Biocview "SingleCell"

Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface
AIPS : Absolute Inference of Patient Signatures
Analysis of RNA-Seq Count Data by Using Multiple Corespondence Analysis
Analysis of RNA-Seq Count Data by Using Multiple Corespondence Analysis
A package to extract spatial features based on multiplex IF images
A package to extract spatial features based on multiplex IF images
A tool for unsupervised projection of single cell RNA-seq data
A tool for unsupervised projection of single cell RNA-seq data
AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)
Augmented functionality for analysis of epigenomic variance
Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations
Chromatin Variation Across Regions
Compare Characteristic Features of Count Data Sets
Compare Clusterings for Single-Cell Sequencing
Compare Clusterings for Single-Cell Sequencing
Compare Clusterings for Single-Cell Sequencing
Compare Clusterings for Single-Cell Sequencing
Cytometry dATa anALYSis Tools
Cytometry dATa anALYSis Tools
Cytometry dATa anALYSis Tools
Deconvolution of Bulk Sequencing Experiments using Single Cell Data
Interpretable marker-based single-cell pseudotime using Bayesian parametric models
Interpretable marker-based single-cell pseudotime using Bayesian parametric models
Linear model and normality based transformation method (Linnorm)
Linear model and normality based transformation method (Linnorm)
Methods for Single-Cell RNA-Seq Data Analysis
Methods for Single-Cell RNA-Seq Data Analysis
Methods for Single-Cell RNA-Seq Data Analysis
Mixture modeling of single-cell RNA-seq data to indentify genes with differential distributions
Mixture modeling of single-cell RNA-seq data to indentify genes with differential distributions
Mixture modeling of single-cell RNA-seq data to indentify genes with differential distributions
Model-based Analysis of Single Cell Transcriptomics
Model-based Analysis of Single Cell Transcriptomics
Model-based Analysis of Single Cell Transcriptomics
Normalization of single cell RNA-seq data
Normalization of single cell RNA-seq data
pipeline for single cell RNA-seq data analysis
S4 Classes for Single Cell Data
S4 Classes for Single Cell Data
S4 Classes for Single Cell Data
SCENIC (Single Cell rEgulatory Network Inference and Clustering)
SIMLR: Single-cell Interpretation via Multi-kernel LeaRning
Simple Simulation of Single-cell RNA Sequencing Data
Simple Simulation of Single-cell RNA Sequencing Data
Simple Simulation of Single-cell RNA Sequencing Data
Single-cell analysis toolkit for gene expression data in R
Single-cell analysis toolkit for gene expression data in R
Single-cell analysis toolkit for gene expression data in R
Single-Cell Consensus Clustering
Single-Cell Consensus Clustering
Single-Cell Consensus Clustering
Single Cell Overview of Normalized Expression data
Single Cell Overview of Normalized Expression data
Single Cell Overview of Normalized Expression data
Single-Cell RNA-Seq Utilities
Single-Cell RNA-Seq Utilities
Title: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning
Title: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning
Tools for ordering single-cell sequencing
TRONCO, an R package for TRanslational ONCOlogy
TRONCO, an R package for TRanslational ONCOlogy
TRONCO, an R package for TRanslational ONCOlogy
Using Mass Cytometry for Differential Abundance Analyses
Using Mass Cytometry for Differential Abundance Analyses
Using Mass Cytometry for Differential Abundance Analyses
Varying-Censoring Aware Matrix Factorization
Zero-Inflated Negative Binomial Model for RNA-Seq Data
Zero-Inflated Negative Binomial Model for RNA-Seq Data