Xiangtian Yu

Identifying critical transitions of complex diseases based on a single sample

MOTIVATION Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before the serious deterioration of a disease is a challenging task, because the state of the system may show little apparent change or symptoms before this critical transition during disease progression. By exploring the rich interaction information provided by high-throughput data, the dynamical network biomarker (DNB) can identify the pre-disease state, but this requires multiple samples to reach a correct diagnosis for one individual, thereby restricting its clinical application. RESULTS In this article, we have developed a novel computational approach based on the DNB theory and differential distributions between the expressions of DNB and non-DNB molecules, which can detect the pre-disease state reliably even from a single sample taken from one individual, by compensating insufficient samples with existing datasets from population studies. Our approach has been validated by the successful identification of pre-disease samples from subjects or individuals before the emergence of disease symptoms for acute lung injury, influenza and breast cancer.

Prediction and early diagnosis of complex diseases by edge-network

MOTIVATION In this article, we develop a novel edge-based network i.e. edge-network, to detect early signals of diseases by identifying the corresponding edge-biomarkers with their dynamical network biomarker score from dynamical network biomarkers. Specifically, we derive an edge-network based on the second-order statistics representation of gene expression profiles, which is able to accurately represent the stochastic dynamics of the original biological system (with Gaussian distribution assumption) by combining with the traditional node-network, which is based only on the first-order statistics representation of the noisy data. In other words, we show that the stochastic network of a biological system can be described by the integration of its node-network and its edge-network in an accurate manner. RESULTS By applying edge-network analysis to gene expressions of healthy adults within live influenza experiment sampling at time points before the appearance of infection symptoms, we identified the edge-biomarkers (80 edges with 22 densely connected genes) discovered in edge-networks corresponding to symptomatic adults, which were used to predict the subsequent outcomes of influenza infection. In particular, we not only correctly predict the final infection outcome of each individual at an early time point before his/her clinic symptom but also reveal the key molecules during the disease progression. The prediction accuracy achieves ~90% under the leave-one-out cross-validation. Furthermore, we demonstrate the superiority of our method on disease classification and predication by comparing with the conventional node-biomarkers. Our edge-network analysis not only opens a new way to understand pathogenesis at a network level due to the new representation for a stochastic network, but also provides a powerful tool to make the early diagnosis of diseases. CONTACT lnchen@sibs.ac.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Edge biomarkers for classification and prediction of phenotypes

In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.

Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to “diagnose disease”, sDNB is based on the information of differential associations, thereby having the ability to “predict disease” or “diagnose near-future disease”. Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.

Unravelling personalized dysfunctional gene network of complex diseases based on differential network model

In the conventional analysis of complex diseases, the control and case samples are assumed to be of great purity. However, due to the heterogeneity of disease samples, many disease genes are even not always consistently up-/down-regulated, leading to be under-estimated. This problem will seriously influence effective personalized diagnosis or treatment. The expression variance and expression covariance can address such a problem in a network manner. But, these analyses always require multiple samples rather than one sample, which is generally not available in clinical practice for each individual. To extract the common and specific network characteristics for individual patients in this paper, a novel differential network model, e.g. personalized dysfunctional gene network, is proposed to integrate those genes with different features, such as genes with the differential gene expression (DEG), genes with the differential expression variance (DEVG) and gene-pairs with the differential expression covariance (DECG) simultaneously, to construct personalized dysfunctional networks. This model uses a new statistic-like measurement on differential information, i.e., a differential score (DEVC), to reconstruct the differential expression network between groups of normal and diseased samples; and further quantitatively evaluate different feature genes in the patient-specific network for each individual. This DEVC-based differential expression network (DEVC-net) has been applied to the study of complex diseases for prostate cancer and diabetes. (1) Characterizing the global expression change between normal and diseased samples, the differential gene networks of those diseases were found to have a new bi-coloured topological structure, where their non hub-centred sub-networks are mainly composed of genes/proteins controlling various biological processes. (2) The differential expression variance/covariance rather than differential expression is new informative sources, and can be used to identify genes or gene-pairs with discriminative power, which are ignored by traditional methods. (3) More importantly, DEVC-net is effective to measure the expression state or activity of different feature genes and their network or modules in one sample for an individual. All of these results support that DEVC-net indeed has a clear advantage to effectively extract discriminatively interpretable features of gene/protein network of one sample (i.e. personalized dysfunctional network) even when disease samples are heterogeneous, and thus can provide new features like gene-pairs, in addition to the conventional individual genes, to the analysis of the personalized diagnosis and prognosis, and a better understanding on the underlying biological mechanisms.

Individual-specific edge-network analysis for disease prediction

Abstract Predicting pre-disease state or tipping point just before irreversible deterioration of health is a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory opens a new way to study this problem by exploring rich dynamical and high-dimensional information of omics data. Although theoretically ENA has the ability to identify the pre-disease state during the disease progression, it requires multiple samples for such prediction on each individual, which are generally not available in clinical practice, thus limiting its applications in personalized medicine. In this work to overcome this problem, we propose the individual-specific ENA (iENA) with DNB to identify the pre-disease state of each individual in a single-sample manner. In particular, iENA can identify individual-specific biomarkers for the disease prediction, in addition to the traditional disease diagnosis. To demonstrate the effectiveness, iENA was applied to the analysis on omics data of H3N2 cohorts and successfully detected early-warning signals of the influenza infection for each individual both on the occurred time and event in an accurate manner, which actually achieves the AUC larger than 0.9. iENA not only found the new individual-specific biomarkers but also recovered the common biomarkers of influenza infection reported from previous works. In addition, iENA also detected the critical stages of multiple cancers with significant edge-biomarkers, which were further validated by survival analysis on both TCGA data and other independent data.

Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data

Motivation: Integrating different omics profiles is a challenging task, which provides a comprehensive way to understand complex diseases in a multi‐view manner. One key for such an integration is to extract intrinsic patterns in concordance with data structures, so as to discover consistent information across various data types even with noise pollution. Thus, we proposed a novel framework called ‘pattern fusion analysis’ (PFA), which performs automated information alignment and bias correction, to fuse local sample‐patterns (e.g. from each data type) into a global sample‐pattern corresponding to phenotypes (e.g. across most data types). In particular, PFA can identify significant sample‐patterns from different omics profiles by optimally adjusting the effects of each data type to the patterns, thereby alleviating the problems to process different platforms and different reliability levels of heterogeneous data. Results: To validate the effectiveness of our method, we first tested PFA on various synthetic datasets, and found that PFA can not only capture the intrinsic sample clustering structures from the multi‐omics data in contrast to the state‐of‐the‐art methods, such as iClusterPlus, SNF and moCluster, but also provide an automatic weight‐scheme to measure the corresponding contributions by data types or even samples. In addition, the computational results show that PFA can reveal shared and complementary sample‐patterns across data types with distinct signal‐to‐noise ratios in Cancer Cell Line Encyclopedia (CCLE) datasets, and outperforms over other works at identifying clinically distinct cancer subtypes in The Cancer Genome Atlas (TCGA) datasets. Availability and implementation: PFA has been implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/PFApackage_0.1.rar. Contact: lnchen@sibs.ac.cn, liujuan@whu.edu.cn or zengtao@sibs.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Characterizing and Discriminating Individual Steady State of Disease-Associated Pathway.

Recently, individual heterogeneity is becoming a hot topic with the development of precision medicine. It is still a challenge to characterize the intrinsic regulatory convergence along with temporal gene expression change corresponding to different individuals. Considering the similar functions will be more suitable than the same genes to find consistent function rather than chaotic genes, we propose a computational framework (ABP: Attractor analysis of Boolean network of Pathway) to recognize the key pathways associated with phenotype change, which uses the network attractor to represent the steady pathway states corresponding to the final biological sate of individuals. By analyzing temporal gene expressions, ABP has shown its ability to recognize key pathways and infer the potential consensus functional cascade among pathways, and especially group individuals corresponding to disease state well.

Mining K-mers of Various Lengths in Biological Sequences

Counting the occurrence frequency of each k-mer in a biological sequence is an important step in many bioinformatics applications. However, most k-mer counting algorithms rely on a given k to produce single-length k-mers, which is inefficient for sequence analysis for different k. Moreover, existing k-mer counters focus more on DNA sequences and less on protein ones. In practice, the analysis of k-mers in protein sequences can provide substantial biological insights in structure, function and evolution. To this end, an efficient algorithm, called VLmer (Various Length k-mer mining), is proposed to mine k-mers of various lengths termed vl-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index method. Moreover, to the best of our knowledge, VLmer is the first able to mine k-mers of various lengths in both DNA and protein sequences.

Integration of multiple heterogeneous omics data

Integration of different genomic profiles is challenging to understand complex diseases in a multi-view manner. Computational method is needed to preserve useful information of data types as well as correct bias. Thus, we proposed a novel framework pattern fusion analysis (PFA), to fuse the local sample patterns into a global pattern of patients with respect to the underlying data, by adaptively aligning the information in each type of biological data. In particular, PFA can adjust the distinct data types and achieve more robust sample pattern within different profiles. To validate the effectiveness of PFA, we tested PFA on various synthetic datasets and found that PFA is able to effectively capture the intrinsic clustering structure than the state-of-the-art integrative methods, such as moCluster, iClusterPlus and SNF. Moreover, in a case study on kidney cancer, PFA not only identified the multi-way feature modules among the prior-known disease associated genes, methylations and miRNAs, but also outperformed in cancer subtypes identification and could get effective clinical prognosis prediction. Totally, PFA not only provides new insights on the more holistic & systems-level sample pattern, but also supplies a new way for selecting more informative types of biological data.