# Screclassify

> scReClassify

- Skill: `biomate-ai/screclassify` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/screclassify`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/screclassify/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/screclassify

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# scReClassify

## Workflows

### Standard Workflow

Correct and fine-tune noisy or mislabeled cell type annotations in single-cell RNA-seq data using semi-supervised learning.

```r
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".

## References
- Homepage: bioconductor.org/packages/screclassify
- Vignette: bioconductor.org/packages/release/bioc/vignettes/screclassify/inst/doc/screclassify.html