09-11, 15:45–16:45 (Europe/Berlin), Workshop room I - MW 0337
A small workshop on existing projection methods for high-dimensional data such as scRNA-seq, interactive plots in python and how to create a documentation of your research that can be hosted online (including the interactive plots).
Associated to this there will be a google colab notebook, github repository and webpage for the participants as active exercise and documentation for future use.
PCA, tSNE and UMAP are probably the most known visualization method for scRNA data. But there is a multitude of techniques out there. I will introduce the state-of-the-art and describe briefly what are their differences - participants' experiences and impressions are welcome.
Afterwards you will work through an analysis example to see how you can create easily interactive plots in python in a jupyter notebook - there will be a CoLab notebook for participants to use actively.
The tutorial will close with a guide and example on how the analysis notebooks can be made available as web documentation including the interactive plots - this guide will be available on github and a web documentation page for future use to each participant.
Optional/Reading: The PacMap paper is a good technical intro to dimensionality reduction https://www.jmlr.org/papers/volume22/20-1061/20-1061.pdf
No previous knowledge expected
I have a solid background in applied mathematics and statistics, where I have extensively developed my programming skills with Python, R/Rcpp, Matlab, Docker, bash and pipelines scripting. I have crossed my competences with the bioinformatics field by developing efficient software and pipelines for Next Generation Sequencing and single cell RNA sequencing Data. I like to challenge myself trying new programming languages, as well as new and old machine learning techniques to solve mathematical and computational problems.