iSEE: interactive and reproducible exploration and visualization of genomics data

# <code>iSEE</code>: interactive and reproducible exploration and visualization of genomics data
### Federico Marini (<a href="mailto:marinif@uni-mainz.de" class="email">marinif@uni-mainz.de</a>) 
### 2019/07/11 useR! 2019 - Toulouse <a href="https://twitter.com/hashtag/useR2019">#useR2019</a>

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I'm **Federico Marini**, Virchow Fellow @CTH Mainz/IMBEI

I like platelets (and their transcriptome), and you should as well.

<a href="mailto:marinif@uni-mainz.de">&nbsp; `marinif@uni-mainz.de`</a> 
<a href="https://federicomarini.github.io">&nbsp; `federicomarini.github.io`</a> 
<a href="http://twitter.com/FedeBioinfo">&nbsp; `@FedeBioinfo`</a> 
<a href="http://github.com/federicomarini">&nbsp; `@federicomarini`</a> 
<a href="http://www.imbei.de">&nbsp; CTH/IMBEI (Mainz, Germany)</a>

You can find this presentation here: [`https://federicomarini.github.io/useR2019/`](https://federicomarini.github.io/useR2019/)

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# Transcriptomics at a glance

- High dimensional snapshot of the transcriptomic activity of all RNA species in a sample

**Data**: genes `\(\times\)` samples (e.g. bulk/single cells)

**Objectives**: 
- Compare abundances to discover differentially expressed genes, gene signatures, etc. - features associated with phenotypic differences
- Identification of cell subpopulations, description of developmental trajectories, study noise in transcriptional regulation

**Challenges**
- Large sets
- Heterogeneous sets
- Sparse sets
- Proper analysis tools _should_ combine interactivity and reproducibility
  - Marini and Binder (2016) - [`10.1186/s12859-019-2879-1`](https://doi.org/10.1186/s12859-019-2879-1)
  - Marini and Binder (2019) - [`10.18547/gcb.2017.vol3.iss1.e39`](https://doi.org/10.18547/gcb.2017.vol3.iss1.e39)
  - Joe Cheng's keynote earlier today 😎

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# Exploration and visualization

Effective and efficient methods are key to deliver...

better **quality assessment**

better **generation of research hypotheses**

better **representation of the results**

better **communication** of findings

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# `SummarizedExperiment`s

If you are into single cell data, check out [`https://osca.bioconductor.org`](https://osca.bioconductor.org)!

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# Does the exploration of your data spark joy?

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# Looking for a silver bullet

Data exploration is crucial:

- No general tool for this, limited to assay types or analysis steps
- No support for reproducibility while keeping it intuitive and usable

Joint work with Aaron Lun, Charlotte Soneson, Kevin Rue-Albrecht initiated at [#EuroBioc2017](https://twitter.com/hashtag/EuroBioC2017)

"We could have an interactive SummarizedExperiment Explorer tool..."

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# Hello `iSEE`

* [`https://f1000research.com/articles/7-741/v1`](https://f1000research.com/articles/7-741/v1), live apps inside
* Available in Bioconductor [`http://bioconductor.org/packages/iSEE/`](http://bioconductor.org/packages/iSEE/)

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# `iSEE(sce)`

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# `iSEE` in action: the *Tabula Muris* dataset

Preprocessing details + `iSEE` configuration for this set can be found at 
[`https://github.com/federicomarini/iSEE_instances`](https://github.com/federicomarini/iSEE_instances/tree/master/iSEE_tabulamuris)

- 20 organs and tissues, from 8 different mice
- 23036 genes
- 43598 cells `\(\rightarrow\)` >1 billion data points!

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# A tour of the panels

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