Bayesian methods are applicable, other methods cannot detect any changes. c Overall performance measured by Matthews’ correlation coefficient (MCC). d Sensitivity measured by true positive rate (TPR). e Precision measured by false discovery rate (FDR). The nominal FDR level of 0.05 for all methods (except scCODA with FDR 0.2) is indicated with a horizontal black line.
scCODA determines the compositional changes in a variety of examples.References are indicated in bold. a Boxplots of blood samples of supercentenarians (n = 7, dark blue) have significantly fewer B cells than younger individuals (control, n = 5, light blue), reference was set to CD16+ Monocytes, Hamiltonian Monte Carlo (HMC) chain length was set to 20,000 with a burn-in of 5000. Credible and significant results are depicted as colored bars (red: scCODA, brown: Wilcoxon rank-sum test (two-sided; Benjamini–Hochberg corrected)3). Results are in accordance with FACS data3. P values and effect sizes are shown in Supplementary Data 1. b Microglia associated with Alzheimer’s disease (AD) are significantly more abundant in the cortex, but not in the cerebellum19 (n = 2 in AD (dark blue) and wild-type (light blue) mice, respectively), HMC chain length was set to 20,000 with burn-in of 5000. P values and effect sizes are shown in Supplementary Data 2. c–e Changes in epithelium and lamina propria in the human colon1 in ulcerative colitis (UC) (n = 133 from 18 UC patients, 12 healthy donors). Credible and significant results are depicted as colored bars (red: scCODA, green: two-sided t test of Dirichlet regression coefficients). Stars indicate the significance level (*adjusted P < 0.05, **adjusted P < 0.01, ****adjusted P < 0.001; Benjamini–Hochberg corrected). c Epithelium and Lamina propria are distinct tissues, which are studied separately. d Compositional changes from healthy (light blue) to non-inflamed (medium blue) and inflamed (dark blue) biopsies of the intestinal epithelium, HMC chain length was set to 150,000 with burn-in of 10,000. P values and effect sizes are shown in Supplementary Data 3. e Boxplots of compositional changes from healthy (light blue) to non-inflamed (medium blue) and inflamed (dark blue) biopsies in the lamina propria, HMC chain length was set to 400,000 with burn-in of 10,000. P values and effect sizes are shown in Supplementary Data 3. f Boxplots of compositional changes in bronchoalveolar cells in COVID-19 patients (n = 4 healthy (light blue), n = 3 mild (medium blue), n = 6 severe (dark blue) disease progression)4. Credible and significant results are depicted as colored bars (red: scCODA, orange: t test (two-sided; Benjamini–Hochberg corrected)), references for scCODA: Plasma (all pairwise comparisons between conditions), FDR at 0.2. Stars indicate the significance level (*: adjusted P < 0.05, **adjusted P < 0.01, ***adjusted P < 0.001; Benjamini–Hochberg corrected), HMC chain length was set to 80,000 with a burn-in of 10,000. P values and effect sizes are shown in Supplementary Data 4. a, b, d–f In all boxplots, the central line denotes the median, boxes represent the interquartile range (IQR), and whiskers show the distribution except for outliers. Outliers are all points outside 1.5 times of the IQR.
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| sccomp: Robust differential composition and variability analysis for single-cell data. | Mangiola S et al. | — | 2023 | → |
| scDiffCom: a tool for differential analysis of cell-cell interactions provides a mouse atlas of aging changes in intercellular communication. | Lagger C et al. | — | 2023 | → |
| Single-cell dissection of cervical cancer reveals key subsets of the tumor immune microenvironment. | Cao G et al. | — | 2023 | → |
| Single cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs. | Weeratunga P et al. | — | 2023 | → |
| Single-nucleus RNA sequencing in ischemic cardiomyopathy reveals common transcriptional profile underlying end-stage heart failure. | Simonson B et al. | — | 2023 | → |
| Single-nucleus transcriptomic mapping uncovers targets for traumatic brain injury. | Yang Q et al. | — | 2023 | → |
| Transcriptomic atlas and interaction networks of brain cells in mouse CNS demyelination and remyelination. | Hou J et al. | — | 2023 | → |
| Transcriptomic cytoarchitecture reveals principles of human neocortex organization. | Jorstad NL et al. | — | 2023 | → |
| Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta. | Chou EL et al. | — | 2022 | → |
| A single-cell atlas of human and mouse white adipose tissue. | Emont MP et al. | — | 2022 | → |
| A single-cell transcriptomic atlas tracking the neural basis of division of labour in an ant superorganism. | Li Q et al. | — | 2022 | → |
| CD8<sup>+</sup> T cells induce interferon-responsive oligodendrocytes and microglia in white matter aging. | Kaya T et al. | — | 2022 | → |
| Cell Type Diversity Statistic: An Entropy-Based Metric to Compare Overall Cell Type Composition Across Samples. | Karagiannis TT et al. | — | 2022 | → |
| Crustacean leg regeneration restores complex microanatomy and cell diversity. | Almazán A et al. | — | 2022 | → |
| Differential abundance testing on single-cell data using k-nearest neighbor graphs. | Dann E et al. | — | 2022 | → |
| Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma. | Haradhvala NJ et al. | — | 2022 | → |
| Hierarchical deployment of Tbx3 dictates the identity of hypothalamic KNDy neurons to control puberty onset. | Shi X et al. | — | 2022 | → |
| High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. | Salcher S et al. | — | 2022 | → |
| Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19. | Ma Y et al. | — | 2022 | → |
| Intratumoral Heterogeneity and Immune Modulation in Lung Adenocarcinoma in Female Smokers and Never Smokers. | Trefzer TB et al. | — | 2022 | → |
| Landscape and age dynamics of immune cells in the Egyptian rousette bat. | Friedrichs V et al. | — | 2022 | → |
| Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. | Ma S et al. | — | 2022 | → |
| Negative binomial factor regression with application to microbiome data analysis. | Mishra AK et al. | — | 2022 | → |
| Primary cilia and SHH signaling impairments in human and mouse models of Parkinson's disease. | Schmidt S et al. | — | 2022 | → |
| Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF. | Eaton DM et al. | — | 2022 | → |
| Single cell atlas of spinal cord injury in mice reveals a pro-regenerative signature in spinocerebellar neurons. | Matson KJE et al. | — | 2022 | → |
| Single-cell transcriptome analysis of the in vivo response to viral infection in the cave nectar bat Eonycteris spelaea. | Gamage AM et al. | — | 2022 | → |
| Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. | Hwang WL et al. | — | 2022 | → |
| Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. | Chaffin M et al. | — | 2022 | → |
| Temporally restricted activation of IFNβ signaling underlies response to immune checkpoint therapy in mice. | Zemek RM et al. | — | 2022 | → |
| The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location. | Liu I et al. | — | 2022 | → |
| Transplanting rejuvenated blood stem cells extends lifespan of aged immunocompromised mice. | Montserrat-Vazquez S et al. | — | 2022 | → |
| A molecular toolkit for superorganisms. | Sieriebriennikov B et al. | — | 2021 | → |
| Applications of single-cell genomics and computational strategies to study common disease and population-level variation. | Auerbach BJ et al. | — | 2021 | → |
| Quantifying the effect of experimental perturbations at single-cell resolution. | Burkhardt DB et al. | — | 2021 | → |
| scCODA is a Bayesian model for compositional single-cell data analysis. | Büttner M et al. | — | 2021 | → |
| tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data. | Ostner J et al. | — | 2021 | → |