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Tutorials

Welcome to the scpviz tutorials. These guides walk you through the main steps of a single-cell or spatial proteomics workflow using pAnnData.

How to Use These Tutorials

Each tutorial is designed to be self-contained, with code snippets you can run in a Python environment.

Each tutorial includes:

  • Example code blocks you can copy into a Jupyter notebook.
  • Visual outputs to illustrate the results.
  • Tips and notes to explain recommended practices.

If you’re new to scpviz, start with Importing Data and work through the sequence.

Workflow at a Glance

  graph TB
  A["`Import data  
  (DIA-NN / PD)`"] --> B["`Parse metadata  
  (.obs from filenames)`"]
  B --> C["`Filter proteins/peptides  
  (≥2 unique peptides, sample queries)`"]
  C --> D["`Normalize  
    (global, reference feature)`"]
    D --> E["`Impute missing values  
    (KNN / group-wise)`"]
    E --> F["`Plotting  
    (abundance, PCA/UMAP, clustermap)`"]
    F --> G["`DE analysis  
    (mean vs. pairwise strategies)`"]
    G --> H["`Enrichment (STRING)  
    (GSEA / GO / PPI)`"]
    B --> I["`Export results`"]

%% Optional side paths
B -. "QC summaries" .-> F
C -. "RS matrix checks" .-> F
G -. "ranked/unranked lists" .-> H
D .-> I
F .-> I
G .-> I 
H .-> I

1. Importing Data

  • Load DIA-NN or Proteome Discoverer (PD) reports into pAnnData.
  • Automatically parse sample metadata (.obs) from filenames.
  • Understand the prot, pep, and rs matrices.

2. Filtering and Normalization

  • Filter proteins by peptide support (e.g. ≥2 unique peptides).
  • Apply sample-level filters and advanced queries on .obs or .summary.
  • Normalize intensities by global scale, reference features, or other strategies.

3. Imputation

  • Handle missing values using KNN-based or group-wise strategies.
  • Summarize imputation statistics stored in pdata.stats.

4. Plotting

  • Visualize abundance distributions with violin/box/strip plots.
  • Run PCA/UMAP embeddings with flexible coloring options.
  • Generate heatmaps and clustermaps with class annotations.

5. Differential Expression (DE)

  • Perform DE testing at the protein or peptide level.
  • Compare fold-change strategies (mean-based vs. pairwise median).
  • Export DE results for downstream use.

6. Enrichment and Networks

  • Run GSEA and GO enrichment with STRING.
  • Explore protein–protein interaction networks.
  • Retrieve functional annotations for differentially expressed genes.

For in-depth details and examples, see the examples in the relevant function's docstring in API Reference.