As a statistical method integrating multi-features and multi-data, meta-analysis was introduced to the field of life science in the 1990s. With the rapid advances in high-throughput technologies, life omics, the core of which are genomics, transcriptomics and proteomics, is becoming the new hot spot of life science. Although the fast output of massive data has promoted the development of omics study, it results in excessive data that are difficult to integrate systematically. In this case, meta-analysis is frequently applied to analyze different types of data and is improved continuously. Here, we first summarize the representative meta-analysis methods systematically, and then study the current applications of meta-analysis in various omics fields, finally we discuss the still-existing problems and the future development of meta-analysis.
Genomics ; Meta-Analysis as Topic ; Proteomics ; Statistics as Topic

Objective To explore whether there were abnormalities of behavioral tests and CNV-like potential in stressed rats following repeatedly forced swim stress.Methods Forty male rats were randomly divided into 4 groups: the control groups (Control-1 and Control-2) and the stress groups ( Stress-1 and Stress-2).Rats in stress groups were administered to repeatedly forced swim 7 or 14 days respectively.Body weight gain, saccharin preference test and open field test were performed.After being anesthetized with urethane, CNV-like potentials were elicited by condition-test stimulus.Results Results of behavioral tests displayed less body weights (F =253.60, P＜0.001 ) and less saccharine solution intake (F= 13.67, P=0.001 ) in stressed group rats and significant effects of stress on the number of crossing squares, the duration of rearing and the number of grooming in open field test.CNV amplitudes were lower in the stressed rats than those in control (F=21.312, P＜0.01 ).Conclusion This study provides an important evidence of changes of CNV-like potential in depressed rats following repeatedly forced swim stress.Based on this study, ER Ps should be taken into consideration and applied as the useful tools in the research work of depressed animal models.

AIM To observe the effects of Huangdi Anxiao Capsules (Puerariae lobatae Radix,Eriobotryae Folium,Notoginseng Radix et Rhizoma,etc.) on blood glucose and insulin resistance in GK rats with type 2 diabetes mellitus (T2DM) and its possible mechanism of action.METHODS GK rats with T2DM included in the experiment were randomly divided into model group,rosiglitazone (1.44 mg/kg) group,Shenqi Jiangtang Granules (1.08 g/kg) group,Huangdi Anxiao Capsules high,medium and low (12,6,3 g/kg) group and normal control group of Wistar rats.After six weeks of consecutive administration,fasting blood glucose (FBG),serum insulin (FINS),fasting plasma glucose (FPG),and insulin resistance index (HOMA-IR) were measured in all groups.Serum biochemical indexes of (TG,TC,HDL-C,LDL-C),glycosylated hemoglobin (HbA1c),glucagon-like peptide (GLP-1) and insulin-like growth factor (IGF-1) were measured.The pancreatic tissue was obtained by routine paraffin embedding and HE staining to observe the pathological changes.RESULTS Compared with the normal group,FBG,FPG,FINS,HOMA-IR,TG,TC,HDL-C,LDL-C and HbA1c of the model group were significantly higher (P ＜0.01),and GLP-1 and IGF-1 expressions were markedly decreased (P ＜ 0.01).Compared with the model group,the levels of FBG,FPG,FINS,HOMA-IR,TG,TC,HDL-C,LDL-C and HbA1 c in Huangdi Anxiao Capsules high-,medium-and low-dose group were significantly decreased (P ＜ 0.05,P ＜0.01);GLP-1 and IGF-1 expressions were markedly increased (P ＜0.05,P ＜0.01),compared with the model group,the structure changes of pancreatic tissue in Huangdi Anxiao Capsules high-,medium-and low-dose groups largely reduced.CONCLUSION Huangdi Anxiao Capsules can reduce GK rats fasting blood glucose,insulin resistance,and its mechanism may be related to promoting the emergence and proliferation of pancreatic islet cells,improving the function of islet beta cells,and increasing insulin secretion.

With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarker discovery strategies and methods have become one of research hotspots. Feature selection and machine learning methods have good effects on solving the "dimensionality" and "sparsity" problems of proteomics data, which have been widely used in the discovery of protein biomarkers. Here, we systematically review the strategy of protein biomarker discovery and the frequently-used machine learning methods. Also, the review illustrates the prospects and limitations of deep learning in this field. It is aimed at providing a valuable reference for corresponding researchers.
Algorithms ; Biomarkers ; Humans ; Machine Learning ; Mass Spectrometry ; Proteomics

In case/control gene expression data, differential expression (DE) represents changes in gene expression levels across various biological conditions, whereas differential co-expression (DC) represents an alteration of correlation coefficients between gene pairs. Both DC and DE genes have been studied extensively in human diseases. However, effective approaches for integrating DC-DE analyses are lacking. Here, we report a novel analytical framework named DC&DEmodule for integrating DC and DE analyses and combining information from multiple case/control expression datasets to identify disease-related gene co-expression modules. This includes activated modules (gaining co-expression and up-regulated in disease) and dysfunctional modules (losing co-expression and down-regulated in disease). By applying this framework to microarray data associated with liver, gastric and colon cancer, we identified two, five and two activated modules and five, five and one dysfunctional module(s), respectively. Compared with the other methods, pathway enrichment analysis demonstrated the superior sensitivity of our method in detecting both known cancer-related pathways and those not previously reported. Moreover, we identified 17, 69, and 11 module hub genes that were activated in three cancers, which included 53 known and three novel cancer prognostic markers. Random forest classifiers trained by the hub genes showed an average of 93% accuracy in differentiating tumor and adjacent normal samples in the TCGA and GEO database. Comparison of the three cancers provided new insights into common and tissue-specific cancer mechanisms. A series of evaluations demonstrated the framework is capable of integrating the rapidly accumulated expression data and facilitating the discovery of dysregulated processes.
Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Microarray Analysis ; Neoplasms/genetics*