Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering

A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data

In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells

BACKGROUND

Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.

RESULTS

This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio-temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio-temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate 'temporal' discriminator genes responsible for various cell type differentiations.

CONCLUSIONS

eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio-temporal formation of cellular organizations.

Optimizing the setting of medical interactive rehabilitation assistant platform to improve the performance of the patients: A case study

Tele-rehabilitation is an alternative to the conventional rehabilitation service that helps patients in remote areas to access a service that is practical in terms of logistics and cost, in a controlled environment. It includes the usage of mobile phones or other wireless devices that are applied to rehabilitation exercises. Such applications or software include exercises in the form of virtual games, treatment monitoring based on the rehabilitation progress and data analysis. However, nowadays, physiotherapists use a default profiling setting for patients carrying out rehabilitation, due to lack of information. Medical Interactive Rehabilitation Assistant (MIRA) is a computer-based (virtual reality) rehabilitation platform. The profile setting includes: a level of difficulty, percentage of tolerance and maximum range. To the best of our knowledge, there is a lack of optimization in the parameter values setting of MIRA exergames that could enhance patients' performance. Generally, non-optimal profile setting leads to reduced effectiveness. Therefore, this study aims to develop a method that optimizes the profile setting of each patient according to the estimated (desired) optimal results. The proposed method is developed using unsupervised and supervised machine learning techniques. We use Self-Organizing Map (SOM) to cluster patient records into several distinct clusters. K-fold cross validation is applied to construct the prediction models. Classification And Regression Tree (CART) is utilized to predict the patient's optimal input setting for playing the MIRA games. The combination of these techniques seems to improve the efficiency of the standard (default) way in predicting the optimal settings for exergames. To evaluate the proposed method, we conduct an experiment with data collected from a rehabilitation center. We use three metrics to quantify the quality of the results: R-squared (R²), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of experimental analysis demonstrate that the proposed method is effective in predicting the adequate parameter setting in MIRA platform. The method has potential to be implemented as an intelligent system for MIRA prediction in healthcare. Moreover, the method could be extended to similar platforms for which data is available to train our method on.

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.