scTypeEval
github.com/carmonalab/sctypeevalscTypeEval 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.
Sourced from
- GitHub — github.com/carmonalab/sctypeeval
- Bioconductor — scTypeEval
Related resources
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.
netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data.
iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications.
omicsGMF is a Bioconductor package that uses the sgdGMF-framework of the \code{sgdGMF} package for highly performant and fast matrix factorization that can be used for dimensionality reduction, visualization and imputation of omics data. It considers data from the general exponential family as input, and therefore suits the use of both RNA-seq (Poisson or Negative Binomial data) and proteomics data (Gaussian data). It does not require prior transformation of counts to the log-scale, because it rather optimizes the deviances from the data family specified. Also, it allows to correct for known sample-level and feature-level covariates, therefore enabling visualization and dimensionality reduction upon batch correction. Last but not least, it deals with missing values, and allows to impute these after matrix factorization, useful for proteomics data. This Bioconductor package allows input of SummarizedExperiment, SingleCellExperiment, and QFeature classes.
iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user).
Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.