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Introduction to BiocDuckDB19 hours ago
Introduction | Installation | The round-trip | The storage layout | Targeting the storage contract | Operations run as SQL | Genomic coordinates are preserved | Single-cell data | The filter, realize, analyze pattern | Multi-omics | When to use BiocDuckDB | Session information
Processing targeted LC-MS/MS lipidomics and metabolomics with MRManalyzeR22 hours ago
Introduction | Scope, and where this sits among related packages | Assumptions and limitations | How the package is organised | Installation | Quick start | Stage 1: data parse | The curated workbook | The TargetLynx export | The simulated dataset used from here on | Assembling a DatasetExperiment | Stage 2: peak-matrix processing | Blank filter | Normalise | Concentration adjustment | Impute missing values | Batch correction | The whole matrix in one call | Stage 3: QC check | How well was each compound measured? | Analytical drift | Normality | PCA | Loadings | What was reported, and what was dropped | Stage 4: statistics | Selecting the biological samples | Group summaries | Comparisons | Per-compound plots | Volcano | Heatmap | Feature correlations | Ion ratios | Driving it from a YAML config | Merging several acquisition panels | References | Session info
Savely Store MS Data Objects in a Portable Stash23 hours ago
Introduction | Installation | A stash for Spectra objects | Creating self-contained stashes | Stashes for Spectra with in-memory backends | Session information
Retrieve and Use Mass Spectrometry Data from Metabolomics Workbench1 days ago
Introduction | Installation | Importing MS Data from Metabolomics Workbench | General use and information retrieval from Metabolomics Workbench | Session information
SPAROscore: Transcriptomics Analysis Workflows1 days ago
Introduction | Example datasets | Bulk Transcriptomics Analysis | Load the dataset and the gene signature | Compute SPAROscore | Combine scores with the sample metadata | Compare treated and untreated samples | Statistical comparison | Visualising pathway activity on a PCA | Relationship between SPAROscore and individual marker genes | Summary | Single-cell RNA-seq analysis | Load the dataset from muscData | Load the gene signature from MSigDB | Comparing stimulated and control cells | Interferon activity across cell types | Visualising pathway activity on the UMAP | Efficiently scoring additional pathways | Pseudo-bulk analyses | Aggregate cells into pseudo-bulk profiles | Compute scores at pseudo-bulk level | Visualise pseudo-bulk level scores | Spatial transcriptomics analysis | Load the DLPFC dataset from SpatialLIBD | Visualise the tissue morphology | Defining a oligodendrocyte gene signature | Visualising pathway activity across the tissue | Pathway activity across anatomical regions | Conclusion
fastPLS Public API and Implementation2 days ago
Overview | Implementation Summary | SVD Algorithms | PLS Algorithms | Classifiers | Backends | Classification Tasks | Fit And Predict A Classifier | Evaluate Classification Predictions | Classification Heads | Kernel PLS For Classification | Classification Score Plots | Regression Tasks | Fit And Predict A Regression Model | Float32 Input | Evaluate Regression Predictions | OPLS For Regression | Cross-Validation | Ordinary k-fold CV | Grouped k-fold CV with constrain | Leave-one-out CV and leave-one-group-out CV | Component and hyperparameter optimization CV | Interpreting R2Y, Q2Y, and RMSD | Permutation-test p-values | Nested or double CV | PCA and SVD Utilities | CUDA and Metal Availability | Helper Functions | Implementation of the Main Methods | Singular-vector backends | PLSSVD | SIMPLS | OPLS | Kernel PLS | Classification heads | Method References | Session Information
Exploring QuickBLAST2 days ago
Introduction to QuickBLAST | Setup and Instance Management | Managing Memory | Raw Sequence Comparison | Remote NCBI Searching | File-to-File Comparisons | Local Database Creation and Searching | High-Performance Output with Apache Arrow | Conclusion
SPAROscore: Data Structures2 days ago
Supported input data structures | Example datasets | Load the gene expression dataset | Load the gene signatures | Gene expression as matrix-like data structures | Dense matrix | SparseMatrix | DelayedArrays | Data frame | Verify if the output scores are the same with differing input formats | Gene signature representations | Single signature as a character vector | Single signature as a GeneSet object | Multiple signatures as a named list | Multiple signatures as a GeneSetCollection object | SummarizedExperiment and derived objects | Seurat objects
SPAROscore: Getting Started2 days ago
What is SPAROscore? | How to use SPAROscore? | Load the dataset | Compute signature scores using SPAROscore | Visualise the results | What transcriptomics technologies can SPAROscore handle? | What data structures can SPAROscore handle? | What are some advanced capabilities of SPAROscore?
Maximum Diversity Clustering3 days ago
Introduction | Background | A complete example | Initialization | Clustering parameters | Optimization parameters | Output | Relationship to Harmony | Session information
Introduction to DuckDBArray4 days ago
Introduction | Installation | Quick start | Working with a DuckDBMatrix | Writing a matrix to the DuckDB backend | Subsetting | Matrix statistics | Sparse data | Lazy evaluation | Realizing large results in blocks | N-dimensional arrays | When to use DuckDBArray | Session information
Introduction to ClinicalVariantR7 days ago
Overview | Installation | Launching the Shiny app | Input requirements | Scope and limitations | Session info
Running and testing ClinicalVariantR7 days ago
Purpose | Prerequisites | Launch the Shiny app | Preferred Bioconductor API | Source-tree launch (development) | Session settings useful for testing | Test data locations | Interactive testing checklist (UI) | Group B - Automated prediction (start here) | Group C - Gene panel | Group A - Full clinical | Automated testing (command line) | Unit tests (testthat) | Engine / pipeline scripts | Optional upload-flow smoke script | Optional Bioconductor / style checks (before submission) | Expected app object (non-interactive sanity check) | Troubleshooting while testing | Recommended smoke sequence (15 minutes) | Session info
MetaPathNet: network analysis of the choline-TMA-TMAO host-microbiome axis8 days ago
Introduction | Installation | Workflow | Overview | Identifier mapping | Network construction | Network extension | Topology analysis | Origin annotation | Permutation test | Pathway enrichment analysis | Conclusion | References
Getting Started with pepVet9 days ago
The core pipeline | Protein input | Step 1: Digest a protein | Step 1a: Inspect cleavage efficiency | Step 2: Score the peptide set | Scoring model | Known limitations | Scope | Step 2a: Apply a workflow preset | Step 2b: Add proteome-aware uniqueness | Step 3: Evaluate in one call | Step 4: Compare enzymes | Step 5: Get the top model rank | Step 6: Batch across a proteome | Step 6a: Summarize a batch | Step 6b: Triage proteins | Step 6c: Export a peptide list | Step 7: Quick interactive check | Step 8: Report to the console | Additional examples and references | A challenging protein: Histone H3.1 | Amino acid reference data | How pepVet differs from common tools | Related articles | Session info
pepVet Compared to Other Tools9 days ago
Comparison basis | Common ground | How tools differ | Worked comparisons | pepVet scoring in practice | Workflow preset effects | Pipeline with PeptideRanger | Binary versus graded: pepVet and Protein Cleaver | Choosing an output type | References | Session info
Detecting human-mouse multiplets in PDX single-cell data with multipletR9 days ago
multipletR | The problem | The approach | Installation | The input: Cell Ranger's GEM classification file | How the data is generated | Where to find it | What it contains | Why this is the input multipletR needs | Column-name flexibility | Detecting multiplets | Reading the diagnostic plots | Adjusting the thresholds | Using the results with Seurat or SingleCellExperiment | Functions | How it works | Session info
GPlinksR: Building Gene-Peak Links from Example Single-Cell Inputs9 days ago
Introduction | Installation | Overview | Minimal Example with Real Peak and Gene Vectors | Using a Peak Data Frame | Wrapper Workflow for MultiAssayExperiment | Wrapper Workflow for SingleCellExperiment | Input Expectations | Session Info
faissR: nearest neighbours, graphs, and clustering9 days ago
Overview | Example data | Backends | Nearest-neighbour search | Automatic method selection | Metrics | Float32 input and output | Reusing fitted indexes with kNN models | k-means | Optional CUDA use | Session information
Preprocessing an HT-SELEX study12 days ago
Introduction | Installation | A complete in-memory workflow | Reading FASTQ and public metadata | References | Session information
cellpaintr Vignette13 days ago
Introduction | Installation | Workflow | Preparation | Load Data | Data Cleaning and Transformation | Unsupervised Analysis | Supervised Analysis | Session Info
Generating bigwig tracks with bam2bw13 days ago
Introduction | Many ways of compiling coverage | Example heatmaps created using different bigwig generation procedures | Working with fragment files as an input | Session information
Visualizing signals in a single region13 days ago
Plotting signals in a region | Merging signal from different tracks | Using an EnsDb object | Further track customization | Session info
scMAGeCK13 days ago
Introduction | Usage | scmageck_rra | scmageck_lr | Output | Contact us | Session info
scCertify: Explainable Confidence Scoring for Single-Cell Annotations14 days ago
Introduction | Installation | Workflow Overview | Input Requirements | Marker Database | Marker Consistency Score | Neighborhood Agreement Score | Entropy-Based Uncertainty | Computing Confidence Scores | Custom Confidence Thresholds | Explaining Confidence | Interpreting Results | Compatibility | Summary | References | Session Information
rvarsim: Variant Simulation with HGVS Notation14 days ago
Introduction | Motivation | Comparison with existing tools | Terminology | Installation | Parsing and Validating HGVS Variants | Format Conversion: HGVS ↔ VCF ↔ SPDI | Variant Extraction from Sequences | Normalization | Simulating Variants (requires genome data) | Genomic-to-Coding Coordinate Mapping | Translation and Backtranslation | Liftover Between Assemblies | Interoperability with Other Bioconductor Packages | References | Session Information
Cloud Storage Access for GDS Files15 days ago
Introduction | Installation | Supported URL Schemes | Quick Start | Authentication | HTTP/HTTPS | Amazon S3 | Google Cloud Storage | Azure Blob Storage | URL-specific credentials | Cache Control | Integration with SeqArray | Session Information
Introduction to DuckDBSpatial15 days ago
Introduction | Installation | Lazy spatial columns | Spatial predicates and filtering | Layer-level engines for coordinate columns | GeoParquet I/O | When to use DuckDBSpatial | Session information
Design and extension of DuckDBSpatial15 days ago
Scope | Architecture | From sf generics to spatial SQL | Two dispatch paths | GeoParquet I/O | Extending, and the BiocDuckDB integration | Session information
Introduction to multipointR16 days ago
Introduction | Installation | Setup | Dataset | Inference with parametric point process models | The inhomogeneous Poisson point process | The Gibbs point process | Single image inference | Model of homogeneous intensity | Model of inhomogeneous intensity | Model of distance to immune cells | Model of segmented objects | Multi-image inference | Fitting a model per image | Second-stage model of coefficients | Fitting a shared model across images | Session Information | References
Benchmarking BiocDuckDB16 days ago
Introduction | What BiocDuckDB optimizes | A small, live comparison | Benchmark setup | Results | Main takeaways | When this matters | Running your own benchmarks | Session information
Introduction to MultiAssaySpatialExperiment17 days ago
Installation | Citing MultiAssaySpatialExperiment | Why another spatial data structure? | When to use MASE | Anatomy of a MultiAssaySpatialExperiment | Overview | Components | ExperimentList: assay data | colData: specimen metadata | sampleMap: assay-to-specimen mapping | points: spatial coordinates | shapes: spatial geometries | images: raster images | labels: segmentation masks | spatialMap: assay-to-spatial mapping | imgData: specimen-to-image mapping | Quick Start: Building a MASE object | Accessors | Inherited from MultiAssayExperiment | Spatial element accessors | Mapping accessors | Subsetting | Spatial operations | Coercion | Quick reference | Next steps | Session info
Working with MultiAssaySpatialExperiment17 days ago
Introduction | Building MASE objects | prepMASE and buildSpatialMap | Optional: lazy Parquet I/O via BiocDuckDB | Minimal MASE | Adding spatial coordinates | Adding spatial shapes | Building from multiple SingleCellExperiment objects | Validation checklist | Subsetting operations | Overview of subsetting operations | Basic subsetting | Subset by specimen (colData) | Subset by assay columns | Bracket notation | Spatial subsetting | Subset by bounding box | Subset by polygon | Multi-region subsets | Buffer-based subsets | Spatial annotation and aggregation | Example setup | Annotate with regions | Aggregate by region | Count points per region | Sum expression per region | Mean expression per region | Lower-level spatial joins | Aggregation strategies | Visualizing aggregated data | Labels ↔ shapes interoperability | Rasterization: shapes → labels | Vectorization: labels → shapes | Use cases | Next steps | Session info
MultiAssaySpatialExperiment use cases17 days ago
Introduction | Part 1: Data Import | Reading Xenium data | Basic usage | Options | When to load transcripts | Reading Visium data | Visium HD support | Reading CosMx data | FOV (Field of View) handling | Reading MERSCOPE data (MERFISH) | Choosing a reader | Converting from other Bioconductor classes | From SpatialExperiment | From SpatialFeatureExperiment | Reading multiple samples | Part 2: Real-World Workflows | Workflow 1: Xenium + Visium integration | Background | Data preparation | Build MASE object | Exploratory analysis | Visualize spatial distribution | Gene expression overlap | Spatial annotation: Cell types in Visium spots | Visualize deconvolution results | Spatial aggregation: Summarize Xenium by regions | Comparative analysis | Integration insights | Workflow 2: Coordinate transformation and alignment | Converting Visium grid to microns | Applying affine transformations | Landmark-based alignment | Storing transformed coordinates | Visualizing alignment | Workflow 3: Multi-sample analysis | Workflow 4: Iterative analysis pattern | Summary | Session info
Introduction to DuckDBGRanges17 days ago
Introduction | Installation | Quick start | Working with a DuckDBGRanges | Accessors | Subsetting | Range operations | Filter, then materialize | Grouped ranges: DuckDBGRangesList | When to use DuckDBGRanges | Session information
Benchmarking DuckDBGRanges17 days ago
Introduction | What DuckDBGRanges optimizes | A small, live comparison | Benchmark setup | Results | Main takeaways | Backend comparison | When to use each | Running your own benchmarks | Session information
Design and extension of DuckDBGRanges17 days ago
Scope | Architecture | Slots | The five required columns | From range operations to SQL | Grouped ranges as LIST columns | Materialization and interoperability | Session information
Introduction to DuckDBDataFrame17 days ago
Introduction | Installation | Quick start | Working with a DuckDBDataFrame | Column access | Row subsetting | Computed columns | Column metadata | Columns come in three flavors | Reaching for SQL directly | When to use DuckDBDataFrame | Session information
Design and extension of DuckDBDataFrame17 days ago
Scope | The classes | The DuckDBTable abstraction | Construction | The contract | Key-dimension semantics | From R to SQL | Connection management | Dimension tables | Extending DuckDBDataFrame | Session information
Benchmarking DuckDBArray17 days ago
Introduction | What DuckDBArray optimizes | A small, live comparison | Benchmark setup | Giving every backend its best effort | Results | Main takeaways | Backend comparison | When to use each | Running your own benchmarks | Session information
Implementing the DuckDBArray backend17 days ago
Scope | Architecture | The seed contract | Shape: dim() and dimnames() | Dense blocks: extract_array() | Sparse blocks: extract_sparse_array() | Wrapping the seed | From array operations to SQL | Grid partitioning | Realization and interoperability | Session information
Vignette of the sbivar package17 days ago
Introduction | Installation | Multithreading | Example analysis on data by Vicari et al. (2024) | Data exploration | Single-image analysis | Multi-image analysis | Other common input formats | SpatialExperiment and MultiAssayExperiment | Single-image | Multi-image | AnnData | Troubleshooting | Sbivar takes forever | Job did not deliver a result | Warnings | Variogram fitting | GAM fitting | gfortran not found on Mac | Session info | Bibliography
GSE142512 DNA methylation data18 days ago
Accessing the resources | Data availability | Session information
spatialdataR18 days ago
Preamble | Installation | Introduction | Representation | Handling | Accession | Subsetting | Internals | Annotations | Transformations | Utilities | Cropping | Masking | Querying | Combining | Coordinates | Appendix | Resources | Session info | References
GSE280465 EPICv2 methylation data18 days ago
Accessing the resource | Data availability | Session information
SimiCviz: Visualization and Analysis of Gene Regulatory Networks19 days ago
Introduction | Relationship to Similar Packages | Installation | Loading GRN Data | Example Dataset | Data Format Requirements | SimiCPipeline Outputs | Manual Loading | Weights from pickle | Weights from other methods | Example: Generic CSV Format | Cell Labels / Phenotype Annotations | Expression Matrix | Create a SimiCvizExperiment Object | Computing Activity Scores (AUC) | Quick Start | Advanced: AUCProcessor Workflow | Filtering Options | Network Visualization | Quality Assessment (SimiC only) | Weights Visualization | TF Barplots: | Target Barplots: | Regulatory Network Heatmap | Dissimilarity Analysis | Global dissimilarity scores | Dissimilarity heatmap | Activity Score Distributions | Density plots | Cumulative distributions (ECDF) | ECDF-based Metrics | Summary Statistics | Mean activity per TF × phenotype | Box plots and violin plots | Session Info
Accessing PTMsToPathwaysData from ExperimentHub22 days ago
Connect to ExperimentHub | Find PTMsToPathwaysData records | Download a resource | Inspect downloaded objects | Session info
TiDEomics Tutorial23 days ago
Introduction | Installation | Example input | Preprocessing for different data types | Workflow | Data preparation | Optional: custom color palette | Quality control | Normalisation to starting time point | Splitting groups and merging replicates | Sample relationships (correlation, PCA, UMAP) | Pairwise differential expression | Feature properties and classification | Segmented regression analysis | Variance decomposition | WGCNA (co-expression modules) | Functional enrichment | Integrated visualisation of WGCNA modules and functional enrichment | Universal: plot features of interest | One-step preprocessing with prepare_tide() | Interoperability within Bioconductor ecosystem | Common usage scenarios | Session information | References
Generating Consensus TADs with generate_tad_consensus30 days ago
Introduction | Function Overview | Parameters | Return Value | Usage Examples | How It Works | The Measure of Concordance (MoC) Score | Dynamic Programming for Optimal TAD Selection | Important Notes
tTEscanR User Guide1 months ago
Overview | Workflow | 1. Loading the data | 2. Setup the tTEscanR object | 2.1 Pre-processing | 2.2. Defining the tTEscanR object | 3. Standard workflow | 3.1. Codon usage assessment | 3.2. Anticodon usage assessment | 3.3. Amio acid level assessment | 3.4. Theoretical Translation Efficiency (tTE) computation | 4. Differential expression analysis | 5. References
tTEscanR tRNA-Specific Preprocessing Module1 months ago
1. Overview | 2. Obtaining the tRNA matrix | 3. Identifying the optimal tRNA cutoff
tTEscanR Codon Frequency-per-Gene Matrix1 months ago
Overview
tTEscanR Visualization Module1 months ago
1. Overview | 2. Configuration options | 2.1. Data transformation | 2.2. Parameters | 3. Visualization options | 4. References
karioCaS: Kraken Confidence Scores Exploration1 months ago
Introduction | 1. Setting up the Environment | 2. Data Harmonization | 3. Visual Exploration (Steps 001 to 005) | 4. Objective Thresholding: The Stability Index (SI) | 5. The Ultimate Biological Mosaic | Session Info
Introduction to CySA1 months ago
Overview | Installation | Quick start | Statistical comparison | Static plots | Session information
Co-culture screen1 months ago
Preparation | Calculating differential abundance | Plotting the screen results
Quality control1 months ago
Preparation | Barcoded construct tables | Creating a sample sheet | Read structure and counting | Read structure definition | Counting sample and construct barcodes | Quality Control plots | Plotting total barcode counts | Representation of individual barcodes
Introduction to WOVEN: Multi-Omics Integration for Incomplete Patient Data1 months ago
Overview | Installation | Preparing Your Data | Input format | Pre-processing recommendations | Quick Start | Simulate a three-modality dataset | Induce block-level missingness | Fit WOVEN | Exploring Results | Latent space scatter | VIP scores: which features matter most? | Feature loadings: direction of effect | Variance explained: choosing K | Quantitative metrics | Predicting New Subjects | Comparing WOVEN and DIABLO: Effective Sample Size | Accessing Raw Results | Session Info
Local Analysis of Plant Genomes with PlantTxDbHub1 months ago
Installation | Introduction | Finding available species | Downloading the databases | Loading a TxDb (Arabidopsis example) | Available columns and keys | Retrieve all genes | Retrieve all transcripts | Retrieve all exons | Filter by gene ID | Retrieving transcript types | Working with chromosome names | Soybean (Glycine max) example | Rice (Oryza sativa) example | Contributing new species | Session information
CMEnt Configuration1 months ago
Overview | Function Parameters | Core Input Parameters | beta | seeds | pheno | Sample Grouping Parameters | sample_group_col | casecontrol_col | ignored_sample_groups | Array and Genome Parameters | array | genome | Filtering Parameters | ext_site_delta_beta | min_seeds | min_adj_seeds | min_sites | Region Building Parameters | max_lookup_dist | expansion_window | max_bridge_seeds_gaps | max_bridge_extension_gaps | Statistical Parameters | max_pval | entanglement | testing_mode | empirical_strategy | ntries | mid_p | aggfun | Performance Parameters | njobs | verbose | Input/Output Parameters | seeds_id_col | output_prefix | beta_row_names_file | BED File Parameters | bed_provided | bed_chrom_col | bed_start_col | Annotation Parameters | annotate_with_genes | .score_dmrs | Advanced Parameters | .load_debug | Global Package Options | Parallelism | Option: CMEnt.njobs | Verbosity | Option: CMEnt.verbose | Memory Management | Option: CMEnt.beta_in_mem_threshold_mb | Caching | Option: CMEnt.use_annotation_cache | Option: CMEnt.annotation_cache_dir | Option: CMEnt.jaspar_cache_dir | Motif Analysis | Option: CMEnt.jaspar_version | Option: CMEnt.jaspar_tax_group | Option: CMEnt.min_motif_similarity | Option: CMEnt.jaspar_corr_threshold | Option: CMEnt.make_debug_dir | DMR scoring | Option: CMEnt.scoring_nfold | Configuration Examples | Example 1: High-Confidence DMRs with Strict Filtering | Example 2: Broad Region Detection with Relaxed Parameters | Example 3: Empirical P-values for Small Sample Sizes | Best Practices | Troubleshooting | Issue: Out of Memory Errors | Issue: DMRs Too Small | Issue: Too Many DMRs | Issue: Slow Performance | Session Info
Overview of loopcityData1 months ago
Overview | Shipped datasets | ExperimentHub datasets | Data provenance | Session info
Geneslator: an R package for comprehensive gene identifier conversion and annotation1 months ago
Introduction | Installation | Load the package | Import annotation databases | Columns and values of annotation databases | Query the annotation databases | Search options | Search using aliases | Search using archived identifiers | Orthologs mapping | Session Information | References
PolyICSFlow: Identifying the Frequency of Polyfunctional Antigen-Specific T cells in ICS Flow Cytometry Data1 months ago
1) Installation | 2) Preparing the data | 2.1) Data pre-processing | 2.2) Cytokine gating | 2.3) Defining cell populations | 4) Using polyICSFlow | 4.1) Assigning cytokine positivity | 4.2) Assigning marker combinations | 4.3) Calculating peptide-specificity | 4.3.1) Example 1: Quantifying number of functions | 4.3.2) Example 2: Quantifying individual marker combinations | 4.3.3) Example 3: Quantifying polyfunctional IFN+ responses | 5) Plotting options | 6) Session info
CMEnt End-to-End Example1 months ago
Goal | Setup | Load Example Inputs | DMR Assembly from Seeds | DMR Summary | Motif Discovery | Motif-Based DMR Interactions | Visualizations | DMR Score Manhattan Plot | Single DMR Plot | Summary Circos Plot | CMEnt interactive Shiny App | Session Info
IntegratedLearner1 months ago
Load Packages | Input Data Contract | Alternative Input Mode: MAE (Complete Binary Example) | Parameter Reference (Conbin, Multiclass, and Survival) | Common Wrapper Parameters (IntegratedLearner) | Conbin-Specific Parameters (Continuous/Binary Path) | Multiclass-Specific Parameters (Native Multiclass Path) | Survival-Specific Parameters (via ...) | Supported Models and Fusion Modules | Supported Models | Supported Fusion Outputs | Output Reference: What You Get and How to Access It | Conbin Outputs (Binary/Continuous) | Multiclass Outputs | Survival Outputs (Single/Early/Late) | Importance Outputs (Conbin, Multiclass, and Survival) | Quick Access Snippets | Example 1: Binary Outcome (IBD Classification) | Step 1: Load and Inspect Training and Validation Data | Step 2: Build PCL Inputs | Step 3: Fit the Model | Step 4: Inspect and Interpret Outputs | Example 2: Continuous Outcome (Gestational Age) | Step 1: Load and Inspect Data | Step 2: Build PCL Input | Step 3: Fit Continuous Model | Step 4: Evaluate Predictive Accuracy | Step 5: Uncertainty and Feature-Level Interpretation (BART) | Example 3: Multiclass Outcome (Franzosa MAE with External Validation) | Example 4: Survival Outcome (Time-to-event) | Interpret Survival Outputs | Session Information | References | Citation
scOverlay: layered visualization of single-cell embeddings2 months ago
Introduction | Installation | Quick start | Loading the package and example data | The basic overlay model | Choosing the foreground and background | Plotting several genes | Splitting plots by groups | Shared scales and legends | Nested group plots | Palettes | Categorical palettes and Named categorical palettes | Rasterizing dense point layers | Saving plots | Session information
Getting Started with grayleafspotr2 months ago
What is grayleafspotr? | Quick start with bundled data | View the feature table | Template plots | Convert to a plain data frame | Analyze your own images | Reload saved results | Next steps
grayleafspotr Workflow2 months ago
Abstract | 1. Package overview | What the package does | Input requirements | Workflow at a glance | 2. Installation | 3. Bundled example data | 3.1 Load the example run | 3.2 Inspect the feature table | 3.3 Locate bundled source images | 4. Template visualisations | Colony expansion over time | Growth rate and edge roughness | Crack coverage and count | Texture organisation | Shape versus stress | Feature correlation heatmap | Radial intensity profile | 5. Work with tidy data | Custom plot | 6. Analyze your own images | 6.1 Prepare your image folder | 6.2 Run the analysis — simple entry point | 6.3 Full-featured alternative: grayleafspot_analyze() | 6.4 Reload saved results | 7. Developer note: Python override | Session information
Interactive WebGL Scatterplots with reglScatterplot2 months ago
Introduction | Installation | A first plot | Categorical colour mapping | Continuous colour mapping | Working with Bioconductor data structures | Linking plots in Shiny | Network requirements | Session info
Scaling reglScatterplot to millions of points2 months ago
What works at what scale | How the wire format works | A benchmark you can run yourself | Sizing inside the host viewport | Memory levers for very large data | Comparison with other R packages | Where the next jump comes from | Session info
PACMOSData2 months ago
Overview | Installation | Load package | Available datasets | MESOMICS datasets | lungNENomics datasets | Test datasets | Data access | Related package
Multi-Omics Integration in sciNOME2 months ago
Introduction | 1. Prepare Mock Multi-Omics Data | 1.1 Global Metadata | 1.2 Region Dictionary | 1.3 Simulate Omics Matrices | 2. Multi-Omics Integration | Mode A: Tri-Omics Integration (RNA + CpG + GpC) | Mode B: Dual Integration (RNA + CpG) | Mode C: Epigenetics Only Integration (CpG + GpC) | Session Information
Visualization Gallery in sciNOME2 months ago
Introduction | 1. Single-Cell RNA Visualization | 1.1 Quality Control Plot | 1.2 Dimensionality Reduction Plot | 1.3 Volcano Plot for DEA | 1.4 Trajectory & Pseudotime Plot | 2. Epigenetic Visualization | 2.1 Epigenetic Landscape (Ridge Plot) | 2.2 Dimensionality Reduction for Epigenetics | 2.3 Epigenetic Volcano Plot | 3. Multi-Omics Integration Plots | 3.1 Omics Correlation Matrix | 3.2 Pairwise Scatter Plots | Session Information
Epigenetic Data Processing and Analysis using sciNOME2 months ago
Introduction | 1. Setup and Mock Data Generation | 2. Data Aggregation | 3. Extracting specific Metrics and Quality Control | 4. Dimensionality Reduction | 5. Differential Epigenetic Analysis | Session Information
Single-Cell RNA Analysis Pipeline in sciNOME2 months ago
Introduction | 1. Data Preparation | 2. Object Construction and QC | 3. Dimensionality Reduction | 4. Unsupervised Clustering | 5. Differential Expression Analysis (DEA) | 6. Trajectory Inference (Pseudotime) | Session Information
TxParq.Hs.gencode.v49: Parquet transformation of TxDb.Hs with Gencode v492 months ago
Introduction | Rich Gene Queries | Get ALL genes with FULL attributes | Filter by gene_type | Filter by annotation confidence level | Rich Transcript Queries | Exon Queries with Context | CDS and Protein Information | UTRs and Codons | Transcripts by Gene | Region Queries | Performance Tips | Session information
How to run MOTL: basic example3 months ago
Introduction | Toy datasets in MOTL | Main steps | Load libraries | Learning dataset Lrn | Learning dataset metadata | Learning dataset factorization model | Learning dataset initialization | Target dataset Trg | Transfer learning inputs | Transfer learning using MOTL | Session info | References
Miscellaneous epiwraps functions3 months ago
Getting read counts in regions of interest | Quality control | Coverage statistics | Fragment length distributions | TSS enrichment | Peak calling | Region merging | Region overlapping | Session information
Visualizing signals across many regions3 months ago
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
Normalizing genomic signals3 months ago
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
LIPIDIFy: A Complete Lipidomics Analysis Workflow3 months ago
Overview | Step 1 - Load the Example Dataset | Step 2 - Lipid Classification | Step 3 - Raw Data Visualization | Step 4 - Normalization | What methods are available? | Compare two pipelines side-by-side | Apply the chosen pipeline | Step 4.5 - Missing Value Imputation | Step 4.6 - Batch Effect Correction | Step 5 - Quality Control with PCA | Step 6 - Differential Analysis | Automatic pairwise contrasts | Run limma | Top results from the first contrast | Step 7 - Results Visualization | Volcano plot | Heatmap of significant features | Step 8 - Lipid Expression | Step 9 - Enrichment Analysis | Session Information
LIPIDIFy: Comprehensive Lipidomics Data Analysis3 months ago
Introduction | Installation | Quick Start | Using the Shiny Interface | Using R Functions | Data Input and Format | Required Data Structure | Example Data | Loading Your Own Data | Lipid Classification | Automatic Classification | Custom Classification | Export Classification | Data Visualization | Raw Data Exploration | Individual Lipid Expression | Normalization | Missing Value Imputation | Available Methods | Batch Effect Correction | Normalization Methods Available | Principal Component Analysis | Differential Abundance Analysis | Using limma | Using EdgeR | Comparing Methods | Contrast Selection | Visualization of Results | Volcano Plots | Heatmaps | Enrichment Analysis | Standard Enrichment | Custom Enrichment Sets | Enrichment Visualization | Complete Analysis Workflow | Advanced Topics | Integration with Other Tools | Quality Control | Session Information | References | Appendix: Supported Lipid Nomenclature
The buzz on using bHIVE3 months ago
Introduction | Parameters for bHIVE() | How bHIVE Works | Example of Parameter Selection | Applications | Clustering | Classification | Hyperparameter Tuning | Using the caret wrapper | Applying the caret-based model | honeycombHIVE: Multilayered bHIVES | Gradient-based Refinement | Applying Refinement to honeycombHIVE | Visualizing Results with visualizeHIVE | Example 1: Scatterplot for Clustering Results | Example 2: Violin Plot for Classification Results | Conclusions
OmicsLake Comprehensive Guide4 months ago
Introduction | Before you run all chunks | Main package features | 1. Basic operations | 1.1 Project initialization | 1.2 Writing and reading tables | 1.3 Advanced analysis with SQL queries (ol_query) | JOINs and subqueries | Lazy evaluation and integration with dplyr | 1.4 Advanced analysis with aggregation and window functions | Calculating gene expression statistics | Ranking genes | Moving average and cumulative sum | Combining with lazy evaluation | 1.5 Saving and loading R objects | 2. Tracking dependencies | 2.1 Saving data with specified dependencies | 2.2 Checking dependencies | 2.3 Importing and exporting Parquet files | Parquet export | Parquet import | Performance tips | 3. Version management | 3.1 Tagging and labeling | 3.2 Creating multiple versions | 3.3 Listing and comparing versions | 3.4 Loading a specific version | 4. Commits and history management | 4.1 Creating commits | 4.2 Viewing history | 5. Visualizing dependencies | 5.1 Creating dependency graphs | 6. Restoring a project | 6.1 Restoring state using labels | 7. Advanced features | 7.1 Filtered data loading | 7.2 Loading with lazy evaluation | 7.3 Deleting tables | 8. Bioconductor integration | 8.1 Creating a SummarizedExperiment | 8.2 Creating a MultiAssayExperiment | 9. Practical workflow example | 9.1 Complete differential expression analysis workflow | 10. Conclusion | List of key functions | Project management | Data saving and loading | Version management | History management | Dependencies | Listing | Bioconductor | Others | Best practices | Session Information
OmicsLake Layer-by-Layer Use Cases4 months ago
Introduction | Before you run all chunks | Covered layers | Initialization | 1. Bulk RNA-seq (SummarizedExperiment) | 2. Single-cell RNA-seq (SingleCellExperiment) | 3. Multi-omics cohort (MultiAssayExperiment) | 4. Proteomics / Metabolomics raw MS (Spectra) | 5. Proteomics quantification graph (QFeatures) | 6. Integrated LC-MS container (MsExperiment) | 7. Cross-layer release manifest | 8. Validation with Tutorial-Derived Data | 8.1 Official MultiAssayExperiment data (miniACC) | 8.2 Official Spectra data (fft_spectrum) | 9. Automated Effectiveness Checks | Summary | Session Information
OmicsLake Practical Workflow: Integrating Phase 1-4 Functions4 months ago
Introduction | Before you run all chunks | Project setup | Preparing the data | Phase 1: Filtering data with SQL queries | Phase 2: Statistical analysis with aggregation functions | Calculate statistics for each gene | Extract top expressed genes | Add rankings | Compute cumulative statistics | Phase 3: Sharing results with Parquet | Exporting results | Importing external data | Phase 4: Compare multiple versions with database views | Scenario: Compare two different analysis methods | Create a view: Compare methods | Using the view: Check concordance | Create multiple views | List views | Deleting views | Integrated workflow example: Combine all features | Practical tips and best practices | Use cases for Phase 1 (SQL queries) | Use cases for Phase 2 (aggregation functions) | Use cases for Phase 3 (Parquet) | Use cases for Phase 4 (views) | Combination patterns | Summary | Session Information
OmicsLake v2.0 Quick Start Guide4 months ago
Introduction | Before you run all chunks | Recommended Next Guides | Key Features | Installation | Basic Usage | Initializing a Lake | Storing and Reading Data | Filtering with Formula Syntax | dplyr Integration (Automatic Lineage Tracking) | Joining Multiple Tables | QueryBuilder | Version Control | Snapshots and Tags | Viewing History | Lineage (Data Provenance) | Checking Dependencies | Lineage Visualization | Lightweight Mode (Integrating with Existing Code) | observe: Track without Code Changes | wrap: Wrap Functions for Tracking | Defining Pipelines | Custom Operators | Bracket Notation | Import/Export | Bioconductor Integration | Global Shortcuts | Migration from Legacy API | Summary | Session Information
OmicsLake v2.0 クイックスタートガイド4 months ago
はじめに | 全コードを実行する前に | 次に読むガイド | 主な特徴 | インストール | 基本的な使い方 | Lakeの初期化 | データの保存と読み込み | Formula構文でフィルタリング | dplyr統合(自動リネージ追跡) | 複数テーブルのJOIN | QueryBuilder | バージョン管理 | スナップショットとタグ | 履歴の確認 | リネージ(データ系譜) | 依存関係の確認 | リネージの可視化 | 軽量モード(既存コードへの導入) | observe: コード変更なしで追跡 | wrap: 関数をラップして追跡 | パイプラインの定義 | カスタム演算子 | ブラケット記法 | インポート/エクスポート | Bioconductor統合 | グローバルショートカット | 旧APIからの移行 | まとめ | Session Information
OmicsLake レイヤー別ユースケース集4 months ago
はじめに | 全コードを実行する前に | 対象レイヤー | 初期化 | 1. Bulk RNA-seq (SummarizedExperiment) | 2. Single-cell RNA-seq (SingleCellExperiment) | 3. Multi-omics cohort (MultiAssayExperiment) | 4. Proteomics / Metabolomics raw MS (Spectra) | 5. Proteomics quantification graph (QFeatures) | 6. LC-MS統合コンテナ (MsExperiment) | 7. クロスレイヤーの公開マニフェスト | 8. チュートリアル由来データでの検証 | 8.1 MultiAssayExperiment公式データ (miniACC) | 8.2 Spectra公式データ (fft_spectrum) | 9. 有効性チェック(自動) | まとめ | Session Information
OmicsLake 実践ワークフロー: フェーズ1-4機能の統合活用4 months ago
はじめに | 全コードを実行する前に | プロジェクトのセットアップ | データの準備 | Phase 1: SQLクエリによるデータフィルタリング | Phase 2: 集計関数による統計解析 | 遺伝子ごとの統計量を計算 | 発現量上位の遺伝子を抽出 | ランキングを追加 | 累積統計の計算 | Phase 3: Parquetでの結果共有 | 結果のエクスポート | 外部データのインポート | Phase 4: データベースビューで複数バージョンを比較 | シナリオ: 2つの異なる解析手法を比較 | ビューの作成: 手法間の比較 | ビューの活用: 一致度の確認 | 複数のビューを作成 | ビューの一覧表示 | ビューの削除 | 統合ワークフロー例: 全機能を組み合わせる | 実践的なヒントとベストプラクティス | Phase 1 (SQLクエリ) の活用場面 | Phase 2 (集計関数) の活用場面 | Phase 3 (Parquet) の活用場面 | Phase 4 (ビュー) の活用場面 | 組み合わせのパターン | まとめ | Session Information
OmicsLake 総合ガイド4 months ago
はじめに | 全コードを実行する前に | パッケージの主な機能 | 1. 基本操作 | 1.1 プロジェクトの初期化 | 1.2 テーブルの書き込みと読み込み | 1.3 SQLクエリによる高度な分析 (ol_query) | JOINとサブクエリ | 遅延評価とdplyrとの統合 | 1.4 集計・ウィンドウ関数による高度な分析 | 遺伝子発現統計の計算 | 遺伝子のランキング | 移動平均と累積和 | 遅延評価との組み合わせ | 1.5 Rオブジェクトの保存と読み込み | 2. 依存関係の追跡 | 2.1 依存関係を指定してデータを保存 | 2.2 依存関係の確認 | 2.3 Parquet ファイルのインポート・エクスポート | Parquetエクスポート | Parquetインポート | パフォーマンスのヒント | 3. バージョン管理 | 3.1 タグ付けとラベル付け | 3.2 複数バージョンの作成 | 3.3 バージョン一覧と比較 | 3.4 特定バージョンの読み込み | 4. コミットと履歴管理 | 4.1 コミットの作成 | 4.2 履歴の確認 | 5. 依存関係の可視化 | 5.1 依存関係グラフの作成 | 6. プロジェクトの復元 | 6.1 ラベルを使った状態の復元 | 7. 高度な機能 | 7.1 データのフィルタリング読み込み | 7.2 遅延評価での読み込み | 7.3 テーブルの削除 | 8. Bioconductor統合 | 8.1 SummarizedExperimentの作成 | 8.2 MultiAssayExperimentの作成 | 9. 実践的なワークフロー例 | 9.1 完全な差次発現解析ワークフロー | 10. まとめ | 主要な関数一覧 | プロジェクト管理 | データ保存・読み込み | バージョン管理 | 履歴管理 | 依存関係 | 一覧表示 | Bioconductor | その他 | ベストプラクティス | Session Information
Detecting Homologous Recombination in Family Pedigrees7 months ago
Introduction | Installation | Detecting recombination | Detecting putative recombination intervals | Filtering for double recombination | Visualizing GenomicRanges output from xoDetect() using trackViewer and rtracklayer | Runs of homozygosity | Phasing at Informative SNPs | Session Information
Minigene report12 months ago
Preparation | Selecting the right reference genome | Adding RNA expression | SNPs and small indels | Reading and filtering mutations | Annotating and subsetting expressed variants | Fusion genes from RNA-seq | Reading a fusion VCF | Annotating fusion genes | Generating the Minigene Library | Tiling cDNAs of interest into smaller peptides | Saving the report file
spbtest56 years ago
BiocFileCache: Managing File Resources Across Sessions8 years ago
Overview
Bioc Tes38 years ago
Overview
test8 years ago
spbtest38 years ago