| name | bioconductor-mast |
| description | Methods and models for handling zero-inflated single cell assay data. |
| when_to_use | Use when: Zero-Inflated scRNA-seq DE: Performing differential expression analysis on single-cell RNA-seq data using a Hurdle model (zlm) to account for bimodal expression patterns.; Data Filtering: Filtering outlier cells and wells where discrete and continuous parts of the signal deviate significantly, visualized via plotSCAConcordance and applied via mast_filter.; Two-Sample Testing: Conducting combined n. Not for: Raw Integer Counts: For un-normalized, raw integer counts; use zinbwave instead because MAST expects log-transformed, approximately scale-normalized data.; Basic QC and Visualization: For simple quality control metric calculation without differential |
| user-invocable | false |
MAST
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 1.38.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: SingleCellExperiment
- Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, reshape2, SummarizedExperiment, progress, Matrix
- Install:
BiocManager::install("MAST")
When to Use
- Zero-Inflated scRNA-seq DE: Performing differential expression analysis on single-cell RNA-seq data using a Hurdle model (
zlm) to account for bimodal expression patterns.
- Data Filtering: Filtering outlier cells and wells where discrete and continuous parts of the signal deviate significantly, visualized via
plotSCAConcordance and applied via mast_filter.
- Two-Sample Testing: Conducting combined normal theory/binomial tests between pairs of groups using the
LRT function.
When NOT to Use
- Raw Integer Counts: For un-normalized, raw integer counts; use
zinbwave instead because MAST expects log-transformed, approximately scale-normalized data.
- Basic QC and Visualization: For simple quality control metric calculation without differential expression; use
scater instead because it provides dedicated functions for QC and PCA plotting.
Data Requirements
- Input Format: A
SingleCellAssay object, which can be constructed from a data.frame (FromFlatDF), a matrix (FromMatrix), or upcast from a SingleCellExperiment (SceToSingleCellAssay).
- Normalization State: Log-transformed, scale-normalized data that has been thresholded (e.g., $\log_2(\text{TPM} + 1)$).
- Structure: Supports dense matrices, sparse
Matrix objects, and HDF5Array backends for out-of-memory data.
Key Parameters
- sca: The
SingleCellAssay object containing the expression data and metadata.
- formula: Symbolic notation specifying the covariates for the Hurdle model in
zlm (e.g., ~ Population + Subject.ID).
- method: The modeling function wrapper used in
zlm (e.g., 'glmer' for mixed models or 'glm').
- ebayes: Logical in
zlm indicating whether to use an empirical Bayes adjustment for the dispersion estimate.
- useContinuousBayes: Logical in
zlm to employ Bayesian linear regression for the continuous component.
- exprs_value: Character string indicating which assay slot contains the log-like data to operate on (e.g.,
'logcounts').
Best Practices
- Upcast existing
SingleCellExperiment objects to SingleCellAssay using SceToSingleCellAssay to ensure compatibility and validity checks.
- Explicitly name the assay slot containing log-like data (e.g.,
exprs_value = 'logcounts') when calling zlm if it is not the default.
- Enable multicore support to speed up model fitting by setting
options(mc.cores=4) before running zlm.
- Use
burdenOfFiltering and plotSCAConcordance to visualize the impact of filtering criteria before applying them.
Common Pitfalls
- Providing Raw Counts: Passing raw integer counts to
zlm leads to suboptimal model performance; Fix: Ensure the input assay contains log-transformed, scale-normalized data (e.g., log2 TPM + 1).
- Memory Exhaustion: Running linear models on extremely large datasets exceeds RAM; Fix: Store the data using a sparse
Matrix or HDF5Array backend and pass it to FromMatrix.
- Missing Assay Detection: MAST fails to find the correct expression values if the assay is uniquely named; Fix: Ensure the assay name matches MAST's default log-like names (e.g.,
'et', 'logcounts') or specify it explicitly.
Alternatives
- zinbwave: Uses zero-inflated negative binomial models directly on integer count data rather than requiring log-transformed inputs.
- scater: Focuses on pre-processing, quality control, and visualization of scRNA-seq data rather than complex hurdle-model differential expression.
- SCnorm: Designed specifically for the robust normalization of single-cell RNA-seq data rather than statistical testing.
Citations
- Finak, Greg, Andrew McDavid, Masanao Yajima, Jingyuan Deng, Vivian Gersuk, Alex K Shalek, Chloe K Slichter, et al. 2015. “MAST: A Flexible Statistical Framework for Assessing Transcriptional Changes and Characterizing Heterogeneity in Single-Cell RNA Sequencing Data.” Genome Biol. 16 (1): 1–13.
- McDavid, Andrew, Lucas Dennis, Patrick Danaher, Greg Finak, Michael Krouse, Alice Wang, Philippa Webster, Joseph Beechem, and Raphael Gottardo. 2014. “Modeling Bi-Modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells.” PLoS Comput. Biol. 10 (7): e1003696.
References
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