Deep probabilistic analysis of single-cell and spatial omics data
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Updated
May 30, 2024 - Python
Deep probabilistic analysis of single-cell and spatial omics data
Spatial Single Cell Analysis in Python
CellRank: dynamics from multi-view single-cell data
Single cell perturbation prediction
Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
Semi-supervised adversarial neural networks for classification of single cell transcriptomics data
A deep learning architecture for robust inference and accurate prediction of cellular dynamics
𝒫robabilistic modeling of RNA velocity ⬱
<<------ Use SnapATAC!!
Visualize cancer genomes with FAIR single-cell RNA-seq data
Finding surprising needles (=features) in haystacks (=single cell/spatial genomics data).
SCGV is an interactive graphical tool for single-cell genomics data, with emphasis on single-cell genomics of cancer
Single cell Perturbations - Analysis of Differential gene Expression
Inference of Disease Progressive Level in Single-Cell Data
Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing
10xGenomics CellRanger - Slurm Job Partitioner (SJP)
SCGid, a consensus approach to contig filtering and genome prediction from single-cell sequencing libraries
Pipeline to analyze Indrops V3 data using STARsolo
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