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Use bioconductor-screclassify to prepare a matPCs preprocessing script for [QC-processed labeled SingleCellExperiment], using [log-normalized assay and label-column names] and saving PCs explaining at least 70% variance to [output object path].
Required inputs
[QC-processed labeled SingleCellExperiment]
[log-normalized assay and label-column names]
[output object path]
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SKILL.md
name: bioconductor-screclassifydescription: A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.when_to_use: Use when: An introduction to scReClassify package; Single-cell RNA-seq analysis (scReClassify). Not for: Requires R ≥ 4.1 and Bioconductor ≥ 3.16user-invocable: false
scReClassify
Workflows
Standard Workflow
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".