MOTIVATION: Spatial transcriptomics (ST) enables the study of gene expression within its spatial context in histopathology samples. To date, a limiting factor has been the resolution of sequencing based ST products. The introduction of the Visium High Definition (HD) technology opens the door to cell resolution ST studies. However, challenges remain in the ability to accurately map transcripts to cells and in assigning cell types based on the transcript data. RESULTS: We developed ENACT, a self-contained pipeline that integrates advanced cell segmentation with Visium HD transcriptomics data to infer cell types across whole tissue sections. Our pipeline incorporates novel bin-to-cell assignment methods, enhancing the accuracy of single-cell transcript estimates. Validated on diverse synthetic and real datasets, our approach is both scalable to samples with hundreds of thousands of cells and effective, offering a robust solution for spatially resolved transcriptomics analysis. AVAILABILITY AND IMPLEMENTATION: ENACT source code is available at https://github.com/Sanofi-Public/enact-pipeline. Experimental data are available at https://zenodo.org/records/14748859.

MOTIVATION: Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose. RESULTS: Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale. AVAILABILITY AND IMPLEMENTATION: SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.

The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. Addressing these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.

Time series classication is an important problem that has received a great amount of attention by researchers and practitioners in the past two decades. In this work, we propose a novel algorithm for time series classication based on the discovery of class-specic representative patterns. We dene representative patterns of a class as a set of subsequences that has the greatest discriminative power to distinguish one class of time series from another. Our approach rests upon two techniques with linear complexity: symbolic discretization of time series, which generalizes the structural patterns, and grammatical inference, which automatically nds recurrent correlated patterns of variable length, producing an initial pool of common patterns shared by many instances in a class. From this pool of candidate patterns, our algorithm selects the most representative patterns that capture the class specicities, and that can be used to eectively discriminate between time series classes. Through an exhaustive experimental evaluation we show that our algorithm is competitive in accuracy and speed with the stateof-the-art classication techniques on the UCR time series repository, robust on shifted data, and demonstrates excellent performance on real-world noisy medical time series.

In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approximation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their "importance" to the class, which not only facilitates well-performing classification procedure, but also provides an interpretable class generalization. The accuracy of the method, as shown through experimental evaluation, is at the level of the current state of the art. While being relatively computationally expensive within a learning phase, our method provides fast, precise, and interpretable classification.