Merge pull request #62 from lucas-diedrich/tutorials-downstream

Tutorials downstream

.gitignore

@@ -18,6 +18,8 @@ __pycache__/
 # docs
 /docs/generated/
 /docs/_build/
-/docs/tutorials/mock-data/
+/docs/tutorials/mock-data/*
+/docs/tutorials/data/*
+
 
 .coverage

docs/api.md

@@ -1,6 +1,6 @@
 # API
 
-## Reader
+## Read
 
 ### Image
 
@@ -25,6 +25,18 @@
     transform_shapes
 ```
 
+### Proteomics
+
+```{eval-rst}
+.. currentmodule:: dvpio.read.omics
+.. autosummary::
+    :toctree: generated
+
+    available_reader
+    parse_df
+    read_precursor_table
+```
+
 ## Write
 
 ```{eval-rst}

docs/references.bib

@@ -80,6 +80,25 @@ @article{Zeng2022
   abstract     = {Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides (https://github.com/MannLabs/alphapeptdeep). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition (https://github.com/MannLabs/PeptDeep-HLA).},
   issue        = {1},
   langid       = {english},
-  keywords     = {Bioinformatics,Computational platforms and environments,Peptides,Proteomics},
-  file         = {/Users/lucas-diedrich/Zotero/storage/WQGI4UQT/Zeng et al. - 2022 - AlphaPeptDeep a modular deep learning framework t.pdf}
+  keywords     = {Bioinformatics,Computational platforms and environments,Peptides,Proteomics}
+}
+
+@article{SOPA2024,
+  title        = {Sopa: A Technology-Invariant Pipeline for Analyses of Image-Based Spatial Omics},
+  shorttitle   = {Sopa},
+  author       = {Blampey, Quentin and Mulder, Kevin and Gardet, Margaux and Christodoulidis, Stergios and Dutertre, Charles-Antoine and André, Fabrice and Ginhoux, Florent and Cournède, Paul-Henry},
+  year         = {2024},
+  journal      = {Nature Communications},
+  shortjournal = {Nat Commun},
+  volume       = {15},
+  number       = {1},
+  pages        = {4981},
+  publisher    = {Nature Publishing Group},
+  issn         = {2041-1723},
+  doi          = {10.1038/s41467-024-48981-z},
+  url          = {https://www.nature.com/articles/s41467-024-48981-z},
+  urldate      = {2025-01-17},
+  abstract     = {Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa (https://github.com/gustaveroussy/sopa), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.},
+  langid       = {english},
+  keywords     = {Computational models,Data processing,Software}
 }

docs/tutorials.md

@@ -28,3 +28,12 @@ tutorials/004_scdvp.ipynb
 
 tutorials/003_write-dvpio.ipynb
 ```
+
+## DVP-IO x External packages
+
+```{toctree}
+:maxdepth: 1
+
+tutorials/005_scportrait.ipynb
+tutorials/006_sopa.ipynb
+```