Biclustering data analysis: a comprehensive survey.

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.

Clustering and visualization of single-cell RNA-seq data using path metrics.

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

scRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data.

A heavy-tailed model for analyzing miRNA-seq raw read counts.

This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions is proposed as a novel approach to better capture the heterogeneity and extreme values commonly observed in miRNA-seq data. Additionally, the parameters of the discrete stable distribution are proposed as an alternative target for differential expression analysis. An R package for computing and estimating the discrete stable distribution is provided. The proposed model is applied to miRNA-seq raw counts from the Norwegian Women and Cancer Study (NOWAC) and the Cancer Genome Atlas (TCGA) databases. The goodness-of-fit is compared with the popular Poisson and negative binomial distributions, and the discrete stable distributions are found to give a better fit for both datasets. In conclusion, the use of discrete stable distributions is shown to potentially lead to more accurate modeling of the underlying biological processes.

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

Multi-layer Bundling as a New Approach for Determining Multi-scale Correlations Within a High-Dimensional Dataset.

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.

Capacidad para aportar y solicitar apoyo emocional en las relaciones de pareja en relación con los perfiles de apego / Ability to provide and request emotional support in affective rela-tionships, in accordance with attachment profiles

El objetivo de esta investigación consistió en estudiar diferencias de género en la capacidad de aportar (posición de soporte) y solicitar (posición de dependencia) apoyo emocional en las relaciones de pareja en función de los perfiles de apego. Para ello se analizaron estas capacidades en el seno de la pareja, considerando que éstas están integradas en el sistema de cuidados. Se trata de conocer en qué medida estas capacidades dependen de los contenidos de género atribuidos a la variable sexo, o a los perfiles de apego. En este estudio participaron 125 personas, mujeres y varones, comprendidos entre 22 y 65 años que en el momento de la investigación convivían de modo estable con sus parejas. Los resultados obtenidos confirmaron que la tendencia a la evitación interfiere en las capacidades de soporte y dependencia en ambos sexos. Sin embargo, la ansiedad fue modulada por la variable sexo de modo que se encontraron diferencias significativas entre mujeres y varones respecto a las variables estudiadas. El presente estudio aporta evidencias acerca del interés de combinar las variables ansiedad y evitación para obtener grupos que representen categorías de apego. Los análisis derivados de ello matizan considerablemente los resultados de modo que los hombres altamente ansiosos cuidan tanto como las mujeres, siempre y cuando el nivel de evitación sea bajo. Sin embargo en el grupo de mujeres ansiosas, la elevada evitación no inhibe los cuidados. Estos resultados sugieren el interés de profundizar en el estudio de las características psicológicas del perfil denominado "evitativo-miedoso", determinado por las personas que muestran puntuaciones elevadas tanto en evitación como en ansiedad (AU)

The aim of this study was to analyze gender differences in the ability to provide (support position) and request (dependence position) emotional support in affective relationships, in accordance with attachment profiles. These abilities were analyzed, as they are considered to form part of the caregiving system within couple relationships. The aim was to determine the extent to which these abilities depend on gender contents attributed to the sex variable or to attachment profiles. Hundred and twenty five women and men, aged between 22 and 65 participated in this study, who at the time of research had a stable couple relationship. Results obtained confirmed that tendency to avoidance interfered in support and dependency abilities, in both, men and women. Anxiety, however, was modulated by gender, as significant differences were found between women and men in the studied variables. This study provides evidence about the importance of combining anxiety and avoidance variables to obtain groups that represent attachment categories. Analysis with categories clarified notably previous results, as it was found that highly anxious men provided care as much as women, as long as avoidance levels were low. Nevertheless, in the anxious women group, high levels of avoidance did not inhibit caregiving. Results from this study suggest the need of studying more the psychological features of the avoidant-fearful profile, formed by people with high scores in anxiety and avoidance (AU)

Avaliação da expressão de PrPc na interação neurônio-glia, em astrócitos e os mecanismos de secreção de STI1 / Avaliation of PrPc expression in neuron-glia crosstalk, in astrocytes and the mechanisms of STI1 secretion

As funções fisiológicas da proteína prion (PrPc) estão sob ampla investigação e caracterização, especialmente as funções associadas ao desenvolvimento cerebral. Destaca-se que a associação de PrPc com Stress Inducible Protein 1 (STI1), induz neuritogênese e neuroproteção via proteína cinase extracelular reguladora (ERK) e proteína cinase dependente de AMPc (PKA) respectivamente. O presente estudo avaliou como a expressão de PrP cem astrócitos pode modular a interação neurônioglia e o papel de STI1 como um fator autócrino em astrócitos. PrPc modula a interação neurônio-glia, a produção de fatores tróficos solúveis e a organização da laminina secretada na matriz extracelular pelos astrócitos. Desta forma, a expressão de PrP ctanto em astrócitos quanto em neurônios é essencial para a neuritogênese e sobrevivência neuronal. O papel autócrino de STI1 em astrócitos também foi demonstrado. A interação PrPc-STI1 previne a morte celular por ativação da via de PKA, e ativa a diferenciação astrocitária, de uma forma protoplasmática para uma fibrosa pela indução de ERK1/2. De acordo com estes resultados, um menor grau de diferenciação é encontrado em camundongos deficientes para PrPc...

The physiological functions of PrPc are under intense investigation and characterization, particularly those associated with brain development. In neurons, the association of PrPc with its ligand, STI1, induces neuritogenesis and neuroprotection via ERK and PKA signaling pathways, respectively. The present study evaluated whether PrPc expression in astrocytes modulates neuron-glia crosstalk and the autocrine role of STI1 in astrocytes. PrPc modulates neuron-glia interaction, the production and secretion of soluble factors, and the organization of the laminin in the extracellular matrix. PrPc expression in neurons and astrocytes is essential to neuritogenesis and neuronal survival. The autocrine role of STI1 in astrocytes was also demonstrated. The PrPc-STI1 interaction prevents cell death in a PKA-dependent manner, and induces astrocyte differentiation, from a flat to a process-bearing morphology in an ERK1/2 dependent manner. We showed that PrPccnull astrocytes presented a slower rate of astrocyte maturation than wild-type ones, with reduced expression of GFAP and increased vimentin and nestin expression...