# Single-Sample Analysis This section provides a comprehensive guide to analyzing individual spatial transcriptomics samples using InSituPy's **`InSituData`** class. The tutorials cover the complete workflow from data preparation through advanced spatial analysis.
## Tutorials The tutorials are organized into two stages: **Setup & Preparation** and **Core Analysis Workflows**. The individual tutorials build on each other, making it necessary to run them sequentially starting with the tutorial "01: Automated Image Registration". ### Setup & Preparation Follow these tutorials to learn how to download demo datasets and register histological images to the spatial omics data. ```{eval-rst} .. card:: 00: Download Demo Datasets :link: 00_InSituPy_demo_datasets :link-type: doc :link-alt: Download demo datasets Download example datasets to follow along with the tutorials. Includes 10x Xenium mouse brain data and other sample datasets. .. card:: 01: Automated Image Registration :link: 01_InSituPy_demo_register_images :link-type: doc :link-alt: Automated image registration Register histological (H&E) or immunofluorescence images to your spatial transcriptomics data using automated alignment. ``` ### Core Analysis Workflows Follow these tutorials sequentially to learn essential analysis steps: ```{eval-rst} .. card:: 02: Quality Control & Preprocessing :link: 02_InSituPy_demo_analyze :link-type: doc :link-alt: QC and preprocessing Perform quality control filtering, normalization, feature selection, dimensionality reduction (PCA, UMAP), and clustering. .. card:: 03: Working with Annotations & Regions :link: 03_InSituPy_demo_annotations :link-type: doc :link-alt: Annotations and regions Import spatial annotations and regions of interest from external tools (QuPath, ImageJ) or create them interactively in napari. .. card:: 04: Cropping & Subsetting Data :link: 04_InSituPy_demo_crop :link-type: doc :link-alt: Crop and subset data Extract regions of interest, subset data by cell type or spatial location, and create focused datasets for detailed analysis. .. card:: 05: Cell Type Annotation :link: 05_InSituPy_cell_type_annotation :link-type: doc :link-alt: Cell type annotation Annotate cell types using marker genes, reference-based methods, or transfer labels from single-cell RNA-seq data. .. card:: 06: Spatial Gene Expression Gradients :link: 06_InSituPy_gene_expression_along_axis :link-type: doc :link-alt: Gene expression along axis Analyze gene expression gradients along anatomical axes or spatial trajectories to identify location-dependent patterns. .. card:: 07: Differential Expression & Enrichment :link: 07_InSituPy_differential_gene_expression :link-type: doc :link-alt: Differential expression analysis Perform differential gene expression analysis between cell types or spatial regions, followed by Gene Ontology enrichment analysis. .. card:: 08: Contamination-Aware DGE :link: 08_contamination_aware_dge :link-type: doc :link-alt: Contamination-aware differential gene expression Perform contamination-aware differential gene expression analysis to identify transcript misassignment artifacts from neighboring cells. .. card:: 09: Quantify IF Signal :link: 09_quantify_IF_signal :link-type: doc :link-alt: Quantify immunofluorescence signal Quantify fluorescence intensity from immunofluorescence images in cells or nuclei. ``` ```{toctree} :hidden: true :maxdepth: 1 00_InSituPy_demo_datasets 01_InSituPy_demo_register_images 02_InSituPy_demo_analyze 03_InSituPy_demo_annotations 04_InSituPy_demo_crop 05_InSituPy_cell_type_annotation 06_InSituPy_gene_expression_along_axis 07_InSituPy_differential_gene_expression 08_contamination_aware_dge 09_quantify_IF_signal ```