Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Multistain segmentation of renal histology: first steps toward artificial intelligence–augmented digital nephropathology

Artificial intelligence (AI), and particularly deep learning (DL), are showing great potential in improving pathology diagnostics in many aspects, 1 of which is the segmentation of histology into (diagnostically) relevant compartments. Although most current studies focus on AI and DL in oncologic pathology, an increasing number of studies explore their application to nephropathology, including the study published in this issue of Kidney International by Jayapandian et al

Tackling stain variability using CycleGAN-based stain augmentation

Background: Considerable inter- and intra-laboratory stain variability exists in pathology, representing a challenge in development and application of deep learning (DL) approaches. Since tackling all sources of stain variability with manual annotation is not feasible, we here investigated and compared unsupervised DL approaches to reduce the consequences of stain variability in kidney pathology. Methods: We aimed to improve the applicability of a pretrained DL segmentation model to 3 external multi-centric cohorts with large stain variability. In contrast to the traditional approach of training generative adversarial networks (GAN) for stain normalization, we here propose to tackle stain variability by data augmentation. We augment the training data of the pretrained model by the stain variability using CycleGANs and then retrain the model on the stain-augmented dataset. We compared the performance of i/ the unmodified pretrained segmentation model with ii/ CycleGAN-based stain normalization, iii/ a feature-preserving modification to ii/ for improved normalization, and iv/ the proposed stain-augmented model. Results: The proposed stain-augmented model showed highest mean segmentation accuracy in all external cohorts and maintained comparable performance on the training cohort. However, the increase in performance was only marginal compared to the pretrained model. CycleGAN-based stain normalization suffered from encoded imperceptible information into the normalizations that confused the pretrained model and thus resulted in slightly worse performance. Conclusions: Our findings suggest that stain variability can be tackled more effectively by augmenting data by it than by following the commonly used approach of normalizing the stain. However, the applicability of this approach providing only a rather slight performance increase has to be weighted against an additional carbon footprint

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

Abstract Background Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis. Results tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command ‘tRigon::run_tRigon()’. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon. Conclusions tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware