Pinned Repositories
CellCnn
Representation Learning for detection of phenotype-associated cell subsets
cellpose
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
cytopath
Simulation based inference of differentiation trajectories from RNA velocity fields.
DICSIT
matLeap
matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inference
MoESimVAE
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
MORESCA
This repository provides a template and some resources on standardized scRNA-seq analysis using Python.
pyPsupertime
Scalable reimplementation of psupertime in python
S3-CIMA
Supervised Spatial Single-Cell Image Analysis
STILT
Stochastic Inference on Lineage Trees
Claassen Lab's Repositories
claassenlab/cellpose
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
claassenlab/S3-CIMA
Supervised Spatial Single-Cell Image Analysis
claassenlab/pyPsupertime
Scalable reimplementation of psupertime in python
claassenlab/CellCnn
Representation Learning for detection of phenotype-associated cell subsets
claassenlab/cytopath
Simulation based inference of differentiation trajectories from RNA velocity fields.
claassenlab/DICSIT
claassenlab/matLeap
matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inference
claassenlab/MoESimVAE
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
claassenlab/MORESCA
This repository provides a template and some resources on standardized scRNA-seq analysis using Python.
claassenlab/STILT
Stochastic Inference on Lineage Trees
claassenlab/Teamproject-2023
Implementation of RNA velocity for estimation of single cell transcriptomic dynamics.
claassenlab/gleason_CNN
claassenlab/psupertime
psupertime is pseudotime ordering for single cell RNA-seq data with sequential labels
claassenlab/Tex_repro
Jupyter notebooks for reproducing the results of Gupta et al. 2024
claassenlab/TreeTop
TreeTop is an algorithm for single-cell data analysis to identify and assess statistical significance of branch points in biological processes with possibly multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T cell maturation, B cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallow hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy.