Clustering gene expression regulators: new approach to disease subtyping

BELMM: Bayesian model selection and random walk smoothing in time-series clustering

MOTIVATION

Due to advances in measuring technology, many new phenotype, gene expression, and other omics time-course datasets are now commonly available. Cluster analysis may provide useful information about the structure of such data.

RESULTS

In this work, we propose BELMM (Bayesian Estimation of Latent Mixture Models): a flexible framework for analysing, clustering, and modelling time-series data in a Bayesian setting. The framework is built on mixture modelling: first, the mean curves of the mixture components are assumed to follow random walk smoothing priors. Second, we choose the most plausible model and the number of mixture components using the Reversible-jump Markov chain Monte Carlo. Last, we assign the individual time series into clusters based on the similarity to the cluster-specific trend curves determined by the latent random walk processes. We demonstrate the use of fast and slow implementations of our approach on both simulated and real time-series data using widely available software R, Stan, and CU-MSDSp.

AVAILABILITY AND IMPLEMENTATION

The French mortality dataset is available at http://www.mortality.org, the Drosophila melanogaster embryogenesis gene expression data at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121160. Details on our simulated datasets are available in the Supplementary Material, and R scripts and a detailed tutorial on GitHub at https://github.com/ollisa/BELMM. The software CU-MSDSp is available on GitHub at https://github.com/jtchavisIII/CU-MSDSp.

Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration

Simple Summary

In this study, we normalized trajectories containing both mesenchymal and epithelial cells to remove the effect of cell location on clustering, and performed a dimensionality reduction on the time series data before clustering. When the clustering results were superimposed on the trajectories prior to normalization, the results still showed similarities in location, indicating that this method can find cells with similar migration patterns. These data highlight the reliability of this method in identifying consistent migration patterns in collective cell migration.

Abstract

Collective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a colony would facilitate an understanding of complex cell migration patterns. Here, we used electrospun fibers as the extracellular matrix for the in vitro modeling of collective cell migration, clustering of mesenchymal and epithelial cells based on trajectories, and analysis of collective migration patterns based on trajectory similarity. We normalized the trajectories to eliminate the effect of cell location on clustering and used uniform manifold approximation and projection to perform dimensionality reduction on the time-series data before clustering. When the clustering results were superimposed on the trajectories before normalization, the results still exhibited positional similarity, thereby demonstrating that this method can identify cells with similar migration patterns. The same cluster contained both mesenchymal and epithelial cells, and this result was related to cell location and cell division. These data highlight the reliability of this method in identifying consistent migration patterns during collective cell migration. This provides new insights into the epithelial–mesenchymal interactions that affect migration patterns.

Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed-ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.

Investigating the gene expression profiles of rehabilitated Florida manatees (Trichechus manatus latirostris) following red tide exposure

To investigate a Florida manatee (Trichechus manatus latirostris) mortality event following a red tide bloom in Southwest Florida, an RNA sequencing experiment was conducted. Gene expression changes in white blood cells were assessed in manatees rescued from a red tide affected area (n = 4) and a control group (n = 7) using RNA sequencing. The genes with the largest fold changes were compared between the two groups to identify molecular pathways related to cellular and disease processes. In total, 591 genes (false discovery rate <0.05) were differentially expressed in the red tide group. Of these, 158 were upregulated and 433 were downregulated. This suggests major changes in white blood cell composition following an exposure to red tide. The most highly upregulated gene, Osteoclast associated 2C immunoglobulin-like receptor (OSCAR), was upregulated 12-fold. This gene is involved in initiating the immune response and maintaining a role in adaptive and innate immunity. The most highly downregulated gene, Piccolo presynaptic cytomatrix protein (PCLO), was downregulated by a factor of 977-fold. This gene is associated with cognitive functioning and neurotransmitter release. Downregulation of this gene in other studies was associated with neuronal loss and neuron synapse dysfunction. Among the cellular pathways that were most affected, immune response, including inflammation, wounds and injuries, cell proliferation, and apoptosis were the most predominant. The pathway with the most differentially expressed genes was the immune response pathway with 98 genes involved, many of them downregulated. Assessing the changes in gene expression associated with red tide exposure enhances our understanding of manatee immune response to the red tide toxins and will aid in the development of red tide biomarkers.

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

Bacterial growth curves, representing population dynamics, are still poorly understood. The growth curves are commonly analyzed by model-based theoretical fitting, which is limited to typical S-shape fittings and does not elucidate the dynamics in their entirety. Thus, whether a certain growth condition results in any particular pattern of growth curve remains unclear. To address this question, up-to-date data mining techniques were applied to bacterial growth analysis for the first time. Dynamic time warping (DTW) and derivative DTW (DDTW) were used to compare the similarity among 1015 growth curves of 28 Escherichia coli strains growing in three different media. In the similarity evaluation, agglomerative hierarchical clustering, assessed with four statistic benchmarks, successfully categorized the growth curves into three clusters, roughly corresponding to the three media. Furthermore, a simple benchmark was newly proposed, providing a highly improved accuracy (~99%) in clustering the growth curves corresponding to the growth media. The biologically reasonable categorization of growth curves suggested that DTW and DDTW are applicable for bacterial growth analysis. The bottom-up clustering results indicate that the growth media determine some specific patterns of population dynamics, regardless of genomic variation, and thus have a higher priority of shaping the growth curves than the genomes do.

PC1/3 KD Macrophages Exhibit Resistance to the Inhibitory Effect of IL-10 and a Higher TLR4 Activation Rate, Leading to an Anti-Tumoral Phenotype

During tumorigenesis, macrophages are recruited by tumors and orientated towards a pro-tumoral phenotype. One of the main anti-tumoral immunotherapy consists of their re-polarization in an anti-tumoral phenotype. We have demonstrated that the inhibition of proprotein convertase 1/3 combined with TLR4 activation in macrophages is a promising strategy. These macrophages display pro-inflammatory and anti-tumoral phenotypes. A hallmark is a stronger activation of the pro-inflammatory NFKB pathway. We believe that this can be explained by a modification of TLR4 expression at the cell surface or MYD88 cleavage since it exhibits a potential cleavage site for proprotein convertases. We tested these hypotheses through immunofluorescence and Western blot experiments. A proteomics study was also performed to test the sensitivity of these macrophages to IL-10. We demonstrated that these macrophages treated with LPS showed a quicker re-expression of TLR4 at the cell surface. The level of MYD88 was also higher when TLR4 was internalized. Moreover, these macrophages were resistant to the pro-tumoral effect of IL-10 and still produced pro-inflammatory factors. This established that the sensitivity to anti-inflammatory molecules and the length of TLR4 desensitization were reduced in these macrophages. Therefore, during antitumoral immunotherapy, a repeated stimulation of TLR4 may reactivate PC1/3 inhibited macrophages even in an anti-inflammatory environment.

Mapping Spatiotemporal Microproteomics Landscape in Experimental Model of Traumatic Brain Injury Unveils a link to Parkinson's Disease

Traumatic brain injury (TBI) represents a major health concerns with no clinically-approved FDA drug available for therapeutic intervention. Several genomics and neuroproteomics studies have been employed to decipher the underlying pathological mechanisms involved that can serve as potential neurotherapeutic targets and unveil a possible underlying relation of TBI to other secondary neurological disorders. In this work, we present a novel high throughput systems biology approach using a spatially resolved microproteomics platform conducted on different brain regions in an experimental rat model of moderate of controlled cortical injury (CCI) at a temporal pattern postinjury (1 day, 3 days, 7 days, and 10 days). Mapping the spatiotemporal landscape of signature markers in TBI revealed an overexpression of major protein families known to be implicated in Parkinson's disease (PD) such as GPR158, HGMB1, synaptotagmin and glutamate decarboxylase in the ipsilateral substantia nigra. In silico bioinformatics docking experiments indicated the potential correlation between TBI and PD through alpha-synuclein. In an in vitro model, stimulation with palmitoylcarnitine triggered an inflammatory response in macrophages and a regeneration processes in astrocytes which also further confirmed the in vivo TBI proteomics data. Taken together, this is the first study to assess the microproteomics landscape in TBI, mainly in the substantia nigra, thus revealing a potential predisposition for PD or Parkinsonism post-TBI.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering

The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series.

A comprehensive overview of oncogenic pathways in human cancer

Alterations of biological pathways can lead to oncogenesis. An overview of these oncogenic pathways would be highly valuable for researchers to reveal the pathogenic mechanism and develop novel therapeutic approaches for cancers. Here, we reviewed approximately 8500 literatures and documented experimentally validated cancer-pathway associations as benchmarking data set. This data resource includes 4709 manually curated relationships between 1557 paths and 49 cancers with 2427 upstream regulators in 7 species. Based on this resource, we first summarized the cancer-pathway associations and revealed some commonly deregulated pathways across tumor types. Then, we systematically analyzed these oncogenic pathways by integrating TCGA pan-cancer data sets. Multi-omics analysis showed oncogenic pathways may play different roles across tumor types under different omics contexts. We also charted the survival relevance landscape of oncogenic pathways in 26 tumor types, identified dominant omics features and found survival relevance for oncogenic pathways varied in tumor types and omics levels. Moreover, we predicted upstream regulators and constructed a hierarchical network model to understand the pathogenic mechanism of human cancers underlying oncogenic pathway context. Finally, we developed `CPAD’ (freely available at http://bio-bigdata.hrbmu.edu.cn/CPAD/), an online resource for exploring oncogenic pathways in human cancers, that integrated manually curated cancer-pathway associations, TCGA pan-cancer multi-omics data sets, drug–target data, drug sensitivity and multi-omics data for cancer cell lines. In summary, our study provides a comprehensive characterization of oncogenic pathways and also presents a valuable resource for investigating the pathogenesis of human cancer.

Comprehensive literature data-mining analysis reveals a broad genetic network functionally associated with autism spectrum disorder

Previous studies have indicated that genetic factors are the predominate cause of Autism spectrum disorder (ASD). Nevertheless, to the best of our knowledge, to date no systematic study has summarized these data and provided an objective, complete list of genes with demonstrated associations with ASD. The present study included a literature data mining analysis of >2,064 articles including publications from January 2000 to April 2016, which identified 488 ASD target genes. Gene set enrichment analysis (GSEA), sub-network enrichment analysis (SNEA) and network connectivity analysis (NCA) were conducted to assess the functional profile and pathogenic significance of these genes. A total of 2 literature metrics were proposed to prioritize the curated ASD genes with specific significance. This approach resulted in the development of an ASD genetic database. Subsequent analysis indicated that 391 of the 488 genes were enriched in 97 biological pathways (P<1×10⁻⁸), demonstrating significant functional associations with each other. The majority of these curated ASD genes also serve significant roles in the pathogenesis of other neuropsychiatric disorders. These results suggest that the genetic causes of ASD are within a large network composed of functionally-associated genes. The genetic database, together with the metric scores developed in the present study, provides a basis for future biological/genetic modeling in the field.

Patient similarity for precision medicine: A systematic review

Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.

RhoA Inhibitor Treatment At Acute Phase of Spinal Cord Injury May Induce Neurite Outgrowth and Synaptogenesis

The therapeutic use of RhoA inhibitors (RhoAi) has been experimentally tested in spinal cord injury (SCI). In order to decipher the underlying molecular mechanisms involved in such a process, an in vitro neuroproteomic-systems biology platform was developed in which the pan-proteomic profile of the dorsal root ganglia (DRG) cell line ND7/23 DRG was assessed in a large array of culture conditions using RhoAi and/or conditioned media obtained from SCI ex vivo derived spinal cord slices. A fine mapping of the spatio-temporal molecular events of the RhoAi treatment in SCI was performed. The data obtained allow a better understanding of regeneration/degeneration induced above and below the lesion site. Results notably showed a time-dependent alteration of the transcription factors profile along with the synthesis of growth cone-related factors (receptors, ligands, and signaling pathways) in RhoAi treated DRG cells. Furthermore, we assessed in a rat SCI model the in vivo impact of RhoAi treatment administered in situ via alginate scaffold that was combined with FK506 delivery. The improved recovery of locomotion was detected only at the early postinjury time points, whereas after overall survival a dramatic increase of synaptic contacts on outgrowing neurites in affected segments was observed. We validate these results by in vivo proteomic studies along the spinal cord segments from tissue and secreted media analyses, confirming the increase of the synaptogenesis expression factors under RhoAi treatment. Taken together, we demonstrate that RhoAi treatment seems to be useful to stimulate neurite outgrowth in both in vitro as well in vivo environments. However, for in vivo experiments there is a need for sustained delivery regiment to facilitate axon regeneration and promote synaptic reconnections with appropriate target neurons also at chronic phase, which in turn may lead to higher assumption for functional improvement.

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis

Background:

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer’s disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust.

Objectives:

This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts.

Methods:

Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis.

Results:

Gene and pathway level models performed similarly to each other and to a model based on demographic information only.

Conclusions:

Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Deciphering the Role of Emx1 in Neurogenesis: A Neuroproteomics Approach

Emx1 has long been implicated in embryonic brain development. Previously we found that mice null of Emx1 gene had smaller dentate gyri and reduced neurogenesis, although the molecular mechanisms underlying this defect was not well understood. To decipher the role of Emx1 gene in neural regeneration and the timing of its involvement, we determine the frequency of neural stem cells (NSCs) in embryonic and adult forebrains of Emx1 wild type (WT) and knock out (KO) mice in the neurosphere assay. Emx1 gene deletion reduced the frequency and self-renewal capacity of NSCs of the embryonic brain but did not affect neuronal or glial differentiation. Emx1 KO NSCs also exhibited a reduced migratory capacity in response to serum or vascular endothelial growth factor (VEGF) in the Boyden chamber migration assay compared to their WT counterparts. A thorough comparison between NSC lysates from Emx1 WT and KO mice utilizing 2D-PAGE coupled with tandem mass spectrometry revealed 38 proteins differentially expressed between genotypes, including the F-actin depolymerization factor Cofilin. A global systems biology and cluster analysis identified several potential mechanisms and cellular pathways implicated in altered neurogenesis, all involving Cofilin1. Protein interaction network maps with functional enrichment analysis further indicated that the differentially expressed proteins participated in neural-specific functions including brain development, axonal guidance, synaptic transmission, neurogenesis, and hippocampal morphology, with VEGF as the upstream regulator intertwined with Cofilin1 and Emx1. Functional validation analysis indicated that apart from the overall reduced level of phosphorylated Cofilin1 (p-Cofilin1) in the Emx1 KO NSCs compared to WT NSCs as demonstrated in the western blot analysis, VEGF was able to induce more Cofilin1 phosphorylation and FLK expression only in the latter. Our results suggest that a defect in Cofilin1 phosphorylation induced by VEGF or other growth factors might contribute to the reduced neurogenesis in the Emx1 null mice during brain development.

Network-Assisted Disease Classification and Biomarker Discovery

Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput "omics" data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.

Deciphering glycomics and neuroproteomic alterations in experimental traumatic brain injury: Comparative analysis of aspirin and clopidogrel treatment

As populations age, the number of patients sustaining traumatic brain injury (TBI) and concomitantly receiving preinjury antiplatelet therapy such as aspirin (ASA) and clopidogrel (CLOP) is rising. These drugs have been linked with unfavorable clinical outcomes following TBI, where the exact mechanism(s) involved are still unknown. In this novel work, we aimed to identify and compare the altered proteome profile imposed by ASA and CLOP when administered alone or in combination, prior to experimental TBI. Furthermore, we assessed differential glycosylation PTM patterns following experimental controlled cortical impact model of TBI, ASA, CLOP, and ASA + CLOP. Ipsilateral cortical brain tissues were harvested 48 h postinjury and were analyzed using an advanced neuroproteomics LC-MS/MS platform to assess proteomic and glycoproteins alterations. Of interest, differential proteins pertaining to each group (22 in TBI, 41 in TBI + ASA, 44 in TBI + CLOP, and 34 in TBI + ASA + CLOP) were revealed. Advanced bioinformatics/systems biology and clustering analyses were performed to evaluate biological networks and protein interaction maps illustrating molecular pathways involved in the experimental conditions. Results have indicated that proteins involved in neuroprotective cellular pathways were upregulated in the ASA and CLOP groups when given separately. However, ASA + CLOP administration revealed enrichment in biological pathways relevant to inflammation and proinjury mechanisms. Moreover, results showed differential upregulation of glycoproteins levels in the sialylated N-glycans PTMs that can be implicated in pathological changes. Omics data obtained have provided molecular insights of the underlying mechanisms that can be translated into clinical bedside settings.

Proteomic Analysis of the Spatio-temporal Based Molecular Kinetics of Acute Spinal Cord Injury Identifies a Time- and Segment-specific Window for Effective Tissue Repair

Spinal cord injury (SCI) represents a major debilitating health issue with a direct socioeconomic burden on the public and private sectors worldwide. Although several studies have been conducted to identify the molecular progression of injury sequel due from the lesion site, still the exact underlying mechanisms and pathways of injury development have not been fully elucidated. In this work, based on OMICs, 3D matrix-assisted laser desorption ionization (MALDI) imaging, cytokines arrays, confocal imaging we established for the first time that molecular and cellular processes occurring after SCI are altered between the lesion proximity, i.e. rostral and caudal segments nearby the lesion (R1-C1) whereas segments distant from R1-C1, i.e. R2-C2 and R3-C3 levels coexpressed factors implicated in neurogenesis. Delay in T regulators recruitment between R1 and C1 favor discrepancies between the two segments. This is also reinforced by presence of neurites outgrowth inhibitors in C1, absent in R1. Moreover, the presence of immunoglobulins (IgGs) in neurons at the lesion site at 3 days, validated by mass spectrometry, may present additional factor that contributes to limited regeneration. Treatment in vivo with anti-CD20 one hour after SCI did not improve locomotor function and decrease IgG expression. These results open the door of a novel view of the SCI treatment by considering the C1 as the therapeutic target.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Gene expression profiling for targeted cancer treatment

INTRODUCTION

There is certain degree of frustration and discontent in the area of microarray gene expression data analysis of cancer datasets. It arises from the mathematical problem called 'curse of dimensionality,' which is due to the small number of samples available in training sets, used for calculating transcriptional signatures from the large number of differentially expressed (DE) genes, measured by microarrays. The new generation of causal reasoning algorithms can provide solutions to the curse of dimensionality by transforming microarray data into activity of a small number of cancer hallmark pathways. This new approach can make feature space dimensionality optimal for mathematical signature calculations.

AREAS COVERED

The author reviews the reasons behind the current frustration with transcriptional signatures derived from DE genes in cancer. He also provides an overview of the novel methods for signature calculations based on differentially variable genes and expression regulators. Furthermore, the authors provide perspectives on causal reasoning algorithms that use prior knowledge about regulatory events described in scientific literature to identify expression regulators responsible for the differential expression observed in cancer samples.

EXPERT OPINION

The author advocates causal reasoning methods to calculate cancer pathway activity signatures. The current challenge for these algorithms is in ensuring quality of the knowledgebase. Indeed, the development of cancer hallmark pathway collections, together with statistical algorithms to transform activity of expression regulators into pathway activity, are necessary for causal reasoning to be used in cancer research.