Introduction | Prerequisites | Reading H5AD files and Zarr stores to a Seurat Object | Mapping between AnnData and Seurat | Writing a Seurat object to a H5AD file or Zarr store | Session info

Introduction | Prerequisites | Reading H5AD files and Zarr stores to a SingleCellExperiment object | Mapping between AnnData and SingleCellExperiment | Writing a SingleCellExperiment object to a H5AD file or Zarr store | Session info

Introduction | Relationship to other packages | Installation | Usage | Read from disk | Using AnnData objects | Interoperability | Manually create an AnnData object | Write to disk | Subsetting AnnData objects | Basic subsetting | Combined subsetting | Using different index types | Working with views | Citing anndataR | Session info

Introduction | Prerequisites | Basic Integration with scanpy | Conversion to R objects | Multi-modal data with mudata | Session info | R | Python

Overview | Reading and writing H5AD files | Converting between SingleCellExperiment and AnnData objects | Progress messages | Session information

Overview | Reading and writing H5AD files | Converting between SingleCellExperiment and AnnData objects | Progress messages | Session information

The base Splat model | Global parameters | nGenes - Number of genes | nCells - Number of cells | seed - Random seed | Batch parameters | nBatches - Number of batches | batchCells - Cells per batch | batch.facLoc - Batch factor location and batch.facScale - Batch factor scale | batch.rmEffect - Remove batch effect | Mean parameters | mean.shape - Mean shape and mean.rate - Mean rate | Library size parameters | lib.loc - Library size location and lib.scale - Library size scale | lib.norm - Library size distribution | Expression outlier parameters | out.prob - Expression outlier probability | out.facLoc - Expression outlier factor location and out.facScale - Expression outlier factor scale | Group parameters | nGroups - Number of groups | group.prob - Group probabilities | Differential expression parameters | de.prob - DE probability | de.downProb - Down-regulation probability | de.facLoc - DE factor location and de.facScale - DE factor scale | Complex differential expression | Biological Coefficient of Variation (BCV) parameters | bcv.common - Common BCV | bcv.df - BCV Degrees of Freedom | Dropout parameters | dropout.type - Dropout type | dropout.mid - Dropout mid point and dropout.shape - Dropout shape | Path parameters | path.from - Path origin | path.nSteps - Number of steps | path.skew - Path skew | path.nonlinearProb - Non-linear probability | path.sigmaFac - Path skew | Session information

Introduction | Quick start | Detailed look into splatPop | Step 1: Parameter Estimation | Step 2: Simulate gene means | Input data | Control parameters | Output | Other examples | Step 3: Simulate single cell counts | Complex splatPop simulations | Cell-group effects | Conditional effects | Batch effects | Gene co-regulation | Session information

The base Splat model | Global parameters | nGenes - Number of genes | nCells - Number of cells | seed - Random seed | Batch parameters | nBatches - Number of batches | batchCells - Cells per batch | batch.facLoc - Batch factor location and batch.facScale - Batch factor scale | batch.rmEffect - Remove batch effect | Mean parameters | mean.shape - Mean shape and mean.rate - Mean rate | Library size parameters | lib.loc - Library size location and lib.scale - Library size scale | lib.norm - Library size distribution | Expression outlier parameters | out.prob - Expression outlier probability | out.facLoc - Expression outlier factor location and out.facScale - Expression outlier factor scale | Group parameters | nGroups - Number of groups | group.prob - Group probabilities | Differential expression parameters | de.prob - DE probability | de.downProb - Down-regulation probability | de.facLoc - DE factor location and de.facScale - DE factor scale | Complex differential expression | Biological Coefficient of Variation (BCV) parameters | bcv.common - Common BCV | bcv.df - BCV Degrees of Freedom | Dropout parameters | dropout.type - Dropout type | dropout.mid - Dropout mid point and dropout.shape - Dropout shape | Path parameters | path.from - Path origin | path.nSteps - Number of steps | path.skew - Path skew | path.nonlinearProb - Non-linear probability | path.sigmaFac - Path skew | Session information

Introduction | Quick start | Detailed look into splatPop | Step 1: Parameter Estimation | Step 2: Simulate gene means | Input data | Control parameters | Output | Other examples | Step 3: Simulate single cell counts | Complex splatPop simulations | Cell-group effects | Conditional effects | Batch effects | Gene co-regulation | Session information

Installation | Quickstart | The Splat simulation | The SplatParams object | Getting and setting | Estimating parameters | Simulating counts | Simulating groups | Simulating paths | Batch effects | Convenience functions | splatPop: Simulating populations | Other simulations | Other expression values | Reducing simulation size | Comparing simulations and real data | Comparing differences | Making panels | Citing Splatter | Session information

Installation | Quickstart | The Splat simulation | The SplatParams object | Getting and setting | Estimating parameters | Simulating counts | Simulating groups | Simulating paths | Batch effects | Convenience functions | splatPop: Simulating populations | Other simulations | Other expression values | Reducing simulation size | Comparing simulations and real data | Comparing differences | Making panels | Citing Splatter | Session information

What is a clustering tree? | A simple example | The data | Plotting a tree | Controlling aesthetics | SC3 stability index | Layout | Adding labels | Clustering trees for scRNA-seq data | SingleCellExperiment objects | Seurat objects | Using genes as aesthetics | Overlaying clustering trees | Choosing what to colour | Labelling nodes | Showing the side view | Modifying appearance | Legends | Citing clustree | References

Introduction | Installation | Usage | Parameters | Citation