Interactive visualization as a key tool for understanding (medical) omics data

# Interactive visualization as a key tool for understanding (medical) omics data
### Federico Marini (<a href="mailto:marinif@uni-mainz.de" class="email">marinif@uni-mainz.de</a>) 
### 2019/09/09 GMDS 2019 - Dortmund <a href="https://twitter.com/hashtag/gmds2019">#gmds2019</a>

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# `Sys.getenv("USER")`

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/GMDS2019/`](https://federicomarini.github.io/GMDS2019/)

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# Why are we here?

- Because we are dealing with complex stuff, be it Life sciences, Medical/Clinical sciences, you name it

- Because we have data that can't fit in our head - and we have to process, manage, and analyse those large (big?) amounts

- Because we are curious and stubborn enough that we want to try and understand this

- The Pro & the Con: **We are not alone**, and we work with a variety of experts

**Mind the gap**: technical expertise to work with data VS biological expertise to interpret the data!

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# Transcriptomics at a glance

**RNA-seq**: 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 and heterogeneity in transcriptional regulation

**Challenges**
- Large sets
- Heterogeneous sets
- Sparse sets

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# Does the exploration of your data spark joy?

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# The analysis workflow

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# The analysis workflow

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# The analysis workflow

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# The analysis workflow

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# The analysis workflow

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# The analysis workflow

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# The analysis workflow

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# The analysis workflow

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# What gives you joy in exploring and visualizing your data?

### *a.k.a., how do we prevent the last steps from happening?*

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# What's there for us to use?

The data is **large**

- use structures/containers that support alternative (out-of-memory) representations, e.g. HDF5 format - combining `assays`, `rowData`, `colData`

Our task is **complex**

- Proper analysis tools _should_ combine interactivity and reproducibility
  - *Development of Applications for Interactive and Reproducible Research: a Case Study*, Marini and Binder (2016) - [`10.1186/s12859-019-2879-1`](https://doi.org/10.1186/s12859-019-2879-1)
  - *pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components*, Marini and Binder (2019) - [`10.18547/gcb.2017.vol3.iss1.e39`](https://doi.org/10.18547/gcb.2017.vol3.iss1.e39)
  - Soon to appear in your preprint venue: *ideal: an R/Bioconductor package for Interactive Differential Expression Analysis*
  - Joe Cheng's keynote at `useR!2019` in Toulouse (welcome `shinymeta`!)

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# Exploration and visualization: why?

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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# 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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