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UCell - Rank-based signature enrichment analysis for single-cell data

UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects.

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singlecellgenesetenrichmenttranscriptomicsgeneexpressioncellbasedassays

11.75 score 212 stars 3 dependents 1.2k scripts 4.2k downloads

scGate - Marker-Based Cell Type Purification for Single-Cell Sequencing Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.

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filteringmarker-genesscgatesignaturessingle-cell

8.51 score 145 stars 1 dependents 215 scripts 609 downloads

GeneNMF - Non-Negative Matrix Factorization for Single-Cell Omics

A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.

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8.01 score 211 stars 64 scripts 691 downloads

STACAS - STACAS: Sub-Type Anchoring Correction for Alignment in Seurat

This package implements methods for batch correction and integration of scRNA-seq datasets, based on the popular Seurat anchor-based integration framework. In particular, STACAS is optimized for the integration of heterogenous datasets with only limited overlap between cell sub-types (e.g. TIL sets of CD8 from tumor with CD8/CD4 T cells from lymphnode), for which the default Seurat alignment methods would tend to over-correct biological differences. The 2.0 version of our package allows to the users to incorporate explicit information about cell-types in order to assist the integration process.

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6.58 score 103 stars 1 dependents 311 scripts

ProjecTILs - Reference-based analysis of scRNA-seq data

This package implements methods to project single-cell RNA-seq data onto a reference atlas, enabling interpretation of unknown cell transcriptomic states in the the context of known, reference states.

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5.99 score 327 stars 148 scripts

SignatuR - Signatures for single-cell data analysis

A database of useful signatures for single-cell data analysis. Query the existing signatures or use it to organize your own gene sets in a hierarchical structure.

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4.32 score 30 stars 70 scripts