Merge branch 'main' of github.com:saeyslab/polygloty

slides/slides.qmd

@@ -22,26 +22,67 @@ exectute:
     echo: true
 ---
 
+Todo: refer to sc best practices
+Todo: paper Lior Pachter differences R & Python
+
 # Introduction
 
-# File formats
+1. How do you interact with a package in another language?
+2. How do you make you package useable for developers in other languages?
 
-# Calling Python from R and vice versa
-or: in-memory interoperability
+We will be focusing on R & Python
 
-## Overview
-reticulate:
+# How do you interact with a package in another language?
+
+1. File format based interoperability
+2. In-memory interoperability
+
+# How do you make your package useable for developers in other languages?
+
+1. Package-based interoperability
+2. Best practices
+
+## Package-based interoperability
+or: the question of reimplementation.
 
-1. Call Python in R
-2. embed a Python session within your R session
+Consider the pros:
+- Discoverability
+- Can your package be useful in other domains?
+- Very user friendly
 
-basilisk allows managing Python environments within the BioConductor ecosystem
+Consider the cons:
+- Think twice: is it worth it?
+- It's a lot of work
+- How will you keep it up to date?
+- How will you ensure parity?
 
+## Best practices
+1. Work with the standards
+2. Work with matrices, arrays and dataframes
+3. Provide vignettes on interoperability
 
-rpy2:
+# File format based interoperability
 
-1. Call R in Python
-2. run R in a Python process
+# In-memory interoperability
+Calling Python in an R environment and vice versa.
+- No need to write out datasets.
+- Best suited to calling functions
+
+rpy2 and reticulate
+
+## Overview
+
+advantages & disadvantaes
+
+rpy2
+1. overview
+2. usage
+3. pitfalls
+
+reticulate:
+1. overview
+2. usage
+3. pitfalls
 
 ## in-memory interoperability advantages
 - no need to write & read results
@@ -53,25 +94,46 @@ rpy2:
 - ensure that the method accepts this
 - you need to be familiar with using & managing both environments
 - data duplication
+- you need to manage the environments
 
-## accessing R from Python
+## rpy2
+Accessing R from Python
 
-rpy2
+Example: code block
+
+`rpy2.rinterface`, the low-level interface
+`rpy2.robjects`, the high-level interface
+
+Example for calling R functions
+
+Example for conversion of arrays
+
+## rpy2
+Conversion:
+numpy & pandas
+
+Example: code block
+
+sparse matrices: anndata2ri
+
+## rpy2
+
+Jupyter(like) notebooks:
+make use of the Magic command interface
+
+`%load_ext rmagic`
+`%R -i input -o output`
 
-Jupyter notebooks:
-- Use IPython magic interface
-- most useful for matrices & arrays
+example
 
-e.g. `%%R -i input -o output`
+## rpy2
 
-- use anndata2ri: converts anndata objects to SingleCellExperiment
+1. let your method be run with matrices and arrays as input
+2. anndata2ri
+?
 
-## accessing Python from R
-reticulate
-basilisk
+## reticulate
 
-# Package-based interoperability
-or: the question of reimplementation
 
 # Workflows