docs/functions.md

In carmonalab/UCell: Rank-based signature enrichment analysis for single-cell data

UCell - Functions

• ScoreSignatures_UCell Calculate module enrichment scores from single-cell data. Given a gene vs. cell matrix (either as sparse matrix or stored in a SingleCellExperiment object), calculates module/signature enrichment scores on single-cell level using Mann-Whitney U statistic. Returned scores are normalized U statistic (equivalent to AUC - Area Under the Curve). This score depends only on the gene expression ranks of individual cell, and therefore is robust across datasets.

• AddModuleScore_UCell A wrapper for UCell to interact directly with Seurat objects. Given a Seurat object, calculates module/signature enrichment scores on single-cell level using Mann-Whitney U statistic. Returned scores are normalized U statistic (equivalent to AUC - Area Under the Curve). In contrast to Seurat's AddModuleScore (based on population average gene expression binning) this score depend only on the gene expression ranks of individual cell, and therefore is robust across datasets.

• StoreRankings_UCell Calculate and store gene rankings for a single-cell dataset. Given a gene vs. cell matrix, calculates the rankings of expression for all genes in each cell. It can then be applied to the function ScoreSignatures_UCell to evaluate gene signatures on the gene expression ranks of individual cells.

• SmoothKNN Perform signature score smoothing using a weighted average of the scores of the first k nearest neighbors (kNN). It can be useful to 'impute' scores by neighboring cells and partially correct data sparsity. While this function has been designed to smooth UCell scores, it can be applied to any numerical metadata contained in SingleCellExperiment or Seurat objects

Find more information, syntax and examples using the R help function e.g. ?ScoreSignatures_UCell. Package information is available with ?UCell

carmonalab/UCell documentation built on Nov. 4, 2024, 5:32 p.m.