Interface | New species / speciesL argument | New version argument

Overview | Installation and loading | Discover supported attributes | Retrieve gene annotations | Retrieve transcript-level annotations | Use the getGene() compatibility wrapper | Retrieve sequences | Find orthologues | Migrating common r BiocStyle::Biocpkg("biomaRt") code | Roadmap and future direction | Session information

Interface | New species / speciesL argument | New version argument

Overview | Installation and loading | Discover supported attributes | Retrieve gene annotations | Retrieve transcript-level annotations | Use the getGene() compatibility wrapper | Retrieve sequences | Find orthologues | Migrating common r BiocStyle::Biocpkg("biomaRt") code | Roadmap and future direction | Session information

Standard workflow | Quick start | How to get help for DESeq2 | Acknowledgments | Funding | Input data | Why un-normalized counts? | The DESeqDataSet | Transcript abundance files and tximport / tximeta | Tximeta for import with automatic metadata | Count matrix input | htseq-count input | SummarizedExperiment input | Pre-filtering | Note on factor levels | Collapsing technical replicates | About the pasilla dataset | Differential expression analysis | Log fold change shrinkage for visualization and ranking | Speed-up and parallelization thoughts | p-values and adjusted p-values | Independent hypothesis weighting | Exploring and exporting results | MA-plot | Alternative shrinkage estimators | Plot counts | More information on results columns | Rich visualization and reporting of results | Exporting results to CSV files | Multi-factor designs | Data transformations and visualization | Count data transformations | Blind dispersion estimation | Extracting transformed values | Variance stabilizing transformation | Regularized log transformation | Effects of transformations on the variance | Data quality assessment by sample clustering and visualization | Heatmap of the count matrix | Heatmap of the sample-to-sample distances | Principal component plot of the samples | Variations to the standard workflow | Wald test individual steps | Control features for estimating size factors | Contrasts | Interactions | Time-series experiments | Likelihood ratio test | Extended section on shrinkage estimators | Recommendations for single-cell analysis | Approach to count outliers | Dispersion plot and fitting alternatives | Local or mean dispersion fit | Supply a custom dispersion fit | Independent filtering of results | Tests of log2 fold change above or below a threshold | Access to all calculated values | Sample-/gene-dependent normalization factors | "Model matrix not full rank" | Linear combinations | Group-specific condition effects, individuals nested within groups | Levels without samples | Theory behind DESeq2 | The DESeq2 model | Changes compared to DESeq | Methods changes since the 2014 DESeq2 paper | Count outlier detection | Expanded model matrices | Independent filtering and multiple testing | Filtering criteria | Why does it work? | Frequently asked questions | How can I get support for DESeq2? | Why are some p values set to NA? | How can I get unfiltered DESeq2 results? | How do I use VST or rlog data for differential testing? | Why after VST are there still batches in the PCA plot? | Do normalized counts correct for variables in the design? | Can I use DESeq2 to analyze paired samples? | If I have multiple groups, should I run all together or split into pairs of groups? | Can I run DESeq2 to contrast the levels of many groups? | Can I use DESeq2 to analyze a dataset without replicates? | How can I include a continuous covariate in the design formula? | I ran a likelihood ratio test, but results() only gives me one comparison. | What are the exact steps performed by DESeq()? | Is there an official Galaxy tool for DESeq2? | I want to benchmark DESeq2 comparing to other DE tools. | I have trouble installing DESeq2 on Ubuntu/Linux... | Session info | References

Using credentials to access S3 buckets | Creating an S3 client

Using credentials to access S3 buckets | Creating an S3 client

1 Data import and preparations | 2 How many genes are expressed? | 3 Cluster stability analysis | 4 Lineage Markers | 5 Differentially expressed genes from E3.25 to E3.5 | 6 Principal Component Analysis | 7 Further analyses of FGF4-KO | 8 Jensen-Shannon Divergence analysis | 9 Classification of temporal profiles | 10 qPCR data analysis | A Influence of cell position on gene expression | B Correlation between Fgf ligands and Fgf receptors | C Session info | D The data import script readdata.R

Function names, signatures, and return values | FAQ | Why a new package?

Function names, signatures, and return values | FAQ | Why a new package?

Zarr version | Reading and Writing | Stores | Data Types | Codecs | Compression codecs | Other codecs | Optional fields or features

Zarr version | Reading and Writing | Stores | Data Types | Codecs | Compression codecs | Other codecs | Optional fields or features

Introduction | Installation of the HDF5 package | High level R-HDF5 functions | Creating an HDF5 file and group hierarchy | Writing and reading objects | Writing and reading objects with file, group and dataset handles | Writing and reading with subsetting, chunking and compression | Influence of chunk size and compression level | Saving multiple objects to an HDF5 file (h5save) | List the content of an HDF5 file | Dump the content of an HDF5 file | Reading HDF5 files with external software | Removing content from an HDF5 file | 64-bit integers | Low level HDF5 functions | Creating an HDF5 file and a group hierarchy | Writing data to an HDF5 file | Session Info

Introduction | Installation of the HDF5 package | High level R-HDF5 functions | Creating an HDF5 file and group hierarchy | Writing and reading objects | Writing and reading objects with file, group and dataset handles | Writing and reading with subsetting, chunking and compression | Influence of chunk size and compression level | Saving multiple objects to an HDF5 file (h5save) | List the content of an HDF5 file | Dump the content of an HDF5 file | Reading HDF5 files with external software | Removing content from an HDF5 file | 64-bit integers | Low level HDF5 functions | Creating an HDF5 file and a group hierarchy | Writing data to an HDF5 file | Session Info

Introduction | Reading subsets of data | Using the index argument | Using hyperslab selections | Irregular selections | Using hyperslab selection tools | Slowdown when selecting unions of hyperslabs | Summary | Writing in parallel | Example data | Serial writing of datasets | Parallel writing of datasets | Session info

Introduction | Reading subsets of data | Using the index argument | Using hyperslab selections | Irregular selections | Using hyperslab selection tools | Slowdown when selecting unions of hyperslabs | Summary | Writing in parallel | Example data | Serial writing of datasets | Parallel writing of datasets | Session info

Introduction | Limitations with Rarr | Example data | Quick start guide | Additional details | Working with Zarr metadata | Writing subsets of data | Creating an "empty" array | Updating a subset of an existing array | Appendix | Session info

Introduction | Limitations with Rarr | Example data | Quick start guide | Additional details | Working with Zarr metadata | Writing subsets of data | Creating an "empty" array | Updating a subset of an existing array | Appendix | Session info

Introduction | romeo | What is OME-Zarr? | Installation | Reading OME-Zarr files | Images | Labels | Reading from S3 storage | Writing OME-Zarr files | Appendix | Session info

Public S3 Buckets | Private S3 Buckets | Session Info

Public S3 Buckets | Private S3 Buckets | Session Info

Guiding principles | Scope | Zarr version | Functional programming and API design

Guiding principles | Scope | Zarr version | Functional programming and API design

Overview | Preparations | Example data | Executability of the code | Alignment | Preparing the annotation | Counting reads | Building a DEXSeqDataSet | Standard analysis workflow | Loading and inspecting the example data | Normalisation | Dispersion estimation | Testing for differential exon usage | Additional technical or experimental variables | Visualization | Parallelization and large number of samples | Perform a standard differential exon usage analysis in one command | Appendix | Controlling FDR at the gene level | Preprocessing with python | Preparing the annotation file with python | Counting reads with python | Reading the data from the python ouputs into R | Preprocessing using featureCounts | Further accessors | Overlap operations | Methodological changes since publication of the paper | Requirements on GTF files | Session Information | References

Introduction | Install and load the package | ZarrArray objects | Construction | Array and matrix operations | Other operations | Write an array-like object to disk in Zarr format | Session information

Introduction | Basic use | Affymetrix data - before preprocessing | Affymetrix data - after preprocessing | ExpressionSet and ExpressionSetIllumina | Two colour arrays, NChannelSet, RGList, MAList | Loading data from ArrayExpress | Making the report more informative by adding a factor of interest | Extended use | Spatial layout of the array | Mapping of the reporters | RNA quality | Session Info | References

Introduction | Basic use | Affymetrix data - before preprocessing | Affymetrix data - after preprocessing | ExpressionSet and ExpressionSetIllumina | Two colour arrays, NChannelSet, RGList, MAList | Loading data from ArrayExpress | Making the report more informative by adding a factor of interest | Extended use | Spatial layout of the array | Mapping of the reporters | RNA quality | Session Info | References

Introduction | Data preparation | Module generating functions | Outlier detection | Rendering the report | Session Info

Introduction | Data preparation | Module generating functions | Outlier detection | Rendering the report | Session Info

Introduction | Setup and Notation | Likelihood for Incremental Normalization | Profile Likelihood | Summary | References

Introduction | Setup and Notation | Likelihood for Incremental Normalization | Profile Likelihood | Summary | References

Latent Embedding Multivariate Regression (LEMUR) | Installation | A note to users | Overview | A worked through example | Using cell type annotation | FAQ | I have already integrated my data using Harmony / MNN / Seurat. Can I call lemur directly with the aligned data? | Can I call lemur with sctransformed instead of log-transformed data? | My data appears less integrated after calling lemur than before. What is happening?! | The conditions still separate if I plot the data using UMAP / t-SNE, even after calling align_harmony / align_neighbors. What should I do? | How do I make lemur faster? | Session Info

Motivation | Usage | Link to the library | Locating the library headers | Configuration arguments for non-standard system setups | Funding | Session info

Motivation | Usage | Link to the library | Locating the library headers | Configuration arguments for non-standard system setups | Funding | Session info

Getting started | Limitations | Other determinants of variance | Numerical stability and convergence | Running VSN on data from a single two-colour array | Running VSN on data from multiple arrays ("single colour normalisation") | Running VSN on Affymetrix genechip data | Print-tip groups | Normalisation against an existing reference dataset | The calibration parameters | The calibration parameters and the additive-multiplicative error model | More on calibration | Variance stabilisation without calibration | Quality assessment | References

Getting started | Limitations | Other determinants of variance | Numerical stability and convergence | Running VSN on data from a single two-colour array | Running VSN on data from multiple arrays ("single colour normalisation") | Running VSN on Affymetrix genechip data | Print-tip groups | Normalisation against an existing reference dataset | The calibration parameters | The calibration parameters and the additive-multiplicative error model | More on calibration | Variance stabilisation without calibration | Quality assessment | References

Motivation | Usage | With rhdf5 | Writing data | Reading data | With external applications | h5dump example | Compiling the compression libraries | Session info

Motivation | Usage | With rhdf5 | Writing data | Reading data | With external applications | h5dump example | Compiling the compression libraries | Session info

Demo | Session Info

Installation | Introduction | Data input | Pre-processing the drug-protein dataset | Preparation of the drug response matrix | Prepare drug response matrix using z-scores | Assessment of missing values | Subset for cell lines with less than 24 missing values (based on assessment above) | MissForest imputation | Calculate column-wise z-score | Combine the feature and response matrix for the regression model | Multivariate model for protein dependence prediction | Multi-target LASSO model | Examples of how to interpret and perform downstream analyses on the inferred protein dependence matrix | Heatmap of protein dependence coefficients | Differential dependence on proteins associated with cancer types and genotypes | Visualize protein associations with cancer type | Visualize P-values of significant associations between protein dependence and mutational background | Boxplot visualization for the difference of target importance values | Session info

Getting started | Reading, displaying and writing images | Image data representation | Color management | Manipulating images | Spatial transformations | Filtering | Linear filters | Median filter | Morphological operations | Thresholding | Global thresholding | Adaptive thresholding | Image segmentation | Watershed | Voronoi tesselation | Object manipulation | Object removal | Filling holes and regions | Highlighting objects | Cell segmentation example | Session Info

Introduction | The problem | Hilbert curves | Usage | Interactive tool for data exploration | Three-channel Hilbert plots | Reading in GFF and wiggle data | Rle vectors | Installation | Session Info | References

1 Introduction | 2 lpsymphony: Quick Start | 3 Integer Linear Programming

Introduction | An example: RNA-Seq differential expression | FDR control | Diagnostic plots | Estimated weights | Decision boundary | Raw versus adjusted p-values | FWER control with IHW | Other data types, and how to choose the covariate | Criteria for choosing a covariate | Examples | Why are the different covariate criteria necessary? | Diagnostic plots for the covariate | Scatter plots | Stratified p-value histograms | Further reading about appropriate covariates | Advanced usage: Working with incomplete p-value lists | References

Introduction to the example data | Visualizing the expression profiling with the plotAlongChrom function

Introduction | Selection of perfect match and background features | Normalization | Alternative normalization methods | Assessment | Version Information

Introduction | Normalization of the data | Segmentation | Visualizing segmentations with the plotAlongChrom function | Version information