Teaching and Mentoring Interests

Current Courses I teach at OHSU and PSU

I teach in the summer hybrid BMI569/669 Data Analytics course at OHSU (Hybrid course co-taught with Kaiser Permanente Data & Information Management Enhancement (DIME) group).

I am also a co-instructor for BMI551/651 Bioinformatics and Computational Biology II: Statistical Methods, providing drop-in sessions for R programming and general tutoring.

We are teaching an introductory bootcamp in Python called NEU640 Python Bootcamp for Neuroscientists.. This is a week-long course meant to introduce neuroscience students to the basics of python with data types familiar to them, including imaging and electrophysiology data. Taught with Stephen David, Brad Buran, Daniela Sadieri, Lucille Moore, Charlie Heller, Zack Schwartz. Winter Quarter, 2018.

I am also course co-director for HSMP/PHE410 Introduction to Health Informatics. at Portland State University. Spring Quarter, 2018.

DataCamp

I have a free online introductory course at DataCamp called the RBootcamp. In this course, we cover the basics of visualization, data munging, and basic statistics using the tidyverse in R. It is freely available to everyone, but registration is required.

I am currently developing a course called Statistical Analysis of Networks in R for DataCamp. More info as it develops.

Training Materials/Workshops

I have developed a number of self-directed training materials for learning R and specific packages that I’ve found useful. Most of these are available from my github page and may be used freely by students.

  • R-Bootcamp (tidyverse) - The new version of RBootcamp, that covers the tidyverse for manipulating data in R. You will need to register on DataCamp. This interactive course was written with both Jessica Minnier and Chester Ismay. DOI
  • R-Bootcamp (Base R) - an MOOC (Massively Open Online Course) for teaching the basics of data manipulation in R (Coursesites registration required). Note that I do not support this course anymore at this point.

Standalone Tutorials

Many of these tutorials have been given through the student club at OHSU that I mentor, called BioData Club. They are all freely available to be reused by other instructors.

  • The Magic of Markdown - An introduction to the many uses of Markdown, including RMarkdown for reproducible scripting.
  • GitHub Pages - A pain free introduction to getting a personal website started using GitHub pages. With Robin Champieux and Eric Leung.

R Tutorials

As a developer and programmer of R, I enjoy teaching others about the basics of data wrangling and analysis. Here are a few of the tutorials I’ve put together.

Big Data to Knowledge

As an instructor under OHSU’s Big Data To Knowledge (BD2K) Training Grant, I have also developed the following short workshops to encourage students to explore data and learn the basics of data-wrangling.

Other Teaching Interests

  • BioData Club - A student and postdoc driven discussion group at OHSU that focuses on practical skills (documentation, software engineering and visualization) necessary for success in data science and bioinformatics.
  • I participate in the PDX R User Group and I was one of the organizers of the first Cascadia R conference
  • I have also started to contribute lesson material to Software Carpentry.

Recent Posts

More Posts

Note: sometimes I write these posts to teach myself a better way to do things in R. I have been avoiding tidyeval somewhat, because I seem to have a bit of a learning block about it. I’m going to try to write some posts that help me understand what’s going on with Tidy Evaluation. Using sym() in a Shiny App One fairly simple Shiny Application might be selecting a column of the dataset and then doing something with it, such as using it in a select() or filter() statement.

CONTINUE READING

I’m still in the process of recovering from my current bout of depression and anxiety. I’d like to talk about what is currently helping me moderate my anxiety. I have been practicing mindfulness and meditation for the past three years and I’m beginning to realize how necessary it is in our information dense age. Many of my symptoms of anxiety are really from an information overglut. I’m currently on way too many projects and am teaching as well.

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I decided to write this post in celebration of my newest album, [Tiny Fugues, being released](). I consider myself a scientist and I am a musician in almost equal parts. There has been a lot of talking about the role of creativity in science. Honestly, I rarely try to cross the streams of science and music, mostly because it really hasn’t been encouraged in my career. I do think that I do use the same problem solving skills that I do in science that I also use in composition.

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I want to thank everyone who has reached out to me after I wrote my post on struggling with my depression and self-care. I am incredibly grateful for everyone’s concern about me. I wrote that at a low point in my life because I had to. I was suffering too long in silence, and I needed to do something. Writing that post was incredibly scary. I am still worried that it may be used against me somehow down the line when I am reviewed for tenure.

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Motivation A few weeks ago, as part of the [rOpenSci Unconference](), a group of us (Sean Hughes, Malisa Smith, Angela Li, Ju Kim and me) decided to work on making the UMAP algorithm accessible within R. UMAP (Uniform Manifold Approximation and Projection) is a [dimensionality reduction technique]() that allows the user to reduce high dimensional data (multiple columns) into a smaller number of columns for visualization purposes (Usually two). It is related to both Priniciple Components Analysis (PCA) and t-SNE, which are techniques often used in the single-cell omics world to visualize high dimensional data.

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Well, Cascadia-R 2018 has come and gone. This year we tried our best to make it as inclusive, welcoming, and friendly as we could. Considering we had 224 participants this time around, I’d say it was a success. I just thought I would do a little write up of some of the things we did and why we did them in our conference. I’m hoping it will be useful for other conference planners to create a welcoming environment.

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Note: I am not writing the following to complain, or excuse any past behavior. I am writing this just to be honest and transparent about my current struggles in academia. I hope it helps someone, or encourages other to seek help. I have to confess that I haven’t really been feeling all that well the past few months. Right now I am plagued with feelings that I am doing my work as an Assistant Professor wrong.

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Even though I’ve been using the tidyverse for a couple of years, there’s always a couple new applications of tidyverse verbs. This one, in retrospect, is pretty simple. I had a one to many table that I wanted to collapse, tidy-style. Let’s look at the diamonds dataset: diamonds %>% select(color, cut) ## # A tibble: 53,940 x 2 ## color cut ## <ord> <ord> ## 1 E Ideal ## 2 E Premium ## 3 E Good ## 4 I Premium ## 5 J Good ## 6 J Very Good ## 7 I Very Good ## 8 H Very Good ## 9 E Fair ## 10 H Very Good ## # .

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In academia, it’s inevitable to have to travel and present at conferences and meetings. As an introvert, I’ve been trying to compile a few tips that have helped me navigate large conferences so I don’t feel overwhelmed. It is unfortunate that though the academic community has many introverts, conference and meeting structure is heavily biased towards extroverts. (No offense to extroverts, but some of you can sometimes seem like blowhards to us introverts.

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Sorry for the lack of posts. I have been busy with co-teaching our Health Informatics course (HSMP410) for the OHSU/PSU School of Public Health. I’m trying to make most of my lectures activity-driven for my students, who are Community Health Education and Nursing majors. I believe that you can teach mathematical concepts visually, so I am experimenting with using LearnR/Shiny to teach the basics of data literacy. I’m also using datacamp-light to show my students a simple intro to data science.

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Software

  • flowDashboard. Shiny modules for building interactive and comparative dashboards for flow cytometry data. See a demo here: https://tladeras.shinyapps.io/sampleFlowDashboard/ DOI
  • DSIExplore. Interactive learnr package for teaching beginning exploratory data analysis and statistics. Jessica Minnier and Ted Laderas. Role: author.
  • infer. Tidy statistical inference package. Andrew Bray, Chester Ismay, Ben Baumer, Mine Cetinkaya-Rundel, Ted Laderas and Nick Solomon. Role: Contributor.
  • surrogateMutation - a package for detecting oncogenic collaboration in somatic mutation and copy number data. DOI
  • surrogateShiny - an R/Shiny Framework for exploring oncogenic collaboration in breast cancer cell lines. See a demo here: https://tladeras.shinyapps.io/surrogateShiny/ DOI
  • Consense - a package for comparing clustering methods. DOI
  • ExonModelStrain - a package for detecting alternative exon usage in the Affymetrix Exon Array. DOI
  • TandTRAQ - Perl Script for merging iTRAQ and XTandem Results.

Recent & Upcoming Talks

Selected Publications

Contact

Other Interests

I have a parallel career as a composer and performing musician. I started taking cello lessons when I turned 30. I’m a lifelong learner and I love to play with and accompany other musicians. For more information, please see my artist webpage.