Deep probabilistic analysis of single-cell and spatial omics data
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Updated
Jun 3, 2024 - Python
Deep probabilistic analysis of single-cell and spatial omics data
scBubbletree: quantitative tool for visual exploration of scRNA-seq data
An interactive explorer for single-cell transcriptomics data
scCTS: identifying the cell type specific marker genes from population-level single-cell RNA-seq
scGEAToolbox: Matlab toolbox for single-cell gene expression analyses
Enables cellxgene to generate violin, stacked violin, stacked bar, heatmap, volcano, embedding, dot, track, density, 2D density, sankey and dual-gene plot in high-resolution SVG/PNG format. It also performs differential gene expression analysis and provides a Command Line Interface (CLI) for advanced users to perform analysis using python and R.
A Julia package for single cell and spatial data analysis
Variational Inference for Cell Type Evolution
CITEViz: Replicating the Interactive Flow Cytometry Workflow in CITE-Seq
Single cell trajectory detection
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment
ThunderBio scRNA-seq pipeline
A tool for projecting genomic alignments to transcriptomic coordinates
A Snakemake workflow for performing differential expression analyses (DEA) on (multimodal) sc/snRNA-seq data powered by the R package Seurat.
A Snakemake workflow for performing and visualizing differential expression analyses (DEA) on NGS data powered by the R package limma.
A package that performs cell type annotations on single cell resolution of spatial transcriptomics data, find the niche interactions and covariation patterns between interacted cell types.
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