Overview | Installation | Background | Hidden Markov Models | ChromHMM | This package! | Getting started | Segmentation analysis using segmenter | Inputs | Model learning | Output segmentation Object | Comparing models | Interpreting models parameters | Emissions & transitions | Overlap Enrichemnt | Genomic locations enrichment | Segments | Final remarks

curatedAdipoArray | Overview | Introduction | What is curatedAdipoArray? | What is contained in curatedAdipoArray? | What is curatedAdipoArray for? | Installation | Docker image | Generating curatedAdipoArray | Data collection and acquisition | Data processing and quality assessment | Exploring the data objects | Citing the studies in this subset of the data | Citing curatedAdipoArray | Related Packages | Session Info

Overview | Theory | Example | Cooperative factors example | Competitive factors example | References

Overview | The theory | Python implementation | R implementation | Example | Advantages of the R implementation | References

Overview | Getting started | Detailed Example | Choosing ROIs | Calculating Correlation Statistics | Selecting a multiple regions in an image | Analyzing a collection of images at once | Reproducing the shiny app output | Description of the co-localization statistics | PCC | MOC | Colocalization Benchmark Source | Acknowledgement

Overview | Introduction | What is curatedAdipoRNA? | What is contained in curatedAdipoRNA? | What is curatedAdipoRNA for? | Installation | Docker image | Generating curatedAdipoRNA | Search strategy & data collection | Pre-processing | 1. Downloading data download_fastq | 2. Making a genome index make_index | 3. Dowinloading annotations get_annotation | 4. Aligning reads align_reads | 5. Counting features count_features | Quality assessment fastqc | Processing | Making Summarized experiment object make_object | Exploring the adipo_counts object | Summary of the studies in the dataset | Example of using curatedAdipoRNA | Motivation | Filtering low quality samples | Filtering low count genes | Applying differential expression using DESeq2 | Next! | Citing the studies in this subset of the data | Citing curatedAdipoRNA | Session Info

curatedAdipoChIP | Overview | Introduction | What is curatedAdipoChIP? | What is contained in curatedAdipoChIP? | What is curatedAdipoChIP for? | Installation | Docker image | Generating curatedAdipoChIP | Search starategy & data collection | Pre-processing | 1. Downloading data download_fastq | 2. Making a genome index make_index | 3. Dowinloading annotations get_annotation | 4. Aligning reads align_reads | 5. Calling peaks peak_calling | 6. Making a peakset get_peakset | 7. Counting reads in peaks count_features | 8. Quality assessment fastqc | Processing | Making a summarized experiment object peak_object | Exploring the peak_counts object | Summary of the studies in the dataset | Example of using curatedAdipoChIP | Motivation | Subsetting the object to samples and peaks of interest | Filtering low quality samples | Filtering peaks with low counts | Check sample reproducibility | Applying differential binding using DESeq2 | Next! | Citing the studies in this subset of the data | Citing curatedAdipoChIP | Session Info