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

11.07 score 196 stars 2 dependents 740 scripts 2.8k downloads

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

10.62 score 196 stars 2 dependents 736 scripts

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

8.79 score 141 stars 1 dependents 209 scripts 544 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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7.81 score 188 stars 49 scripts 703 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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6.73 score 98 stars 1 dependents 305 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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6.33 score 320 stars 166 scripts

scTypeEval - Evaluation of cell type classifications in single-cell transcriptomics

scTypeEval provides tools to evaluate and validate cell type classifications in single-cell transcriptomics when ground truth labels are limited or unavailable. Results are organized in an S4 object that integrates processed data, dimensional reductions, dissimilarity assays, and consistency metrics computed across samples. The workflow includes preprocessing and feature selection, principal component analysis, computation of dissimilarity matrices, internal validation metrics (for example, silhouette-based summaries), and visualization utilities to inspect heatmaps and PCA plots. Functions support common single-cell containers and enable comparison of clustering and labeling strategies across datasets.

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singlecelltranscriptomicsgeneexpressioncellbasedassaysdimensionreductionpreprocessingprincipalcomponent

6.13 score 2 stars 4 scripts 156 downloads

scTypeEval - Evaluation of cell type classifications in single-cell transcriptomics

scTypeEval provides tools to evaluate and validate cell type classifications in single-cell transcriptomics when ground truth labels are limited or unavailable. Results are organized in an S4 object that integrates processed data, dimensional reductions, dissimilarity assays, and consistency metrics computed across samples. The workflow includes preprocessing and feature selection, principal component analysis, computation of dissimilarity matrices, internal validation metrics (for example, silhouette-based summaries), and visualization utilities to inspect heatmaps and PCA plots. Functions support common single-cell containers and enable comparison of clustering and labeling strategies across datasets.

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singlecelltranscriptomicsgeneexpressioncellbasedassaysdimensionreductionpreprocessingprincipalcomponent

6.11 score 2 stars 4 scripts

scECODA - Single-Cell Exploratory Compositional Data Analysis

The scECODA R package provides a complete workflow for the analysis and visualization of compositional data, primarily focusing on cell type proportions derived from single-cell data. It implements specialized methods, such as the Centered Log-Ratio (CLR) transformation, to properly analyze proportional data while avoiding the biases introduced by the compositional constraint. The package encapsulates data management, transformation, and analysis into a single SummarizedExperiment object, offering downstream tools for dimensionality reduction via PCA, calculating critical metrics like the Adjusted Rand Index (ARI) and Modularity to quantify sample grouping quality, and generating high-quality visualizations like heatmaps and scatter plots.

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softwaresinglecelltranscriptomicscellbasedassaysnormalizationpreprocessingvisualizationclusteringdimensionreductionfeatureextractionprincipalcomponent

6.08 score 8 stars 6 scripts 139 downloads

scECODA - Single-Cell Exploratory Compositional Data Analysis

The scECODA R package provides a complete workflow for the analysis and visualization of compositional data, primarily focusing on cell type proportions derived from single-cell data. It implements specialized methods, such as the Centered Log-Ratio (CLR) transformation, to properly analyze proportional data while avoiding the biases introduced by the compositional constraint. The package encapsulates data management, transformation, and analysis into a single SummarizedExperiment object, offering downstream tools for dimensionality reduction via PCA, calculating critical metrics like the Adjusted Rand Index (ARI) and Modularity to quantify sample grouping quality, and generating high-quality visualizations like heatmaps and scatter plots.

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softwaresinglecelltranscriptomicscellbasedassaysnormalizationpreprocessingvisualizationclusteringdimensionreductionfeatureextractionprincipalcomponent

6.08 score 8 stars 6 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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4.23 score 28 stars 61 scripts