Introduction | Identifying doublet origins | kNN-based approach | Multi-label classifier | Doublet enrichment analysis | Session information

Installation | Usage | Cluster-based approach | Multiple samples | Description of the method | Splitting captures | Reducing and clustering the data | Generating artificial doublets | Examining the k-nearest neighbors (kNN) of each cell | Training a classifier | Thresholding | Doublet origins and enrichments | Some important parameters | Expected proportion of doublets | Number of artificial doublets | Frequently-asked questions | I'm getting way too many doublets called - what's going on? | Should I use the cluster-based doublet generation or not? | The clusters don't make any sense! | 'Size factors should be positive' error | Identifying homotypic doublets | What is a sample exactly? Usage with barcoded and 10X Flex data. | How can I make this reproducible? | Can I use this in combination with Seurat or other tools? | How can I call scDblFinder from the command line? | How should I multithread this? | Can this be used with scATACseq data? | Should I run QC cell filtering before or after doublet detection? | What about ambiant RNA decontamination? | Can I combine this method with others? | Session information

Introduction | Installing betterChromVAR | Computing motif deviations with betterChromVAR | Individual steps of betterChromVAR | Including fragment length bias | Note on single-cell data | Execution on Atlas-scale datasets | Bias-normalization of bulk ATAC-seq data | betterChromVAR with kNN-based background | Appendix | References | Session info

Introduction | Many ways of compiling coverage | Example heatmaps created using different bigwig generation procedures | Working with fragment files as an input | Session information

Plotting signals in a region | Merging signal from different tracks | Using an EnsDb object | Further track customization | Session info

tl;dr | Mathematical background | Normalization by library size | Testing for (lack of) intermediacy | Calling doublet clusters | Discussion | Session information

tl;dr | Algorithm overview | Size factor handling | Normalization size factors | RNA content size factors | Interactions between them | Doublet score calculations | Session information

Introduction | computeDoubletDensity | recoverDoublets | scDblFinder | directDblClassification | findDoubletClusters | Installation | Which method to choose? | Session information

Introduction | The PipelineDefinition | Example run | Exploring the metrics | Doublet detection and cell filtering | Evaluation based on the reduced space | Subpopulation silhouette | Variance in the PCs explained by the subpopulations | Correlation with covariates | Clustering | Metrics | Plotting | Computing time | Extension and reuse | Datasets | scRNAseq benchmark datasets used in the paper | Using new datasets

Getting read counts in regions of interest | Quality control | Coverage statistics | Fragment length distributions | TSS enrichment | Peak calling | Region merging | Region overlapping | Session information

Introduction | Reading signal in/around a set of regions | Extracting and manipulating signal matrices | Normalization | Plotting heatmaps | Color-scale trimming | Different colorscales for different tracks | Scaled regions | Heatmap rasterization | Sorting and clustering | Plotting aggregated signals | Visualizing DNAme and sparse signals | Session information

Introduction | Applying normalization factors when generating the bigwig files | Obtaining normalization factors for a set of signal files | Normalization methods | Obtaining normalization factors from the signal matrices themselves | Session information

Introduction | Applying the scDblFinder method | Using the Amulet method | Combining mehtods | The Clamulet method | Simple p-value combination | References | Session information

12-mer dissociation rates | KdModels | KdModelLists | Creating a KdModel object | Common KdModel collections | Under the hood | Session info

Getting started | Package installation | Example data | Example usage | Basic functionalities | Row ordering | Color scale | Annotation colors | crossHm | Other convenience functions | Session info

Scanning | Background | Basic Scan | Using a miRNA Seed | Using a miRNA sequence | Using a KdModel | About match types | Further Options | ORF length | Supplementary 3' pairing | Shadow and Overlapping Matches | Aggregation on the fly | Aggregating Sites | Basic Aggregation | Dealing with very large scans | Multithreading | Dealing with large collections of predictions | Session info

Getting started | Package installation | Example data | Merging and aggregating SEs | Merging by rowData columns | Aggregating a SE | Other convenience functions | Session info

tl;dr | Mathematical background | Obtaining explicit calls | Discussion | Session information

ScanMiRAnno objects | Convenience functions | Obtaining the UTR sequence of a transcript | Plotting sites on the UTR sequence of a transcript | Running a full-transcriptome scan | Detecting enriched miRNA-target pairs | Shiny app | Setting up the application | Multi-threading | Caching | Session info

IndexedFst | Storing GRanges as IndexedFst | More... | Multithreading | Under the hood | Session info

Introduction | Differential exon usage | Differential 3' UTR usage | Getting started | Package installation | Obtaining gene annotations | Workflow for differential exon usage (DEU) analysis | Preparing the annotation | Counting reads in bins | Differential analysis | Workflow for differential 3' UTR usage analysis | Obtaining alternative poly-adenlyation sites and preparing the bins | Counting and differential analysis: | Exploring the results | Top genes | Gene profiles | Overlaying with transcripts

Loading the collections | Session info

Introduction | Installation | pipeComp overview | Running only a subset of the combinations | Dealing with the PipelineDefinition object | Creating a PipelineDefinition | Manipulating a PipelineDefinition | Merging results of different runPipeline calls | Handling errors | Debugging | Skipping errors and fixing them afterwards | Plotting results | Running times

Introduction | Building the PipelineDefinition object | Preparing evaluation function | Assembling the PipelineDefinition | Example run | Building the wrappers | Defining the alternative parameter values to test | Benchmark datasets | Running the benchmark | Exploring the results