> For the complete documentation index, see [llms.txt](https://help.bioinsight.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.bioinsight.illumina.com/dragen-stratamap/illumina-stratamap-image-tool/analysis-methods.md). # Analysis Methods ## Registration The image registration process aligns tissue images to a standardized coordinate system to ensure alignment of images with spatial data during analysis. This is achieved using a dedicated spatial substrate with fiducial markers embedded in a predefined pattern. When an image is processed, fiducials are detected and used to estimate a global affine transformation. This transformation is applied to the tissue image, aligning it with the standard reference grid. By registering tissue images in this way, we ensure accurate spatial mapping, which enables precise correlation with transcriptomic data. *** ## Image QC To ensure image accuracy and reliability, we employ a set of quality control (QC) functions. These QC checks help detect errors early in the workflow, ensuring that only high-quality images proceed to the next step. ### Fiducial QC Fiducial QC ensures that the fiducial markers are detected accurately for precise image alignment. It consists of the following key checks: * Median registration error (checking for potential misalignment). * The number of detected fiducials (ensuring enough are present for accurate alignment). * Their spatial distribution (ensuring even spread across the image). * Orientation consistency (correcting for image flips or rotations). * Global positioning (identifying the sample position on the substrate). ### Blur QC Blur QC identifies blurry images caused by focus errors or improper microscope operation. The method analyzes image sharpness by detecting edges and calculating a sharpness score. The image is divided into patches, and each patch is analyzed for blur levels. If too many patches fail the sharpness threshold, the image is flagged as blurry. This helps users determine if an image is suitable for further processing or if it needs to be re-imaged. *** ## Tissue Masking Tissue masking is a crucial preprocessing step that differentiates tissue from the background. This is done using a method based on color thresholding. * An automated thresholding algorithm determines the optimal segmentation threshold. * Additional processing, such as dilation and contour filtering, is applied to refine the mask. * Users can manually adjust the mask using an interactive interface for improved accuracy. Segmented tissue masks serve as the foundation for subsequent spatial analysis, ensuring that only relevant tissue areas are considered in downstream workflows. *** By implementing these analysis methods, we ensure accurate image alignment, high-quality imaging, and precise tissue segmentation, which provides a reliable foundation for spatial transcriptomic analysis. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.bioinsight.illumina.com/dragen-stratamap/illumina-stratamap-image-tool/analysis-methods.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.