From c8289a39de60b995077bf3750ec67977f6f459d1 Mon Sep 17 00:00:00 2001
From: Louise Deconinck <louise.deconinck@gmail.com>
Date: Wed, 4 Sep 2024 14:54:30 +0200
Subject: [PATCH 1/2] Package based interoperability

---
 slides/slides.qmd | 54 +++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 48 insertions(+), 6 deletions(-)

diff --git a/slides/slides.qmd b/slides/slides.qmd
index b4b6405..deb0b75 100644
--- a/slides/slides.qmd
+++ b/slides/slides.qmd
@@ -24,12 +24,49 @@ exectute:
 
 # 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?
+
+We will be focusing on R & Python
+
+# 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.
+
+Consider the pros:
+- Discoverability
+- Can your package be useful in other domains?
+- Very user friendly
+
+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
+
+# File format based interoperability
+
 
 # Calling Python from R and vice versa
 or: in-memory interoperability
 
 ## Overview
+
+
 reticulate:
 
 1. Call Python in R
@@ -37,7 +74,6 @@ reticulate:
 
 basilisk allows managing Python environments within the BioConductor ecosystem
 
-
 rpy2:
 
 1. Call R in Python
@@ -53,16 +89,23 @@ 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
+rpy2: an interface to R running embedded in a Python process
+
+
+
 
 Jupyter notebooks:
 - Use IPython magic interface
 - most useful for matrices & arrays
 
-e.g. `%%R -i input -o output`
+e.g. `%%R -i input -o output` as the first line of the cell
+
+## accessing R from Python
+rpy2
 
 - use anndata2ri: converts anndata objects to SingleCellExperiment
 
@@ -70,8 +113,7 @@ e.g. `%%R -i input -o output`
 reticulate
 basilisk
 
-# Package-based interoperability
-or: the question of reimplementation
+
 
 # Workflows
 

From e395e043f3e38dd766ab659d669bb81530514199 Mon Sep 17 00:00:00 2001
From: Louise Deconinck <louise.deconinck@gmail.com>
Date: Wed, 4 Sep 2024 16:33:13 +0200
Subject: [PATCH 2/2] rpy2 part

---
 slides/slides.qmd | 68 ++++++++++++++++++++++++++++++-----------------
 1 file changed, 44 insertions(+), 24 deletions(-)

diff --git a/slides/slides.qmd b/slides/slides.qmd
index deb0b75..586e46c 100644
--- a/slides/slides.qmd
+++ b/slides/slides.qmd
@@ -22,6 +22,9 @@ exectute:
     echo: true
 ---
 
+Todo: refer to sc best practices
+Todo: paper Lior Pachter differences R & Python
+
 # Introduction
 
 1. How do you interact with a package in another language?
@@ -60,24 +63,26 @@ Consider the cons:
 
 # File format based interoperability
 
+# In-memory interoperability
+Calling Python in an R environment and vice versa.
+- No need to write out datasets.
+- Best suited to calling functions
 
-# Calling Python from R and vice versa
-or: in-memory interoperability
+rpy2 and reticulate
 
 ## Overview
 
+advantages & disadvantaes
 
-reticulate:
-
-1. Call Python in R
-2. embed a Python session within your R session
-
-basilisk allows managing Python environments within the BioConductor ecosystem
-
-rpy2:
+rpy2
+1. overview
+2. usage
+3. pitfalls
 
-1. Call R in Python
-2. run R in a Python process
+reticulate:
+1. overview
+2. usage
+3. pitfalls
 
 ## in-memory interoperability advantages
 - no need to write & read results
@@ -91,28 +96,43 @@ rpy2:
 - data duplication
 - you need to manage the environments
 
-## accessing R from Python
+## rpy2
+Accessing R from Python
 
-rpy2: an interface to R running embedded in a Python process
+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
 
-Jupyter notebooks:
-- Use IPython magic interface
-- most useful for matrices & arrays
+## rpy2
+Conversion:
+numpy & pandas
 
-e.g. `%%R -i input -o output` as the first line of the cell
+Example: code block
 
-## accessing R from Python
-rpy2
+sparse matrices: anndata2ri
+
+## rpy2
+
+Jupyter(like) notebooks:
+make use of the Magic command interface
+
+`%load_ext rmagic`
+`%R -i input -o output`
+
+example
 
-- use anndata2ri: converts anndata objects to SingleCellExperiment
+## rpy2
 
-## accessing Python from R
-reticulate
-basilisk
+1. let your method be run with matrices and arrays as input
+2. anndata2ri
+?
 
+## reticulate
 
 
 # Workflows