Public S3 Buckets | Private S3 Buckets | Session Info

Public S3 Buckets | Private S3 Buckets | Session Info

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

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 | 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

Introduction | Limitations with Rarr | Example data | Quick start guide | Additional details | Working with Zarr metadata | Using credentials to access S3 buckets | Creating an S3 client | 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 | Using credentials to access S3 buckets | Creating an S3 client | Writing subsets of data | Creating an "empty" array | Updating a subset of an existing array | Appendix | 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

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

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

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

Introduction | 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 | 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 | 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 | 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 | Wormbase | Phytozome | Version 12 | Version 13 | Session Info

Introduction | Wormbase | Phytozome | Version 12 | Version 13 | Session Info

Introduction | Selecting an Ensembl BioMart database and dataset | Step1: Identifying the database you need | Step 2: Choosing a dataset | Ensembl mirror sites | Using archived versions of Ensembl | Using Ensembl Genomes | How to build a biomaRt query | Searching for filters and attributes | Using predefined filter values | Finding out more information on filters | filterType | Attribute Pages | Using select() | Result Caching | biomaRt helper functions | exportFASTA | Examples of biomaRt queries | Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes | Annotate a set of EntrezGene identifiers with GO annotation | Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y, and are associated with specific GO terms | Annotate set of idenfiers with INTERPRO protein domain identifiers | Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chromosome 16 between basepair 1100000 and 1250000. | Retrieve all EntrezGene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. | Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences | Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185,514,033 and 185,535,839 | Retrieve protein sequences for a given list of EntrezGene identifiers | Retrieve known SNPs located on the human chromosome 8 between positions 148350 and 148400 | Given the human gene TP53, retrieve the human chromosomal location of this gene and also retrieve the chromosomal location and RefSeq id of its homolog in mouse. | Connection troubleshooting | r BiocStyle::Biocpkg("biomaRt") specific solutions | Global connection settings | Error: "SSL certificate problem" | Error: "sslv3 alert handshake failure" | Session Info

Introduction | Selecting an Ensembl BioMart database and dataset | Step1: Identifying the database you need | Step 2: Choosing a dataset | Ensembl mirror sites | Using archived versions of Ensembl | Using Ensembl Genomes | How to build a biomaRt query | Searching for filters and attributes | Using predefined filter values | Finding out more information on filters | filterType | Attribute Pages | Using select() | Result Caching | biomaRt helper functions | exportFASTA | Examples of biomaRt queries | Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes | Annotate a set of EntrezGene identifiers with GO annotation | Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y, and are associated with specific GO terms | Annotate set of idenfiers with INTERPRO protein domain identifiers | Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chromosome 16 between basepair 1100000 and 1250000. | Retrieve all EntrezGene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. | Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences | Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185,514,033 and 185,535,839 | Retrieve protein sequences for a given list of EntrezGene identifiers | Retrieve known SNPs located on the human chromosome 8 between positions 148350 and 148400 | Given the human gene TP53, retrieve the human chromosomal location of this gene and also retrieve the chromosomal location and RefSeq id of its homolog in mouse. | Connection troubleshooting | r BiocStyle::Biocpkg("biomaRt") specific solutions | Global connection settings | Error: "SSL certificate problem" | Error: "sslv3 alert handshake failure" | Session Info

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

Example data | Using ggplot2

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 | 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