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.
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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").
matPCs to select principal components explaining at least 70% of the variance.SingleCellExperiment object.scran or Seurat because scReClassify is a post hoc semi-supervised tool that requires initial cell type annotations.scReClassify relies on the adaSampling algorithm which requires a set of initial (potentially noisy) labels.SingleCellExperiment object or a matrix.colData (e.g., cellTypes).logNorm assay) is required for dimension reduction.SingleCellExperiment object to perform dimension reduction on.matPCs.matPCs to automatically select the number of principal components that explain at least 70% of the variance.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").Seurat for general single-cell classification and clustering.scater for standard PCA and dimension reduction.scran for unsupervised cell clustering.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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