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The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.
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Esquizofrenia , Humanos , Esquizofrenia/fisiopatologia , Transtornos Psicóticos/fisiopatologia , Psicologia do EsquizofrênicoRESUMO
BACKGROUND: Tuber bruising in tetraploid potatoes (Solanum tuberosum) is a trait of economic importance, as it affects tubers' fitness for sale. Understanding the genetic components affecting tuber bruising is a key step in developing potato lines with increased resistance to bruising. As the tetraploid setting renders genetic analyses more complex, there is still much to learn about this complex phenotype. Here, we used capture sequencing data on a panel of half-sibling populations from a breeding programme to perform a genome-wide association analysis (GWAS) for tuber bruising. In addition, we collected transcriptomic data to enrich the GWAS results. However, there is currently no satisfactory method to represent both GWAS and transcriptomics analysis results in a single visualisation and to compare them with existing knowledge about the biological system under study. RESULTS: When investigating population structure, we found that the STRUCTURE algorithm yielded greater insights than discriminant analysis of principal components (DAPC). Importantly, we found that markers with the highest (though non-significant) association scores were consistent with previous findings on tuber bruising. In addition, new genomic regions were found to be associated with tuber bruising. The GWAS results were backed by the transcriptomics differential expression analysis. The differential expression notably highlighted for the first time the role of two genes involved in cellular strength and mechanical force sensing in tuber resistance to bruising. We proposed a new visualisation, the HIDECAN plot, to integrate the results from the genomics and transcriptomics analyses, along with previous knowledge about genomic regions and candidate genes associated with the trait. CONCLUSION: This study offers a unique genome-wide exploration of the genetic components of tuber bruising. The role of genetic components affecting cellular strength and resistance to physical force, as well as mechanosensing mechanisms, was highlighted for the first time in the context of tuber bruising. We showcase the usefulness of genomic data from breeding programmes in identifying genomic regions whose association with the trait of interest merit further investigation. We demonstrate how confidence in these discoveries and their biological relevance can be increased by integrating results from transcriptomics analyses. The newly proposed visualisation provides a clear framework to summarise of both genomics and transcriptomics analyses, and places them in the context of previous knowledge on the trait of interest.
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Solanum tuberosum , Solanum tuberosum/genética , Solanum tuberosum/metabolismo , Tetraploidia , Locos de Características Quantitativas , Estudo de Associação Genômica Ampla , Melhoramento Vegetal , Tubérculos/metabolismo , FenótipoRESUMO
The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.
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Algoritmos , Modelos Biológicos , Biologia Computacional , Simulação por Computador , Bases de Dados de Ácidos Nucleicos , Redes Reguladoras de Genes , Humanos , Modelos Genéticos , Distrofia Muscular Facioescapuloumeral/genética , Distrofia Muscular Facioescapuloumeral/metabolismo , RNA-Seq , Software , Biologia de Sistemas , Integração de Sistemas , Teoria de Sistemas , TranscriptomaRESUMO
SUMMARY: We present sismonr, an R package for an integral generation and simulation of in silico biological systems. The package generates gene regulatory networks, which include protein-coding and non-coding genes along with different transcriptional and post-transcriptional regulations. The effect of genetic mutations on the system behaviour is accounted for via the simulation of genetically different in silico individuals. The ploidy of the system is not restricted to the usual haploid or diploid situations but can be defined by the user to higher ploidies. A choice of stochastic simulation algorithms allows us to simulate the expression profiles of the genes in the in silico system. We illustrate the use of sismonr by simulating the anthocyanin biosynthesis regulation pathway for three genetically distinct in silico plants. AVAILABILITY AND IMPLEMENTATION: The sismonr package is implemented in R and Julia and is publicly available on the CRAN repository (https://CRAN.R-project.org/package=sismonr). A detailed tutorial is available from GitHub at https://oliviaab.github.io/sismonr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Algoritmos , Software , Simulação por Computador , Redes Reguladoras de Genes , Humanos , PloidiasRESUMO
Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32,423 expressed sequences to drought and to abscisic acid (ABA) and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST ) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of the sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time-scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments.
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Redes Reguladoras de Genes , Helianthus/genética , Modelos Genéticos , Ácido Abscísico/genética , Algoritmos , Evolução Biológica , Secas , Regulação da Expressão Gênica de Plantas , Helianthus/fisiologia , Nitratos/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , TranscriptomaRESUMO
BACKGROUND: Raising an autistic child is associated with increased parenting stress relative to raising typically developing children. Increased parenting stress is associated with lower parent wellbeing, which in turn can negatively impact child wellbeing. AIMS: The current study sought to quantify parenting stress and parent health-related quality of life (HRQOL) in the autism context, and further understand the relationship between them by employing a relatively novel statistical method, Network Analysis. METHODS AND PROCEDURES: This cross-sectional study involved 476 parents of an autistic child. Parents completed an online survey requesting information on parent and child characteristics, parent's perceptions of their autistic child's symptoms and problem behaviours, and assessed their parenting stress and HRQOL. OUTCOMES AND RESULTS: Relative to normative data, parent HRQOL was significantly lower in terms of physical health and mental wellbeing. The structure extracted by the Network Analysis indicated that child age and externalising behaviours were the main contributors to parenting stress, and that externalising behaviours, ASD core behavioural symptoms, and parenting stress predicted HRQOL. CONCLUSIONS AND IMPLICATIONS: Parental responses to child-related factors likely determine parent HRQOL. Findings are discussed in relation to the transactional model, emphasising the importance of both parent and child wellbeing.
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Transtorno Autístico , Poder Familiar , Pais , Qualidade de Vida , Estresse Psicológico , Humanos , Qualidade de Vida/psicologia , Masculino , Feminino , Pais/psicologia , Criança , Estudos Transversais , Poder Familiar/psicologia , Estresse Psicológico/psicologia , Adulto , Transtorno Autístico/psicologia , Pré-Escolar , Saúde Mental , Transtorno do Espectro Autista/psicologia , Pessoa de Meia-Idade , Adolescente , Inquéritos e Questionários , Comportamento Problema/psicologia , Nível de SaúdeRESUMO
Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.
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Brucelose Bovina , Brucelose , Doenças dos Bovinos , Feminino , Humanos , Bovinos , Animais , Brucella abortus , Teorema de Bayes , Análise de Classes Latentes , Sensibilidade e Especificidade , Testes de Aglutinação/veterinária , Brucelose/epidemiologia , Brucelose/veterinária , Ensaio de Imunoadsorção Enzimática/veterinária , Brucelose Bovina/diagnóstico , Brucelose Bovina/epidemiologia , Anticorpos Antibacterianos , Testes Sorológicos/veterináriaRESUMO
Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.
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Mycoplasma bovis (Mbovis) was first detected in cattle in New Zealand (NZ) in July 2017. To prevent further spread, NZ launched a world-first National Eradication Programme in May 2018. Existing diagnostic tests for Mbovis have been applied in countries where Mbovis is endemic, for detecting infection following outbreaks of clinical disease. Diagnostic test evaluation (DTE) under NZ conditions was thus required to inform the Programme. We used Bayesian Latent Class Analysis on paired serum ELISA (ID Screen Mycoplasma bovis Indirect from IDvet) and tonsillar swabs (qPCR) for DTE in the absence of a gold standard. Tested samples were collected at slaughter between June 2018 and November 2019, from infected herds depopulated by the Programme. A first set of models evaluated the detection of active infection, i.e. the presence of Mbovis in the host. At a modified serology positivity threshold of SP%> = 90, estimates of animal-level ELISA sensitivity was 72.8% (95% credible interval 68.5%-77.4%), respectively 97.7% (95% credible interval 97.3%-98.1%) for specificity, while the qPCR sensitivity was 45.2% (95% credible interval 41.0%-49.8%), respectively 99.6% (95% credible interval 99.4%-99.8%) for specificity. In a second set of models, prior information about ELISA specificity was obtained from the National Beef Cattle Surveillance Programme, a population theoretically free-or very low prevalence-of Mbovis. These analyses aimed to evaluate the accuracy of the ELISA test targeting prior exposure to Mbovis, rather than active infection. The specificity of the ELISA for detecting exposure to Mbovis was 99.9% (95% credible interval 99.7%-100.0%), hence near perfect at the threshold SP%=90. This specificity estimate, considerably higher than in the first set of models, was equivalent to the manufacturer's estimate. The corresponding ELISA sensitivity estimate was 66.0% (95% credible interval 62.7%-70.7%). These results confirm that the IDvet ELISA test is an appropriate tool for determining exposure and infection status of herds, both to delimit and confirm the absence of Mbovis.
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Doenças dos Bovinos , Infecções por Mycoplasma , Mycoplasma bovis , Bovinos , Animais , Reação em Cadeia da Polimerase em Tempo Real/veterinária , Mycoplasma bovis/genética , Análise de Classes Latentes , Teorema de Bayes , Sensibilidade e Especificidade , Ensaio de Imunoadsorção Enzimática/veterinária , Ensaio de Imunoadsorção Enzimática/métodos , Testes Sorológicos , Doenças dos Bovinos/diagnóstico , Infecções por Mycoplasma/diagnóstico , Infecções por Mycoplasma/veterináriaRESUMO
Biomarkers for osteoarthritis (OA) in horses have been extensively investigated, but translation into clinical use has been limited due to cost, limited sensitivity, and practicality. Identifying novel biomarkers that overcome these limitations could facilitate early diagnosis and therapy. This study aimed to compare the concentrations of synovial fluid (SF) and plasma cell-free DNA (cfDNA) over time in control horses with those with induced carpal OA. Following an established model, unilateral carpal OA was induced in 9 of 17 healthy Thoroughbred fillies, while the remainder were sham-operated controls. Synovial fluid and plasma samples were obtained before induction of OA (Day 0) and weekly thereafter until Day 63, and cfDNA concentrations were determined using fluorometry. The SF cfDNA concentrations were significantly higher for OA joints than for sham-operated joints on Days 28 (median 1430 µg/L and 631 µg/L, respectively, p = 0.017) and 63 (median 1537 µg/L and 606 µg/L, respectively, p = 0.021). There were no significant differences in plasma cfDNA between the OA and the sham groups after induction of carpal OA. Plasma cfDNA measurement is not sufficiently sensitive for diagnostic purposes in this induced model of OA. Synovial fluid cfDNA measurement may be used as a biomarker to monitor early disease progression in horses with OA.
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SUMMARY: Among classical methods for module detection, SpaCEM(3) provides ad hoc algorithms that were shown to be particularly well adapted to specific features of biological data: high-dimensionality, interactions between components (genes) and integrated treatment of missingness in observations. The software, currently in its version 2.0, is developed in C++ and can be used either via command line or with the GUI under Linux and Windows environments. AVAILABILITY: The SpaCEM(3) software, a documentation and datasets are available from http://spacem3.gforge.inria.fr/.
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Algoritmos , Biologia Computacional/métodos , Software , Análise por Conglomerados , Genômica/métodos , Cadeias de Markov , Modelos Biológicos , Linguagens de ProgramaçãoRESUMO
Models of root systems are essential tools to understand how crops access and use soil resources during their development. However, scaling up such models to field scale remains a great challenge. In this paper, we detail a new approach to compute the growth of root systems based on density distribution functions. Growth was modelled as the dynamics of root apical meristems, using Partial Differential Equations. Trajectories of root apical meristems were used to deform root domains, the bounded support of root density functions, and update density distributions at each time increment of the simulation. Our results demonstrate that it is possible to predict the growth of root domains, by including developmentally meaningful parameters such as root elongation rate, gravitropic rate and branching rate. Models of this type are computationally more efficient than state-of-the-art finite volume methods. At a given prediction accuracy, computational time is over 10 times quicker; it allowed deformable models to be used to simulate ensembles of interacting plants. Application to root competition in crop-weed systems is demonstrated. The models presented in this study indicate that similar approaches could be developed to model shoot or whole plant processes with potential applications in crop and ecological modelling.
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Algoritmos , Simulação por Computador , Modelos Biológicos , Raízes de Plantas/crescimento & desenvolvimento , Produtos Agrícolas/crescimento & desenvolvimento , Meio Ambiente , Imageamento Tridimensional , Raízes de Plantas/anatomia & histologia , Plantas Daninhas/crescimento & desenvolvimentoRESUMO
Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.
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Vírus da Febre Suína Africana , Febre Suína Africana , Epidemias , Febre Suína Africana/epidemiologia , Animais , Animais Selvagens , Sus scrofa , SuínosRESUMO
The early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques-random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models-were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%), the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.
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Biological networks are a very convenient modeling and visualization tool to discover knowledge from modern high-throughput genomics and post-genomics data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.
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Redes Reguladoras de Genes , Modelos Genéticos , Biologia de Sistemas/métodos , Algoritmos , Teorema de Bayes , Perfilação da Expressão Gênica/instrumentação , Perfilação da Expressão Gênica/métodos , Software , Biologia de Sistemas/instrumentaçãoRESUMO
Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.
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Redes Reguladoras de Genes , Modelos Genéticos , Modelos Estatísticos , Biologia de Sistemas/métodos , Algoritmos , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Processos Estocásticos , Biologia de Sistemas/instrumentaçãoRESUMO
Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth "Dialogue for Reverse Engineering Assessments and Methods" (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on "Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.
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Teorema de Bayes , Redes Reguladoras de Genes , Genômica , MutaçãoRESUMO
UNLABELLED: Clustering of genes into groups sharing common characteristics is a useful exploratory technique for a number of subsequent computational analysis. A wide range of clustering algorithms have been proposed in particular to analyze gene expression data, but most of them consider genes as independent entities or include relevant information on gene interactions in a suboptimal way. We propose a probabilistic model that has the advantage to account for individual data (e.g., expression) and pairwise data (e.g., interaction information coming from biological networks) simultaneously. Our model is based on hidden Markov random field models in which parametric probability distributions account for the distribution of individual data. Data on pairs, possibly reflecting distance or similarity measures between genes, are then included through a graph, where the nodes represent the genes, and the edges are weighted according to the available interaction information. As a probabilistic model, this model has many interesting theoretical features. In addition, preliminary experiments on simulated and real data show promising results and points out the gain in using such an approach. AVAILABILITY: The software used in this work is written in C++ and is available with other supplementary material at http://mistis.inrialpes.fr/people/forbes/transparentia/supplementary.html.
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Perfilação da Expressão Gênica , Cadeias de Markov , Família Multigênica , Algoritmos , Análise por Conglomerados , Simulação por Computador , Redes Reguladoras de Genes , Glicólise , Redes e Vias Metabólicas , RNA Polimerase II/metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , SoftwareRESUMO
The different measurement techniques that interrogate biological systems provide means for monitoring the behavior of virtually all cell components at different scales and from complementary angles. However, data generated in these experiments are difficult to interpret. A first difficulty arises from high-dimensionality and inherent noise of such data. Organizing them into meaningful groups is then highly desirable to improve our knowledge of biological mechanisms. A more accurate picture can be obtained when accounting for dependencies between components (e.g., genes) under study. A second difficulty arises from the fact that biological experiments often produce missing values. When it is not ignored, the latter issue has been solved by imputing the expression matrix prior to applying traditional analysis methods. Although helpful, this practice can lead to unsound results. We propose in this paper a statistical methodology that integrates individual dependencies in a missing data framework. More explicitly, we present a clustering algorithm dealing with incomplete data in a Hidden Markov Random Field context. This tackles the missing value issue in a probabilistic framework and still allows us to reconstruct missing observations a posteriori without imposing any pre-processing of the data. Experiments on synthetic data validate the gain in using our method, and analysis of real biological data shows its potential to extract biological knowledge.