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RegVelo's reproducibility repository

RegVelo’s reproducibility repository

Complexity scale analysis with mouse neural crest and Schwann cell datasets

Split mouse neural crest data into different scale
Construct pySCENIC GRN
Preprocess GRN
RegVelo-based analysis on murine NCC cell
scVelo application on murine neural crest data
veloVI application on murine neural crest data
Benchmark transition from hub cells to terminal states
Evaluate consistency of velocity and cell fate probability across scale
Benchmark Terminal State Identification

Cell cycle (U2OS)

Cell cycle data preparation
RegVelo benchmark on cell cycle data
scVelo benchmark on cell cycle data
veloVI benchmark on cell cycle data
TFvelo benchmark on cell cycle data
UniTVelo benchmark on cell cycle data
veloVAE (fullvb) benchmark on cell cycle data
veloVAE (VAE) benchmark on cell cycle data
cell2fate benchmark on cell cycle data
DPT benchmark on cell cycle data
CellOracle benchmark on cell cycle
GRNBoost2 benchmark on cell cycle
SpliceJAC benchmark on cell cycle
Correlation benchmark on cell cycle
Performance comparison of inference on cell cycle
GRN influence on velocity inference with RegVelo
Visualize GRN
Prepare datasets with different neighborhood size
RegVelo benchmark on cell cycle data
veloVI benchmark on cell cycle data
Performance comparison of inference on cell cycle

Cell cycle (RPE1)

Cell cycle data preparation
RegVelo benchmark on cell cycle data
CellDancer benchmark on cell cycle data
Benchmark transcription rate prediction
Evaluate latent time prediction via redefining global latent time

mouse hematopoietic stem and progenitor cell (mHSPC)

Preprocess mHSPC datasets
Run RegVelo on mHSPC datasets
Run TFvelo on mHSPC datasets
Run other GRN inference methods on mHSPC dataset
Benchmark GRN inference
GRN calibration evaluation

Pancreatic endocrinogenesis

Basic preprocessing of the pancreatic endocrine dataset
RegVelo-based analyis of pancreatic endocrine data
scVelo-based analyis of pancreatic endocrine data
TSI comparison
Pancreatic endocrine cell fate analysis
Regulation is important for terminal state identification in pancreatic endocrine
E2f1 perturbation on pancreatic endocrine dataset
Evaluate RegVelo predicted E2f1 downstream targets
Robustness of RegVelo’s perturbation analysis
Perturbation prediction on pancreatic endocrine via RegVelo
veloVI-based analyis of pancreatic endocrine data
Benchmark cell fate and velocity identifiability
Analyzing perturbation simulation results on pancreatic endocrine dataset
Neurod2 target analysis

Hematopoiesis

Basic preprocessing and analysis of the human hematopoiesis dataset
Regularized RegVelo-based analysis of hematopoiesis dataset
RegVelo-based analysis of hematopoiesis dataset
scVelo-based analysis of hematopoiesis dataset
veloVI-based analysis of hematopoiesis dataset
Comparing lineage-associated gene rankings
Comparing uncertainty
Performance comparison of inference on hematopoiesis dataset
Dynamo-based drivers analysis
Perturbation prediction on hematopoiesis dataset via RegVelo
Comparison of driver TF ranking performance
PU.1 and GATA1 perturbation
Repeat run RegVelo and veloVI
Compare estimated velocity robustness between RegVelo and veloVI

Zebrafish neural crest

Basic preprocessing and analysis of the zebrafish data
RegVelo-based analyis of zebrafish data
scVelo-based analyis of zebrafish data
veloVI-based analyis of zebrafish data
Terminal state identification
Early driver perturbation prediction
TFs perturbation prediction with RegVelo
Driver ranking benchmark
CellOracle-based perturbation prediction
Dynamo-based perturbation analysis
Using RegVelo to predict perturbation
Perturbation prediction benchmark
Perturb-seq data analysis
elf1 perturbation simulation
Visualize regulon activity and expression dynamics
Run simple simulation via perturb nr2f5 targets
Perform ablation study to the regvelo

Smart-seq3 and perturb-seq combined analysis

Reannotation of pigment cells
Evaluate the cell fate perturbation significance on perturb-seq
Compute density depletion likelihood with RegVelo
Dynamo-based perturbation analysis
CellOracle-based perturbation prediction
Benchmark perturbation prediction

Toy GRN data

Simulation of toy GRN and cellular dynamics
RegVelo benchmark on toy GRN
scVelo benchmark on toy GRN
veloVI benchmark on toy GRN
DPT benchmark on toy GRN
CellOracle benchmark on toy GRN
GRNBoost2 benchmark on toy GRN
Correlation benchmark on toy GRN
Performance comparison of inference on toy GRN data
Plot the results of Toy simulate results

Dyngen-simulated data

Simulate synthetic datasets with dyngen
Dyngen data preparation
RegVelo benchmark on dyngen data
scVelo benchmark on dyngen data
veloVI benchmark on dyngen data
UniTVelo benchmark on dyngen data
VeloVAE (fullvb) benchmark on dyngen data
VeloVAE benchmark on dyngen data
cell2fate benchmark on dyngen data
CellOracle benchmark on dyngen data
GRNBoost2 benchmark on dyngen data
spliceJAC benchmark on dyngen data
Correlation benchmark on dyngen data
Performance comparison of inference on dyngen data
Using veloVI as baseline for RegVelo identifiability test
Prior GRN sensitivity evaluation
Plot downstream metrics

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By Weixu Wang, Philipp Weiler

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