An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
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Updated
May 21, 2024 - R
An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
Coarse-graining of large single-cell RNA-seq data into metacells
Explore and share your scRNAseq clustering results
Data-driven Network-based Bayesian Inference of Drivers
A deep learning-based tool for alignment and integration of single cell genomic data across multiple datasets, species, conditions, batches
BITFAM is a Bayesian approach and platform to infer transcription factor activities within individual cells using single cell RNA-sequencing data. Please see Gao S et al., Genome Research (2021) https://genome.cshlp.org/content/31/7/1296 for details.
R package - Analysis of Single Cell Expression, Normalisation and Differential expression (ascend)
Cell type matching in single-cell RNA-sequencing data using FR-Match
The following repository contains code for all scRNAseq analysis and visualization performed in the paper: Single cell resolution analysis of the human pancreatic ductal progenitor cell niche
R package for single-cell RNA-sequencing analysis
A package for reference mapping and nice visualization
Granular Functional Filtering (Gruffi) to isolate stressed cells
Intercellular communication analysis for scRNA-seq data
Novel joint clustering method with scRNA-seq and CITE-seq data
Simple implementation of different single cell RNA-seq methods
iDA: dimensionality reduction for latent structure discovery
Display gene expression along a given reduced dimension on a heatmap
scBubbletree: quantitative tool for visual exploration of scRNA-seq data
This repository stores the scigenex R library.
R package: {rfca} Random forest-based cell annotation methods for scRNAseq analysis. {rfca} contains methods which identifies cell types using machine learning trained on a diversity of cell types, without the need for a labelled training dataset. It also allows you to train your own cell prediction models with your own labels (cell type, subtyp…
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