Correct and fine-tune noisy or mislabeled cell type annotations in single-cell RNA-seq data using semi-supervised learning.
library(scReClassify)
library(SingleCellExperiment)
# Load pre-processed SingleCellExperiment data
data("gse87795_subset_sce")
dat <- gse87795_subset_sce
# Perform dimension reduction using matPCs to retain PCs explaining >= 70% variance
reducedDim(dat, "matPCs") <- matPCs(dat, assay = "logNorm", 0.7)
Input: A SingleCellExperiment object containing log-normalized expression data. Output: A SingleCellExperiment object with a dimension-reduced matrix stored in reducedDim(dat, "matPCs").
When to Use
To perform dimension reduction on single-cell RNA-seq data using matPCs to select principal components explaining at least 70% of the variance.
To correct and fine-tune noisy or mislabeled cell type annotations in a SingleCellExperiment object.
When NOT to Use
For initial unsupervised clustering of completely unlabeled single-cell data, use scran or Seurat because scReClassify is a post hoc semi-supervised tool that requires initial cell type annotations.
For datasets where no initial cell type labels are available, use unsupervised clustering methods because scReClassify relies on the adaSampling algorithm which requires a set of initial (potentially noisy) labels.
Data Requirements
Input format:SingleCellExperiment object or a matrix.
Structure: Must contain cell type annotations in the colData (e.g., cellTypes).
Normalization state: Log-normalized expression data (e.g., logNorm assay) is required for dimension reduction.
Key Parameters
assay ("logNorm"): The assay name in the SingleCellExperiment object to perform dimension reduction on.
Best Practices
Perform quality control and preprocessing of the single-cell dataset before running matPCs.
Use matPCs to automatically select the number of principal components that explain at least 70% of the variance.
Common Pitfalls
Running matPCs on a SingleCellExperiment without specifying the correct assay name, which will result in an error if the default assay is missing. Fix by explicitly setting the assay parameter (e.g., assay = "logNorm").
Alternatives
Seurat for general single-cell classification and clustering.
scater for standard PCA and dimension reduction.
scran for unsupervised cell clustering.
Citations
Kim T (2026), "An introduction to scReClassify package".
1---2name: screclassify3description: scReClassify4---56# scReClassify78## Workflows910### Standard Workflow1112Correct and fine-tune noisy or mislabeled cell type annotations in single-cell RNA-seq data using semi-supervised learning.1314```r15library(scReClassify)16library(SingleCellExperiment)1718# Load pre-processed SingleCellExperiment data19data("gse87795_subset_sce")20dat <- gse87795_subset_sce2122# Perform dimension reduction using matPCs to retain PCs explaining >= 70% variance23reducedDim(dat, "matPCs") <- matPCs(dat, assay = "logNorm", 0.7)24```2526*Input:* A `SingleCellExperiment` object containing log-normalized expression data. *Output:* A `SingleCellExperiment` object with a dimension-reduced matrix stored in `reducedDim(dat, "matPCs")`.2728## When to Use29- To perform dimension reduction on single-cell RNA-seq data using `matPCs` to select principal components explaining at least 70% of the variance.30- To correct and fine-tune noisy or mislabeled cell type annotations in a `SingleCellExperiment` object.3132## When NOT to Use33- For initial unsupervised clustering of completely unlabeled single-cell data, use `scran` or `Seurat` because `scReClassify` is a post hoc semi-supervised tool that requires initial cell type annotations.34- For datasets where no initial cell type labels are available, use unsupervised clustering methods because `scReClassify` relies on the `adaSampling` algorithm which requires a set of initial (potentially noisy) labels.3536## Data Requirements37- **Input format:** `SingleCellExperiment` object or a matrix.38- **Structure:** Must contain cell type annotations in the `colData` (e.g., `cellTypes`).39- **Normalization state:** Log-normalized expression data (e.g., `logNorm` assay) is required for dimension reduction.4041## Key Parameters42- **assay** ("logNorm"): The assay name in the `SingleCellExperiment` object to perform dimension reduction on.4344## Best Practices45- Perform quality control and preprocessing of the single-cell dataset before running `matPCs`.46- Use `matPCs` to automatically select the number of principal components that explain at least 70% of the variance.4748## Common Pitfalls49- Running `matPCs` on a `SingleCellExperiment` without specifying the correct `assay` name, which will result in an error if the default assay is missing. Fix by explicitly setting the `assay` parameter (e.g., `assay = "logNorm"`).5051## Alternatives52- `Seurat` for general single-cell classification and clustering.53- `scater` for standard PCA and dimension reduction.54- `scran` for unsupervised cell clustering.5556## Citations57- Kim T (2026), "An introduction to scReClassify package".5859## References60- Homepage: bioconductor.org/packages/screclassify61- Vignette: bioconductor.org/packages/release/bioc/vignettes/screclassify/inst/doc/screclassify.html
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