Adaptive thresholds to detect differentially expressed genes in microarray data.
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ABSTRACT:
SUBMITTER: Fukuoka Y
PROVIDER: S-EPMC3163930 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this study, we empirically evaluated the consistency and accuracy of five different methods to detect differentially expressed genes (DEGs) based on microarray data.Five different methods were compared, including the t-test, significance analysis of microarrays (SAM), the empirical Bayes t-test (eBayes), t-tests relative to a threshold (TREAT), and assumption adequacy averaging (AAA). The percentage of overlapping genes (POG) and the percentage of overlapping genes related (POGR) scores were used to rank the different methods on their ability to maintain a consistent list of DEGs both within the same data set and across two different data sets concerning the same disease. The power of each method was evaluated based on a simulation approach which mimics the multivariate distribution of the original microarray data.For smaller sample sizes (6 or less per group), moderated versions of the t-test (SAM, eBayes, and TREAT) were superior in terms of both power and consistency relative to the t-test and AAA, with TREAT having the highest consistency in each scenario. Differences in consistency were most pronounced for comparisons between two different data sets for the same disease. For larger sample sizes AAA had the highest power for detecting small effect sizes, while TREAT had the lowest.For smaller sample sizes moderated versions of the t-test can generally be recommended, while for larger sample sizes selection of a method to detect DEGs may involve a compromise between consistency and power.
| S-EPMC3993975 | biostudies-literature
Project description:Glioma represents one of the main causes of cancer-related death worldwide. Unfortunately, its exact molecular mechanisms remain poorly understood, which limits the prognosis and therapy. This study aimed to identify the critical genes, transcription factors and the possible biochemical pathways that may affect glioma progression at transcription level. After downloading micro-array data from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between glioma and normal samples were screened. We predicted novel glioma-related genes and carried on online software DAVID to conduct GO enrichment and transcription factor analysis of these selected genes. String software was applied to construct a PPI protein interaction network, as well as to find the key genes and transcription factors in the regulation of glioma. A total of 97 DEGs were identified associated with cancer, the GO enrichment analysis indicated these DEGs were mainly relevant to immune responses as well as regulation of cell growth. In addition, the transcription factor analysis showed these DEGs were regulated by the binding sites of transcription factors GLI2, SP1, SMAD7, SMAD3, RELA, STAT5B, CTNNB1, STAT5A, TFAP2A and SP3. PPI protein interaction network analysis demonstrated the hub nodes in the interaction network were EGFR, TGFB1, FN1 and MYC. The hub DEGs may be the most critical in glioma and could be considered as drug targets for glioma therapy after further exploration. Besides, with the identification of regulating transcription factors, the pathogenesis of glioma at transcription level might be brought to light.
| S-EPMC4694224 | biostudies-literature
Project description:Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.
| S-EPMC2584353 | biostudies-literature
Project description:BACKGROUND: Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility. RESULTS: We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated t statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra- and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra- and inter-platform reproducibility of WAD was also observed at a higher biological function level. CONCLUSION: These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.
| S-EPMC3128035 | biostudies-literature
Project description:BackgroundIdentification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip(R) system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset.ResultsWe introduce a new fold-change (FC)-based method, the weighted average difference method (WAD), for ranking DEGs. It uses the average difference and relative average signal intensity so that highly expressed genes are highly ranked on the average for the different conditions. The idea is based on our observation that known or potential marker genes (or proteins) tend to have high expression levels. We compared WAD with seven other methods; average difference (AD), FC, rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT). The evaluation was performed using a total of 38 different binary (two-class) probe-level datasets: two artificial "spike-in" datasets and 36 real experimental datasets. The results indicate that WAD outperforms the other methods when sensitivity and specificity are considered simultaneously: the area under the receiver operating characteristic curve for WAD was the highest on average for the 38 datasets. The gene ranking for WAD was also the most consistent when subsets of top-ranked genes produced from three different preprocessed data (MAS, RMA, and DFW) were compared. Overall, WAD performed the best for MAS-preprocessed data and the FC-based methods (AD, WAD, FC, or RP) performed well for RMA and DFW-preprocessed data.ConclusionWAD is a promising alternative to existing methods for ranking DEGs with two classes. Its high performance should increase researchers' confidence in microarray analyses.
| S-EPMC2464587 | biostudies-literature
Project description:BACKGROUND:Rotator cuff tear (RCT) is a common shoulder disorder in the elderly. Muscle atrophy, denervation and fatty infiltration exert secondary injuries on torn rotator cuff muscles. It has been reported that satellite cells (SCs) play roles in pathogenic process and regenerative capacity of human RCT via regulating of target genes. This study aims to complement the differentially expressed genes (DEGs) of SCs that regulated between the torn supraspinatus (SSP) samples and intact subscapularis (SSC) samples, identify their functions and molecular pathways. METHODS:The gene expression profile GSE93661 was downloaded and bioinformatics analysis was made. RESULTS:Five hundred fifty one DEGs totally were identified. Among them, 272 DEGs were overexpressed, and the remaining 279 DEGs were underexpressed. Gene ontology (GO) and pathway enrichment analysis of target genes were performed. We furthermore identified some relevant core genes using gene-gene interaction network analysis such as GNG13, GCG, NOTCH1, BCL2, NMUR2, PMCH, FFAR1, AVPR2, GNA14, and KALRN, that may contribute to the understanding of the molecular mechanisms of secondary injuries in RCT. We also discovered that GNG13/calcium signaling pathway is highly correlated with the denervation atrophy pathological process of RCT. CONCLUSION:These genes and pathways provide a new perspective for revealing the underlying pathological mechanisms and therapy strategy of RCT.
| S-EPMC6234628 | biostudies-literature
Project description:The present study aimed to identify differentially expressed genes (DEGs) and biological processes (BPs) associated with asthma. DEGs between allergic asthma and healthy controls were screened from GSE15823. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was constructed, followed by module mining and functional analysis. Additionally, GSE41649 was downloaded to validate the reliability of the results. In GSE41649, DEGs were identified and compared with key DEGs identified in GSE15823. A total of 43 upregulated and 275 downregulated DEGs were obtained from GSE15823. Upregulated DEGs, such as nitric oxide synthase 2 (NOS2), were enriched in BPs related to oxidation reduction. Downregulated DEGs, such as chemokine (C-C motif) ligand 19 (CCL21) and Cys-X-Cys ligand (CXCL9), were enriched in immune response-related BPs. Protein tyrosine phosphatase receptor type C (PTPRC), CCL21, and CXCL9 were identified as hub genes. The DEGs in module 1 were significantly involved in the chemokine signaling pathway (P<0.05). The expression of the key genes obtained in GSE15823 demonstrated the same variation directions in the two datasets. The immune response, oxidants and nitric oxide metabolic pathways may have important roles in the progression of asthma. DEGs of PTPRC, CCL21, CXCL9 and NOS2 may be the potential targets for asthma diagnosis and treatment.
| S-EPMC6122392 | biostudies-literature
Project description:Pituitary adenomas, monoclonal in origin, are the most common intracranial neoplasms. Altered gene expression as well as somatic mutations is detected frequently in pituitary adenomas. The purpose of this study was to detect differentially expressed genes (DEGs) and biological processes during tumor formation of pituitary adenomas. We performed an integrated analysis of publicly available GEO datasets of pituitary adenomas to identify DEGs between pituitary adenomas and normal control (NC) tissues. Gene function analysis including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) networks analysis was conducted to interpret the biological role of those DEGs. In this study we detected 3994 DEGs (2043 upregulated and 1951 downregulated) in pituitary adenoma through an integrated analysis of 5 different microarray datasets. Gene function analysis revealed that the functions of those DEGs were highly correlated with the development of pituitary adenoma. This integrated analysis of microarray data identified some genes and pathways associated with pituitary adenoma, which may help to understand the pathology underlying pituitary adenoma and contribute to the successful identification of therapeutic targets for pituitary adenoma.
| S-EPMC4302352 | biostudies-literature
Project description:Identification of genes differentially expressed across multiple conditions has become an important statistical problem in analyzing large-scale microarray data. Many statistical methods have been developed to address the challenging problem. Therefore, an extensive comparison among these statistical methods is extremely important for experimental scientists to choose a valid method for their data analysis. In this study, we conducted simulation studies to compare six statistical methods: the Bonferroni (B-) procedure, the Benjamini and Hochberg (BH-) procedure, the Local false discovery rate (Localfdr) method, the Optimal Discovery Procedure (ODP), the Ranking Analysis of F-statistics (RAF), and the Significant Analysis of Microarray data (SAM) in identifying differentially expressed genes. We demonstrated that the strength of treatment effect, the sample size, proportion of differentially expressed genes and variance of gene expression will significantly affect the performance of different methods. The simulated results show that ODP exhibits an extremely high power in indentifying differentially expressed genes, but significantly underestimates the False Discovery Rate (FDR) in all different data scenarios. The SAM has poor performance when the sample size is small, but is among the best-performing methods when the sample size is large. The B-procedure is stringent and thus has a low power in all data scenarios. Localfdr and RAF show comparable statistical behaviors with the BH-procedure with favorable power and conservativeness of FDR estimation. RAF performs the best when proportion of differentially expressed genes is small and treatment effect is weak, but Localfdr is better than RAF when proportion of differentially expressed genes is large.
| S-EPMC3280440 | biostudies-literature
Project description:BackgroundOligonucleotide microarrays measure the relative transcript abundance of thousands of mRNAs in parallel. A large number of procedures for normalization and detection of differentially expressed genes have been proposed. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined.ResultsWe have employed four different normalization methods and all possible combinations with three different statistical algorithms for detection of differentially expressed genes on a prototype dataset. The number of genes detected as differentially expressed differs by a factor of about three. Analysis of lists of genes detected as differentially expressed, and rank correlation coefficients for probability of differential expression shows that a high concordance between different methods can only be achieved by using the same normalization procedure.ConclusionsNormalization has a profound influence of detection of differentially expressed genes. This influence is higher than that of three subsequent statistical analysis procedures examined. Algorithms incorporating more array-derived information than gene-expression values alone are urgently needed.
| S-EPMC126238 | biostudies-literature