class: center, middle, title-slide # <code>GeneTonic</code>: combining components of transcriptome analyses for efficient exploration ### Federico Marini (<a href="mailto:marinif@uni-mainz.de" class="email">marinif@uni-mainz.de</a>)</br> <!-- <i class="fab fa-twitter "></i> `@FedeBioinfo`</br> --> ### 2019/12/09<br><a href="http://bit.ly/genetonic2019"><code>http://bit.ly/genetonic2019</code></a><br> <code><i class="fab fa-twitter "></i> @FedeBioinfo</code> --- layout: true <div class="my-footer"> <span>                                  <!--            -->       <!-- <a href="http://bit.ly/genetonic2019"><code>http://bit.ly/genetonic2019</code></a> --> <a href="https://federicomarini.github.io/EuroBioc2019"><code>https://federicomarini.github.io/EuroBioc2019</code></a> </span> </div> --- class: center <!-- Submitted abstract: --> <!-- GeneTonic: combining components of transcriptome analyses for efficient exploration --> <!-- The interpretation of results from transcriptome experiments can be a complex task, where the essential information is distributed among different tabular and list formats - normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. --> <!-- The identification of relevant functional patterns, as well as their contextualization in the data and results at hand, are not straightforward if these pieces of information are not combined together efficiently. --> <!-- Interactivity can play an essential role in simplifying the way how one accesses and digests RNA-seq data analysis in a more comprehensive way. --> <!-- In this work, I am introducing an application that aims to reduce the barrier to understanding such data better. --> <!-- For example, starting from bird's eye perspective summaries (gene-geneset graphs, enrichment maps), it is easy to generate a number of visualizations, where user actions enable further insight and deliver additional information (e.g., gene info boxes, geneset summary and signature heatmaps). --> <!-- Complex datasets interpretation can be wrapped up into a single call to the GeneTonic package, which also supports built-in RMarkdown reporting, to both conclude an exploration session, or also to generate in batch the output of the available functionality, delivering an essential foundation for computational reproducibility. --> <!-- https://federicomarini.github.io/EuroBioc2019 --> <!-- Format: 8 min or so! --> <!-- Each talk has been allotted 8 minutes and presenters should aim for approximately 6 slides. The following rules will apply in these sessions in order to keep things flowing: --> <!-- There will be a warning at 7 minutes. --> <!-- At this point the next talk will be set up on the computer, allowing the next speaker to get going immediately when their time begins. --> <!-- Flashlight talks will be interrupted (possibly mid-syllable) at the 8 minute mark regardless of how many slides the speaker has left. --> <!-- I'm **Federico Marini**, Virchow Fellow @CTH Mainz/IMBEI --> <!-- I like platelets (and their transcriptome), and you should as well. --> # It takes two to tango -- .huge[Results] -- ### and .huge[Interpretation] <!-- The data is **large** ("can't fit in our head") --> <!-- - use structures/containers that support alternative (out-of-memory) representations --> <!-- Our task is **complex** --> <!-- - yet we are curious and stubborn enough that we want to try and understand this --> --- class: center # It takes two to tango -- .huge[Bioinformatician] -- ### and .huge[Experimental scientist] <!-- The Pro & the Con: **We are not alone**, we work with a variety of experts --> <!-- - mind (and bridge) the gap when **communicating**! --> --- class: center # It takes two to tango -- .huge[Reproducibility] -- ### and .huge[Interactivity] <!-- # Reproducible data science = good data science --> <!-- What do you need for doing that? --> <!-- **Data** - needless to say --> <!-- **Software** - list all required packages/versions, if needed to recreate environments on demand --> <!-- **Analysis steps/parameters**, with all tools and params in each step, documenting order, inputs, outputs --> <!-- # (Interactive) Exploration and visualization: why? --> <!-- Effective and efficient methods are key to deliver... --> <!-- <i class="fas fa-check "></i> better **quality assessment** --> <!-- <i class="fas fa-check "></i> better **generation of research hypotheses** --> <!-- <i class="fas fa-check "></i> better **representation of the results** --> <!-- <i class="fas fa-check "></i> better **communication** of findings --> <!-- IMHO... (might be biased) --> <!-- - Proper analysis tools _should_ combine **interactivity and reproducibility** --> <!-- - Marini and Binder (2016) - [`10.18547/gcb.2017.vol3.iss1.e39`](https://doi.org/10.18547/gcb.2017.vol3.iss1.e39) --> <!-- - Marini and Binder (2019) - [`10.1186/s12859-019-2879-1`](https://doi.org/10.1186/s12859-019-2879-1) --> <!-- - Joe Cheng's keynote at `useR!2019` in Toulouse (welcome `shinymeta`!) --> --- # The analysis workflow <p align="center"> <img src="images/Interaction_nostep.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step0.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step1.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step2.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step3.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step4.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step5.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step6.png" alt=""/> </p> --- # The analysis workflow <p align="center"> <img src="images/Interaction_step7.png" alt=""/> </p> <!-- ### *a.k.a., how do we prevent the last steps from happening?* --> <!-- Is there a better way to support these cycles of exploration, inquiry, and hypothesis generation? --> --- class: center <br> # Previously, on Bioconductor... `pcaExplorer`, `ideal`, `iSEE`, and (hopefully soon!) `GeneTonic` -- .pull-left[ <img src="images/ideal_logo_v2.png" alt="" height = 200/> ] -- .pull-right[ <img src="images/iSEE.png" alt="" height = 200/> ] <!-- Essential role to cut down time in one of the best output of omics data, i.e. new questions and hypotheses --> <!-- but also: analyst and experimental scientist- gap to mind and fill --> --- class: center # The real bottleneck in data analysis -- Probably is **data interpretation** - provided you have efficient methods to process your data, and thanks to Bioconductor, you do! -- .pull-left[ <img src="images/thetruth.jpg" alt="" height = 200/> ] .pull-right[ <br><br><br> ... just not in plain sight ] -- ### Still... You might (should) have a set of standardized objects/containers for your datasets and results (Excel does *not* count) <!-- you need combination of tables, iterations --> --- # A cocktail recipe 4 ingredients <!-- (although Germans say all good things are three) --> -- - `dds` -- - `res_de` -- - `res_enrich` -- - `anno_df` -- ``` BiocManager::install("federicomarini/GeneTonic") ``` <p align="center"> <img src="images/genetonic_hex_concept.png" alt="" height = 200/> </p> <!-- Let the exploration of your data spark joy again --> --- background-image: url("images/gif_gt_welcometour.gif") background-size: contain background-position: 50% 50% class: center, bottom # --- background-image: url("images/gif_gt_ggs.gif") background-size: contain background-position: 50% 50% class: center, bottom # --- # A quick aperitivo <p align="center"> <img src="images/gt_ss_emap.png" alt=""/> </p> --- <p align="center"> <img src="images/gt_ss_enhancetable.png" alt=""/> </p> -- <p align="center"> <img src="images/gt_ss_mds.png" alt=""/> </p> --- <p align="center"> <img src="images/gt_ss_horizon.png" alt=""/> </p> -- <p align="center"> <img src="images/gt_ss_bookmarks.png" alt=""/> </p> <!-- Your typical tasks: find common themes, check how they relate to the expression data, repeat. --> <!-- or better, with their efficient combination --> <!-- bird's eye perspective summaries --> <!-- info boxes --> <!-- summary vizs --> <!-- hairballs becoming info-rich with interactivity --> <!-- live demo! --> <!-- Screenshots of some functionality? --> --- # Shopping for genes and genesets <blockquote class="twitter-tweet"> <p lang="en" dir="ltr">Accidentally duplicated every item in my <a href="https://twitter.com/zotero?ref_src=twsrc%5Etfw">@zotero</a> library.<br><br>A sane person would've written a script to automate the merging process.<br><br>I took the analogue approach. <a href="https://t.co/kAF9c6tUs3">pic.twitter.com/kAF9c6tUs3</a></p>— Dooley Murphy (@DooleyMurphy) <a href="https://twitter.com/DooleyMurphy/status/1191744575980941312?ref_src=twsrc%5Etfw">November 5, 2019</a> </blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> <!-- like this like that. but after exploration, what is there? --> <!-- demo for bookmarking - with keystroke --> <!-- happy hour, works live and offline for batch processing --> --- class: center # Wrapping up [<i class="fab fa-github "></i> `federicomarini/GeneTonic`](https://github.com/federicomarini/GeneTonic) Still a work in progress - I'm happy to discuss features, UI, ... -- You can also bookmark with a keystroke! -- **My treat**: interactivity to reduce (a lot!) the time to generate insights out of the result "atomic objects" -- ... never forgetting it is important to keep a reproducible track of the performed operations. -- <br><br><br>`iSEE` hackathon incoming 🎂🎂 ! Checkout [<i class="fab fa-github "></i> `csoneson/iSEEu`](https://github.com/csoneson/iSEEu) <!-- a la mode (shout out: we are working on a rewriting of the API + stickers are still there!) --> --- class: center, middle ## ... thank you for your attention! You can find me here: <br> <code>marinif@uni-mainz.de - <i class="fab fa-twitter "></i></code> [`@FedeBioinfo`](https://twitter.com/FedeBioinfo)<br> <a href="https://federicomarini.github.io"><i class="fas fa-home "></i> `federicomarini.github.io`</a><br> <a href="http://www.imbei.de"><i class="fas fa-map-marker-alt "></i> CTH/IMBEI (Mainz, Germany)</a><br><br><br> [<i class="fab fa-github "></i> `federicomarini/GeneTonic`](https://github.com/federicomarini/GeneTonic) <br><br>You can find this presentation here: [`https://federicomarini.github.io/EuroBioc2019/`](https://federicomarini.github.io/EuroBioc2019/) [`http://bit.ly/genetonic2019`](http://bit.ly/genetonic2019) --- <!-- empty slide --> --- class: center, middle # Links [`http://bioconductor.org/packages/pcaExplorer/`](http://bioconductor.org/packages/pcaExplorer/) [`http://bioconductor.org/packages/ideal/`](http://bioconductor.org/packages/ideal/) [`http://bioconductor.org/packages/iSEE/`](http://bioconductor.org/packages/iSEE/) [`https://github.com/federicomarini/GeneTonic`](https://github.com/federicomarini/GeneTonic) ### Acknowledgements - Center for Thrombosis and Hemostasis (CTH), Mainz (Virchow Fellowship) - IMBEI - Biostatistics & Bioinformatics division --- # eRum2020 is coming <p align="center"> <img src="images/plug_erum2020.png" alt="" height="500"/> </p>