Single-cell technologies have significantly advanced our understanding of cellular heterogeneity by allowing the examination of individual cells at high resolution. Traditional single-cell RNA sequencing (scRNA-Seq) methods, which utilise whole cells, capture comprehensive RNA content. In contrast, emerging Multiome technologies, which simultaneously profile multiple omics such as gene expression (GEX) and chromatin accessibility, rely on nuclear RNA, potentially missing key cytoplasmic information. This discrepancy results in substantial technical and biological differences between GEX and scRNA-Seq datasets, making it challenging to integrate the data and perform downstream tasks, such as cell-type classification. To address this challenge, we introduce GENESIS (Gene Expression Normalisation and Enhancement for Single-cell Integrated Sequencing), a novel computational framework designed to transform GEX data from Multiome experiments into enhanced, scRNA-Seq like profiles. Utilising advanced generative models—including Variational Autoencoders, Generative Adversarial Networks, and a tailored VAE_UNet architecture—GENESIS can generate high-quality data by modelling and compensating for the inherent differences between nuclear and cytoplasmic RNA. Our comprehensive evaluations show that GENESIS, particularly through the VAE_UNet model, generates synthetic scRNA-Seq data that closely resembles the resolution and biological accuracy of whole-cell sequencing, thereby improving downstream tasks, especially cell-type classification.

Cell type annotation is a crucial step for analyzing single-cell RNA sequencing data. Among others, single-cell Automatic Labeling of cell POpulations (scALPO) is a computational pipeline developed to automatically assign the cell types to the identified clusters in scRNA-seq data. Different from most of the approaches, scALPO relies only on the information on marker genes from published literature. Specifically, after the definition of the dataset obtained from gene information retrieved from online databases, the Leiden clustering algorithm is executed to partition cells that are finally annotated. Since the Leiden algorithm might struggle to obtain a reliable outcome under certain circumstances, in this work, we include several clustering algorithms in scALPO, and we propose a pseudo-voting consensus approach that combines the outcome of a set of clustering algorithms. The results obtained on three different datasets show that the consensus approach can improve the cell type annotation without selecting a specific clustering algorithm that best suits the data under investigation.

Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.

The increasing number of single-cell transcriptomics and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism, as well as the onset of pathologies. In this context, cell type annotation represents a crucial step for the analysis of single-cell RNA sequencing data, which is usually performed by means of time-consuming and possibly biased manual processes, carried out by expert biologists. Recently, alternative computational tools have been proposed to realize an automatic cell identification either based on supervised or unsupervised Machine Learning approaches. These methods typically exploit gene expression data of curated marker gene databases to associate gene expression profiles of single cells with a cell type. In this paper, we propose a novel fully-automatic computational pipeline, named single-cell Automatic Labeling of cell POpulations (scALPO), which leverages a Long Short-Term Memory Neural Network to assign the cell types. Specifically, scALPO can label the provided clusters by simply relying on marker genes rather than gene expressions. Our results, obtained by considering two different datasets, show that scALPO outperforms the most promising state-of-the-art approaches (i.e., SCSA and scType), achieving a cell type annotation more similar to the manually-created ground truth.

The computational analyses of single-cell data, aimed at elucidating and characterizing the functional roles of known and putative novel cell types, are enabling a thorough understanding of the processes driving cell development and pathology progression. The isolation of specific cell types is a crucial step to perform detailed analyses but requires the identification of succinct marker panels, which include genes that refer to cell surface proteins and clusters of differentiation molecules. This still represents a challenging NP-hard computational problem, which can be tackled through global optimization techniques. In this work, we formulate the marker panel identification problem as a bi-objective optimization problem, where the first objective regards the capability of the marker panels to accurately discriminate different cell types, while the second objective is related to the number of genes to include in the panel. In particular, we compared the performance of two multi-objective optimization algorithms, as well as of Genetic Algorithms (GAs) when considering only the first objective, employing two different representations for the candidate solutions. Our results show that the multi-objective optimization algorithms are better than GAs, considering both the quality and the consistency of the obtained marker panels; moreover, the collected results point out that different representations of the candidate solutions have a relevant impact on the performance of the optimization algorithms.