# Secondary analysis In this tutorial, you will learn how to run the complementary pipeline `SAW reanalyze` for * [`cluster`](#clustering) Clustering * [`lasso`](#lasso) Extract feature expression matrices of interest regions. * [`diffExp`](#differential-expression-analysis) differential expression analysis * [`multiomics`](#proteome-and-transcriptome-joint-analysis) Proteome & Transcriptome joint analysis * [`midFilter`](#mid-filtering) Performe manually filtering spatial expression matrices by MID range. * [`removeBackground`](#automatic-protein-background-removal) Automatic protein background removal. ## Clustering In bioinformatic downstream analysis, clustering is a critical and fundamental method that creates groups of spatial expression points with similar characteristics. The process helps uncover the underlying structure and patterns within the expression data. Clustering plays a crucial role in bioinformatics research because of its versatility in finding gene expression patterns, investigating cell types, and studying disease subtypes. Choose an appropriate [bin size](https://stereotoolss-organization.gitbook.io/saw-user-manual-v8.1/getting-started/glossary#stomics-terminology) for your datasets. For example, the diameter of a mammalian cell is about 10 µm, based on the physical spacing of a pair of DNBs being 500 nm, so it can be roughly estimated that bin20 is a suitable starting point. [Leiden algorithm](https://stereotoolss-organization.gitbook.io/saw-user-manual-v8.1/algorithms/gene-expression-algorithms#clustering) is called for clustering, `--Leiden-resolution` , default to 1.0, controls the coarseness of the clustering when performing Leiden. Higher values lead to more clusters. Differential expression analysis can be performed through `--marker` based on the clusters categorized by Leiden algorithm. You can perform clustering with a bin GEF and set up the command as below: ```sh saw reanalyze cluster \ --gef=/path/to/input/GEF \ --bin-size=20 \ --Leiden-resolution=1.0 \ --marker \ --output=/path/to/output/clustering ``` Clustering outputs based on bin GEF are listed: ```sh clustering ├── .bin20_1.0.h5ad ##._.h5ad, containing analysis results ├── find_marker_genes.csv ##original output CSV └── bin20_marker_features.csv ##formatted CSV for visualization in StereoMap ``` {% hint style="info" %} If turn `--marker` on to the analysis, you will get results related to differential expression analysis, namely `find_marker_genes.csv` and `_marker_features.csv`. * `find_marker_genes.csv` is the original output file. * `_marker_features.csv`. is [a formatted CSV](https://stereotoolss-organization.gitbook.io/saw-user-manual-v8.1/analysis/outputs/analysis#differential-expression) that records mean MID counts, L2FC, adjusted p-value, and expression ratio of marker features for each cluster. {% endhint %} Or begin with a cellbin GEF: ```sh saw reanalyze cluster \ --cellbin-gef=/path/to/input/cellbin/GEF \ --Leiden-resolution=1.0 \ --marker \ --output=/path/to/output/clustering ``` Clustering outputs based on cellbin GEF are listed: ```sh clustering ├── .cellbin.gef ##a copy of input cellbin GEF but with new clustering information ├── .cellbin_1.0.h5ad ##.cellbin_.h5ad, containing analysis results ├── find_marker_genes.csv ##original output CSV └── cellbin_marker_features.csv ##formatted CSV for visualization in StereoMap ``` ## Lasso The interactive tool in **StereoMap** can manually delineate closed regions of interest. It needs `SAW reanalyze lasso` to extract feature expression matrices of regions, using the GeoJSON from **StereoMap**.

Lasso in StereoMap

Run the pipeline for lasso, and set up the command as below: ```sh saw reanalyze lasso \ --gef=/path/to/input/GEF \ --lasso-geojson=/path/to/lasso/GeoJSON \ --bin-size=1,20,50 \ --output=/path/to/output/lasso ``` {% hint style="info" %} `--bin-size` parameter can accept a list of INTs to generate expression matrices with multiple bin sizes at once. {% endhint %} Lasso outputs based on bin GEF are listed: ```sh lasso ├── │ ├── SN..label.gef ##lasso GEF of bin1 │ └── segmentation │ ├── SN.lasso...gem.gz ##GEM of lasso area of different bin sizes │ ... │ ├── SN.lasso...gem.gz │ └── SN.lasso..mask.tif ##mask image of lasso area └── ├── ... └── ... ``` Or begin with: ```sh saw reanalyze lasso \ --cellbin-gef=/path/to/input/cellbin/GEF \ --lasso-geojson=/path/to/lasso/GeoJSON \ --output=/path/to/output/lasso ``` Lasso outputs based on cellbin GEF are listed: ```sh lasso ├── │ └── SN..label.cellbin.gef ##cellbin GEF of lasso area └── └── ... ``` ## Differential expression analysis `SAW reanalyze` can perform differential expression analysis based on both clustering and lasso areas, using the **diffexp** **GeoJSON** file from **StereoMap**. {% hint style="info" %} Selected clusters and lasso regions are recorded in the diffexp GeoJSON. {% endhint %} Perform the analysis simply: ```bash saw reanalyze diffExp \ --count-data=/path/to/previous/SAW/count/result/folder/id \ --diffexp-geojson=/path/to/StereoMap/diffexp/GeoJSON \ --output=/path/to/output/differential_expression ``` {% hint style="info" %} `--count-data` accepts an output directory of the last `SAW count`, `SAW reanalyze` will detect all files, needed for differential expression analysis. Related information is recorded in the `*.diffexp.geojson`. {% endhint %} Differential expression analysis outputs are listed: ```sh differential_expression ├── ._1.0.h5ad ##H5ad containing analysis results ├── find_marker_genes.csv ##original output CSV └── _marker_features.csv ##formatted CSV for visualization in StereoMap ``` Or: 
differential_expression
├── <SN>.cellbin_1.0.h5ad  ##H5ad for cellbin containing analysis results
├── find_marker_genes.csv  ##original output CSV
└── cellbin_marker_features.csv  ##formatted CSV for visualization in StereoMap
## Proteome & Transcriptome joint analysis `SAW multiomics` can integrate RNA and protein data and compute the latent space by Total Variational Inference. Perform clustering analysis for latent space and do one-vs-all differential expression analysis to find marker genes and proteins. You can perform joint analysis with gene and protein bin GEF and set up the command as below: ```bash saw reanalyze multiomics \ --gef=/path/to/input/gene/GEF,/path/to/input/protein/GEF \ --protein-panel=/path/to/ProteinPanel.list \ --bin-size=50 \ --output=/path/to/output/joint_analysis ``` Or begin with gene and protein cellbin GEF: ```bash saw reanalyze multiomics \ --cellbin-gef=/path/to/input/gene/cellbin/GEF,/path/to/input/protein/cellbin/GEF \ --protein-panel=/path/to/ProteinPanel.list \ --output=/path/to/output/joint_analysis ``` {% hint style="info" %} `--gpu-id ` is available for computing accelaration. Find the corresponding protein panel used in `SAW count`. You can also use `--ref-libraries ` instead of `--protein-panel `. {% endhint %} Joint analysis outputs are listed: ```sh joint_analysis ├── ..differential_expression.csv ##original outoput CSV containing differential expression results └── ..h5mu ##mutimodal data containing clustering results ``` Or: ```sh joint_analysis ├── .cellbin.differential_expression.csv ##original outoput CSV containing differential expression results └── .cellbin.h5mu ##mutimodal data containing clustering results ``` ## MID filtering The interactive tool in **StereoMap** can manually set MID range. ```bash saw reanalyze midFilter \ --gef=/path/to/input/GEF \ --mid-json=/path/to/MID/filtering/JSON \ --output=/path/to/output/mid_filtering ``` MID filtering outputs are listed: ```sh mid_filtering └── .filter.gef ##common GEF filtered by MID range ``` Or: ```sh mid_filtering └── .protein.filter.gef ##protein GEF filtered by MID range ``` ## Automatic protein background removal A method for automatically removing non-specific binding protein signals. Find more algorithm details in [Proteom background removal](https://stereotoolss-organization.gitbook.io/saw-user-manual-v8.1/algorithms/gene-expression-algorithms#proteome-background-removal). ```bash saw reanalyze removeBackground \ --gef=/path/to/output/input/protein/GEF \ --bin-size=50 \ --protein-panel=/path/to/ProteinPanel.list \ --output=/path/to/output/removeBackground ``` {% hint style="info" %} Find the corresponding protein panel used in `SAW count`. You can also use `--ref-libraries ` instead of `--protein-panel `. {% endhint %} `removeBackground` outputs based on protein bin GEF are listed: ```bash removeBackground └── A03684D4.protein.tissue.rmbg.gem.gz ##protein expression matrix after background removal ```