Introduction | Sample preparation and sequencing | Read alignment and expression quantitation | Loading the zebrafish data into R | Session info | References

Introduction | Human Neurogenesis | Visualizing Technical Variability and Batch Effects | Drop-out Characteristics | The scone Workflow | Sample Filtering with metric_sample_filter | On Threshold Selection | Applying the sample filter | Running and Scoring Normalization Workflows with scone | Creating a SconeExperiment Object | Defining Normalization Modules | Selecting SCONE Workflows | Calling scone with run=TRUE | Step 3: Selecting a normalization for downstream analysis | Session Info

Installation | Introduction | The ZINB-WaVE model | Example dataset | Gene filtering | ZINB-WaVE | Adding covariates | Sample-level covariates | Gene-level covariates | t-SNE representation | Normalized values and deviance residuals | The zinbFit function | Differential Expression | Differential expression with edgeR | Differential expression with DESeq2 | Using zinbwave with Seurat | Working with large datasets | A note on performance and parallel computing | Session Info | References

Overview | A typical differential expression analysis workflow | Filtering and exploratory data analysis | RUVg: Estimating the factors of unwanted variation using control genes | Differential expression analysis | Empirical control genes | Differential expression analysis with DESeq2 | RUVs: Estimating the factors of unwanted variation using replicate samples} | RUVr: Estimating the factors of unwanted variation using residuals | Session info | References

Introduction | Citation | Data Simulation | PsiNorm data normalization | Data Normalization with PsiNorm | Unsupervised approach: Adusted Rand Index | Supervised approach: Silhouette index | Correlation of PC1 and PC2 with sequencing depth | Using PsiNorm in scone() | Using PsiNorm with Seurat | Using PsiNorm with HDF5 files | Session Information

Basics | Install awst | Required knowledge | Asking for help | Citing awst | What does awst does? | Quick start | Role of normalization | Reproducibility | Bibliography

Motivation | Quick start | Design explanation | Simplifying to a SingleCellExperiment | Session information

Motivation | Creating SingleCellExperiment instances | Adding low-dimensional representations | Convenient access to named assays | Adding alternative feature sets | Storing row or column pairings | Additional metadata fields | Session information

Introduction | Using the internal fields | Rationale | Conflicts between packages | Using "Inception-style" nesting | Contacting us | Other design decisions | What's up with reducedDims? | Why derive from a RangedSummarizedExperiment? | Why not use a MultiAssayExperiment? | Session information

Installation | Introduction | Example dataset | mbkmeans | Choice of arguments | Batch size | Initialization | Running mbkmeans with multiple values of $k$ | Comparison with k-means | Use with bluster | Working with on-disk data in Bioconductor | References

Introduction | Reading in unaligned and aligned read data | Unaligned reads | Aligned reads | Read-level EDA | Numbers of unaligned and aligned reads | Read quality scores | Individual lane summaries | Read nucleotide distributions | Gene-level EDA | Classes and methods for gene-level counts | Between-lane distribution of gene-level counts | Over-dispersion | Gene-specific effects on read counts | Normalization | Offset | Differential expression analysis | edgeR | DESeq2 | Definitions and conventions | Rounding | Zero counts | Retrieving gene length and GC-content | SessionInfo | References