Efficient quantification of pattern similarity between spatial genomics and cell morphology Open Access
Yang, Yefeng (Spring 2024)
Abstract
Spatial transcriptomics (ST) is a powerful tool for studying gene expression patterns in spatial context, complemented with tissue histology image. Despite the availability of various methods to integrate the gene expression and tissue histology imaging in ST, the lack of a gold-standard metric to accurately quantify the similarity between features across different modalities has been a limitation for data integration. There is a strong demanding to measure the pattern similarity across features from different modalities, which provides insights into the association between molecular profile and cell morphology and enhances our understanding of tissue organization and function. To address this, we introduce DCOGC, a spatial pattern similarity measurement using a combination of correlation and mean squared error of orthogonal gradient curve that effectively captures the pattern similarity of features from different modality in the spatial context. Our method starts from identifying the common regions indicated by different modalities. Next, within each domain, it characterizes the pattern of each feature using gradient curves in two orthogonal directions. Then, we calculate the correlation and mean squared error of the different curves for each direction in every
domain, summing them up to quantify the dissimilarity between two comparing features. The maximum value observed across all regions serves as an indicator of the highest level of feature disparity. DCOGC has been evaluated on multiple datasets, and compared with other popular spatial pattern measurements, i.e., SSIM, spatial correlation. The results demonstrate that our method can better identify image features that exhibit concordance with gene expression as well as those that show dissimilarity than all existing metrics. DCOGC can be further used to filter out image features dissimilarity to all gene expression patterns, which captured artificial effect such as blurriness resulting from loss of camera focus. DCOGC serves as a bridge between gene expression patterns and tissue histology imaging in ST, providing a robust metric to assess the similarity of features across diverse modalities
Table of Contents
optional
About this Master's Thesis
| School | |
|---|---|
| Department | |
| Subfield / Discipline | |
| Degree | |
| Submission | |
| Language |
|
| Research Field | |
| Keyword | |
| Committee Chair / Thesis Advisor | |
| Committee Members |
Primary PDF
| Thumbnail | Title | Date Uploaded | Actions |
|---|---|---|---|
|
|
Efficient quantification of pattern similarity between spatial genomics and cell morphology () | 2024-04-02 16:38:41 -0400 |
|
Supplemental Files
| Thumbnail | Title | Date Uploaded | Actions |
|---|