Reflections on teaching math to biologists
Table of Contents
The Cold Spring Harbor Laboratory (CSHL)
Single Cell Analysis Course
(not to be confused with the “Single Cell Analyses” Meeting) for 2017 just
finished a few weeks ago. I was a co-Instructor and was in charge of the
bioinformatics curriculum. In some ways, teaching at CSHL is a perfect teaching
setting because there are no distractions - the students are there exactly to
just work on the course. However, many of the students had minimal programming
and command line exposure so while they can completely focus on learning, they
also have a lot to catch up on.
This post goes over some lessons learned from this experience. If you want all
the teaching materials, I made a
github repo containing all of
the notebooks we used in the course.
Expectation vs Reality
As always, what you planned isn’t what actually happened.
What I planned to do in the course
To help prepare students for any possible single cell question they run into, I
wanted to expose them to several papers’ different methods of analysis. I
wanted paoers covering four common questions in single cells
- Dissociate a tissue –> ??? –> Celltypes!
- Treatment vs control, in a singlecell context
- Molecular transformations over time (“pseudotime”)
- Perturbations + single-cell RNA-seq
Therefore in an ideal setting, we would have done:
- Students read 4 papers ahead of the course
- We recreate one figure from each of the papers
- We’d cover all possible questions that could be answered using single-cell RNA-seq
What actually happened
- Students read 4 papers
- We recreated one figure from one paper
- We spent a lot of time talking about Python basics, much more than I had
anticipated
- We didn’t get to answering every possible single-cell question
- We spent far less time on the “interesting” part of bioinformatics -
interpretation of results
I often let “perfect be the enemy of good” and would not fully reuse a lesson
from last year because it was from last year’s papers, even if the concepts or
programming commands were the same.
The way I designed the notebooks was there were two notebooks for each concept:
- Explain a mathematical concept using images, animations, and
interactive Jupyter widgets.
- Assessment: Class discussion, calling on
random_student() to explain
the question
- Show how the plot from the previous figure was created with complete
step-by-step breakdowns of each function and parameter
- Assessment: the students have to apply the function in a slightly new way
or read documentation and add a new parameter
Bugs I ran into
- Jake VDP’s Scikit-learn
tutorial, while excellent, has not been updated to the most recent version of
Jupyter/IPython widgets. Specifically, the syntax for specifying
Lessons learned: What I would do differently next time
- Start with simple dataframe manipulation such as with a metadata file
- show plotting and groupby
- Focus on one paper, and one analysis
- Take an existing paper and recreate its figures, but also show how to do
the analysis the “right” way, if it differs from what they did in the paper
- Start from the end - what commands and concepts do they need to know by the
end of the analysis? How can we build towards that?
- Using a single dataset and analyzing it all the way through would help the
students get deeper into a single problem, rather than exposing them to a lot
of random stuff
- Don’t expect that people have any experience when we start. Start out the
course with an overview of Python and programming
One of the hardest things about designing a curriculum like this is finding a
good dataset to showcase all of these analyses on. It’s hard to find a single
paper that covers all possible questions, but maybe that’s alright. I always
have to remind myself that “good enough” but shipped is still better than
perfect and unrelease.
The good thing is that these mistakes made me write out my
learning goals
for the different sections of the course. I remember now from Software
Carpentry teacher training (
Lessons and Objectives)
that I should have done this in the beginning, but hey, you live and you learn.