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# Bioconductor
### Mikhail Dozmorov
### Virginia Commonwealth University
### 03-01-2021

---

## Summarized Experiments

`SummarizedExperiment` - Rows are indexed by a dataframe of _features_. Accessible with `rowData()`

.center[ <img src="img/SummarizedExperiment1.png" height = 350> ]

.small[ https://bioconductor.org/packages/SummarizedExperiment/ ]

---
## RangedSummarizedExperiment

- `RangedSummarizedExperiment` - Rows are indexed by _genomic ranges_. Accessble with `rowRanges()`

---
## SingleCellExperiment

`SingleCellExperiment` - an extension of `RangedSummarizedExperiment` with several internal slots

- Has a slot for spike-in measures

- Can store reduced dimensionality representation of the data

.small[ https://bioconductor.org/packages/SingleCellExperiment/ ]

---
## MultiAssayExperiment

Bioconductor package for management of multi-assay data. Especially useful for integrating TCGA datasets.

.center[ <img src="img/multiassayexperiment.png" height = 350> ]

.small[ https://bioconductor.org/packages/MultiAssayExperiment/ ]

---
## MultiAssayExperiment

.center[ <img src="img/multiassayexperiment1.png" height = 400> ]

.small[ Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez Cabrera, Tiffany Chan, et al. “[Software For The Integration Of Multi-Omics Experiments In Bioconductor](https://doi.org/10.1158/0008-5472.CAN-17-0344),” Cancer Research, October 31, 2017 ]

<!--
## Domain-specific packages

- Important packages for analysis of **differential expression** include `edgeR` and `DESeq2`; both have excellent vignettes for exploration.

.small[ http://bioconductor.org/packages/edgeR

http://bioconductor.org/packages/DESeq2 ]

- Popular **ChIP-seq** packages include `DiffBind` and `csaw` for comparison of peaks across samples, `ChIPQC` for quality assessment, and `ChIPpeakAnno` and `ChIPseeker` for annotating results (e.g., discovering nearby genes).

.small[  http://bioconductor.org/packages/ChIPpeakAnno

http://bioconductor.org/packages/ChIPseeker

http://bioconductor.org/packages/DiffBind

http://bioconductor.org/packages/csaw

http://bioconductor.org/packages/ChIPQC ]

## Domain-specific packages

- Working with called variants (VCF files) is facilitated by packages such as `VariantAnnotation`, `VariantFiltering` and `ensemblVEP`

- Packages for calling variants include, e.g., `h5vc` and `VariantTools`.

.small[ https://bioconductor.org/packages/release/bioc/html/VariantAnnotation.html

http://bioconductor.org/packages/VariantFiltering.html

https://bioconductor.org/packages/release/bioc/html/ensemblVEP.html

https://bioconductor.org/packages/release/bioc/html/h5vc.html

https://bioconductor.org/packages/release/bioc/html/VariantTools.html ]

## Domain-specific packages

- **Single-cell 'omics'** are increasingly important. From the `biocView` page, enter 'single cell' in the 'search table' field.
- Several packages identify **copy number variants** from sequence data, including `cn.mops`. The `CNTools` package provides some useful facilities for comparison of segments across samples.
- **Microbiome and metagenomic** analysis is facilitated by packages such as `phyloseq` and `metagenomeSeq`.
- **Metabolomics, chemoinformatics, image analysis**, and many other high-throughput analysis domains are also represented in _Bioconductor_; explore these via `biocViews` and title searches.

.small[ https://bioconductor.org/packages/release/BiocViews.html#___Software

https://bioconductor.org/packages/release/bioc/html/cn.mops.html

http://bioconductor.org/packages/CNTools

http://bioconductor.org/packages/phyloseq

http://bioconductor.org/packages/metagenomeSeq ]

## Visiualization
  
A number of _Bioconductor_ packages help with visualization and reporting, in addition to functions provided by individual packages.

- `Gviz` provides a track-like visualization of genomic regions.
- `ComplexHeatmap` does an amazing job of all sorts of heatmaps, including OncoPrint-style summaries.
- `ReportingTools` provides a flexible way to generate static and dynamic HTML-based reports.

.small[ http://bioconductor.org/packages/Gviz

http://bioconductor.org/packages/ComplexHeatmap

http://bioconductor.org/packages/ReportingTools ]

## Working with big data

- Much Bioinformatic data is very large and often cannot fit into memory
- Several general strategies for working with large data

**Restriction to specific genomic regions**

- e.g., `ScanBamParam()` limits input to desired data at specific genomic ranges

**Iteration over pieces of genomic data**

- e.g., `yieldSize` argument of `BamFile()`, or `FastqStreamer()` allows iteration through large files.

## Working with big data

**Compression**

- Genomic vectors represented as `Rle` (run-length encoding) class
- Lists e.g., `GRangesList` are efficiently maintain the illusion that vector elements are grouped.

**Parallel processing**

- e.g., via `BiocParallel` package

.small[ https://bioconductor.org/packages/BiocParallel/

Lawrence, M and Morgan, M. [Scalable Genomic Computing and Visualization with _R_ and _Bioconductor_]((http://arxiv.org/abs/1409.2864)). Statistical Science (2014) ]

## Code optimization

- `aprof` - Amdahl's Profiler, Directed Optimization Made Easy

- `profvis` - Visualize R profiling data

- `microbenchmark` - Accurate Timing Functions

.small[ http://cran.r-project.org/web/packages/aprof/index.html

https://rstudio.github.io/profvis. Examples: https://rpubs.com/wch/123888

http://cran.r-project.org/web/packages/microbenchmark/index.html ]
-->

---
## Summary

- _Bioconductor_ is a large collection of R packages for the analysis and comprehension of high-throughput genomic data

- _Bioconductor_ relies on formal classes to represent genomic data, so it is important
to develop a rudimentary comfort with classes, including seeking help
for classes and methods

- _Bioconductor_ uses vignettes to augment traditional help pages; these can be very valuable in illustrating overall package use

Notes for current slide

Notes for next slide

BioconductorMikhail DozmorovVirginia Commonwealth University03-01-20211 / 7

Summarized Experiments

SummarizedExperiment - Rows are indexed by a dataframe of features. Accessible with rowData()

https://bioconductor.org/packages/SummarizedExperiment/

2 / 7

RangedSummarizedExperimentRangedSummarizedExperiment - Rows are indexed by genomic ranges. Accessble with rowRanges()
3 / 7

SingleCellExperiment

SingleCellExperiment - an extension of RangedSummarizedExperiment with several internal slots

Has a slot for spike-in measures
Can store reduced dimensionality representation of the data

https://bioconductor.org/packages/SingleCellExperiment/

4 / 7

MultiAssayExperiment

Bioconductor package for management of multi-assay data. Especially useful for integrating TCGA datasets.

https://bioconductor.org/packages/MultiAssayExperiment/

5 / 7

MultiAssayExperiment

Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez Cabrera, Tiffany Chan, et al. “Software For The Integration Of Multi-Omics Experiments In Bioconductor,” Cancer Research, October 31, 2017

6 / 7

Summary

Bioconductor is a large collection of R packages for the analysis and comprehension of high-throughput genomic data
Bioconductor relies on formal classes to represent genomic data, so it is important to develop a rudimentary comfort with classes, including seeking help for classes and methods
Bioconductor uses vignettes to augment traditional help pages; these can be very valuable in illustrating overall package use

7 / 7

Summarized Experiments

SummarizedExperiment - Rows are indexed by a dataframe of features. Accessible with rowData()

https://bioconductor.org/packages/SummarizedExperiment/

2 / 7

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