--- title: "Tutorials" --- ```{r setup, include=FALSE} # use rmarkdown::render_site(envir = knitr::knit_global()) knitr::opts_chunk$set(echo = TRUE) ```

## Spatially Aware Data Integration and Analysis **VoltRon** incorporates multiple **data integration utilities to achieve data transfer** across a diverse set of spatial data modalities and types. VoltRon utilizes OpenCV to align and synchronize spatial omic datasets using **computer vision and image registration**. Users can automaticaly or manually align a list of microscopy images (**H&E**, **DAPI** etc) using a **Shiny App** incorporated within our analysis workflow. Once aligned, feature data and metadata level information can be transfered across aligned tissue sections. In addition, VoltRon provides a number of spatially aware data analysis methods to detecting niches (i.e. **Niche Clustering**) within tissues. VoltRon allows estimating niches associated with each cell by incorporating the cell type level information around each cell or spot. We either detect cellular populations/compositions within a spatial neighborhood of a cell to create these niche level information (e.g. Xenium) or we estimate the cell type abundances of spots (e.g. Visium, DBIT-Seq) from a reference single cell data (Seurat, SingleCellExperiment etc.) with already annotated cell types. VoltRon can also use these spatial neighborhood to detect hot spots (i.e. **Hot Spot Analysis**) of features, cell types and even molecular densities.

Spatial Data Alignment

Automated and manual alignment of spatial data assays

Multi-omic Integration

Integrating data modalities within or across tissue sections

Niche Clustering

Clustering based on ROI/spot deconvolution and Spatial Neighborhood



## Additional Downstream Analysis **VoltRon** is also capable of end-to-end analysis of diverse set of spatial data types (or spatial entities)such as **ROIs** (regions of interest), **spots**, **single cells**, **molecules** and even **images**. Users can set any data type as default at any time where VoltRon provides minimal set of functions to analyze, process and visualize each of these modalities.

Region of Interests (ROIs)

Quality control, analysis and visualization of ROI segments

Cells/Spots

Quality control, analysis and visualization of Cell/Spot datasets

Molecules


Analysis and visualization of Molecule datasets
(Under Development)

Pixels (Image Only)

Analysis and visualization of Image datasets
(Under Development)



## Other Utilities Here, we provide a group of tutorials to use additional features of the VoltRon objects as well as further information on how to use VoltRon. We describe a collection of features that VoltRon package utilizes such as **interactive annotation/visualization** and importing spatially aware data from **diverse spatial omic technologies**. VoltRon is able to convert its objects to a diverse set of objects/datatypes commonly incorporated spatial data analysis (**Seurat**, **SpatialExperiment**, **Giotto**, **AnnData** etc.). Large VoltRon objects can be **saved ondisk** and efficiently analyzed without straining memory.

Interactive Utilities


Interactive annotation and visualization

Working with VoltRon Objects




Manipulating and configuring VoltRon objects

Importing Spatial Data

Importing readouts of spatial technologies

Converting VoltRon Objects


Converting VoltRon objects into Seurat, SpatialExperiment and Squidpy (anndata) etc.

OnDisk Analysis Utilities

Efficient access to large VoltRon objects