Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.     

Arabidopsis microarrays identify conserved and differentially expressed genes involved in shoot growth and development from distantly related plant species

Expressed sequence tags (EST)-based microarrays are powerful tools for gene discovery and signal transduction studies in a small number of well-characterized species. To explore the usefulness of this technique for poorly characterized species, we have hybridized the 11,522-element Arabidopsis microarrays with labeled cDNAs from mature leaf and shoot apices from several different species. Expression of 23 to 47% of the genes on the array was detected, demonstrating that a large number of genes from distantly related species can be surveyed on Arabidopsis arrays. Differential expression of genes with known functions was indicative of the physiological state of the tissues tested. Genes involved in cell division, stress responses, and development were conserved and expressed preferentially in growing shoots.     

Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

MOTIVATION: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. RESULTS: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to < 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT-PCR (qRT-PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. AVAILABILITY: The data presented can be viewed at http://www.ncbi.nlm.nih.gov/geo/ under GEO accession no. GSE3017.     

Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays

MOTIVATION: In microarray studies gene discovery based on fold-change values is often misleading because error variability for each gene is heterogeneous under different biological conditions and intensity ranges. Several statistical testing methods for differential gene expression have been suggested, but some of these approaches are underpowered and result in high false positive rates because within-gene variance estimates are based on a small number of replicated arrays. RESULTS: We propose to use local-pooled-error (LPE) estimates and robust statistical tests for evaluating significance of each gene's differential expression. Our LPE estimation is based on pooling errors within genes and between replicate arrays for genes in which expression values are similar. We have applied our LPE method to compare gene expression in na?ve and activated CD8+ T-cells. Our results show that the LPE method effectively identifies significant differential-expression patterns with a small number of replicated arrays. AVAILABILITY: The methodology is implemented with S-PLUS and R functions available at http://hesweb1.med.virginia.edu/bioinformatics     

A generalized likelihood ratio test to identify differentially expressed genes from microarray data

MOTIVATION: Microarray technology emerges as a powerful tool in life science. One major application of microarray technology is to identify differentially expressed genes under various conditions. Currently, the statistical methods to analyze microarray data are generally unsatisfactory, mainly due to the lack of understanding of the distribution and error structure of microarray data. RESULTS: We develop a generalized likelihood ratio (GLR) test based on the two-component model proposed by Rocke and Durbin to identify differentially expressed genes from microarray data. Simulation studies show that the GLR test is more powerful than commonly used methods, like the fold-change method and the two-sample t-test. When applied to microarray data, the GLR test identifies more differentially expressed genes than the t-test, has a lower false discovery rate and shows more consistency over independently repeated experiments. AVAILABILITY: The approach is implemented in software called GLR, which is freely available for downloading at http://www.cc.utah.edu/~jw27c60     

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

Background  

Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.     

Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes

Background  

In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one searches for differentially expressed genes, the small number of samples gives rise to an inaccurate estimation of the experimental noise. This, in turn, leads to loss of statistical power.     

Combining SSH and cDNA microarrays for rapid identification of differentially expressed genes

Comparing patterns of gene expression in cell lines and tissues has important applications in a variety of biological systems. In this study we have examined whether the emerging technology of cDNA microarrays will allow a high throughput analysis of expression of cDNA clones generated by suppression subtractive hybridization (SSH). A set of cDNA clones including 332 SSH inserts amplified by PCR was arrayed using robotic printing. The cDNA arrays were hybridized with fluorescent labeled probes prepared from RNA from ER-positive (MCF7 and T47D) and ER-negative (MDA-MB-231 and HBL-100) breast cancer cell lines. Ten clones were identified that were over-expressed by at least a factor of five in the ER-positive cell lines. Northern blot analysis confirmed over-expression of these 10 cDNAs. Sequence analysis identified four of these clones as cytokeratin 19, GATA-3, CD24 and glutathione-S-transferase mu-3. Of the remaining six cDNA clones, four clones matched EST sequences from two different genes and two clones were novel sequences. Flow cytometry and immunofluorescence confirmed that CD24 protein was over-expressed in the ER-positive cell lines. We conclude that SSH and microarray technology can be successfully applied to identify differentially expressed genes. This approach allowed the identification of differentially expressed genes without the need to obtain previously cloned cDNAs.     

A modified cDNA subtraction to identify differentially expressed genes from plants with universal application to other eukaryotes

We have designed a simple and efficient polymerase chain reaction (PCR)-based cDNA subtraction protocol for high-throughput cloning of differentially expressed genes from plants that can be applied to any experimental system and as an alternative to DNA chip technology. Sequence-independent PCR-amplifiable first-strand cDNA population was synthesized by priming oligo-dT primer with a defined 5' heel sequence and ligating another specified single-stranded oligonucleotide primer on the 3' ends of first-strand cDNAs by T4 RNA ligase. A biotin label was introduced into the sense strands of cDNA that must be subtracted by using 5' biotinylated forward primer during PCR amplification to immobilize the sense strand onto the streptavidin-linked paramagnetic beads. The unamplified first strand (antisense) of the interrogating cDNA population was hybridized with a large excess of amplified sense strands of control cDNA. We used magnetic bead technology for the efficient removal of common cDNA population after hybridization to reduce the complexity of the cDNA prior to PCR amplification for the enrichment and sequence abundance normalization of differentially expressed genes. Construction of a subtracted and normalized cDNA library efficiently eliminates common abundant cDNA messages and also increases the probability of identifying clones differentially expressed in low-abundance cDNA messages. We used this method to successfully isolate differentially expressed genes from Pennisetum seedlings in response to salinity stress. Sequence analysis of the selected clones showed homologies to genes that were reported previously and shown to be involved in plant stress adaptation.     

A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments

MOTIVATION: Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-gene-analysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed. RESULTS: We propose a method that can avoid the difficult task of estimating variability for each gene, while reliably identifying a group of differentially expressed genes with low false discovery rates, even when the fold-changes are very small. In this article, a new characterization of differentially expressed genes is established based on a theorem about the distribution of ranks of genes sorted by (log) ratios within each array. This characterization of differentially expressed genes based on rank is an example of all-gene-analysis instead of single gene analysis. We apply the method to a cDNA microarray dataset and many low fold-changed genes (as low as 1.3 fold-changes) are reliably identified without carrying out hypothesis testing on a gene-by-gene basis. The false discovery rate is estimated in two different ways reflecting the variability from all the genes without the complications related to multiple hypothesis testing. We also provide some comparisons between our approach and single-gene-analysis based methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Library subtraction of in vitro cDNA libraries to identify differentially expressed genes in scrapie infection.

We have developed a system where double-stranded cDNA can be amplified using a synthetic oligonucleotide primer and the polymerase chain reaction, generating cDNA libraries in vitro. Using a library subtraction strategy (1), scrapie and control brain in vitro cDNA libraries were used to identify sequences whose expression is modulated in scrapie infection. One of these sequences represents beta-2 microglobulin, while the other two have not been previously described. The use of in vitro libraries offers increased speed and efficiency of construction, and their subtraction is more efficient and powerful, compared with the previous system (1).     

Isolation of differentially expressed genes in human heart tissues

We applied RNA arbitrarily primed-PCR (RAP-PCR) to screen the genes differentially expressed between common congenital heart defects (CHD) [atrial septal defect, ventricular septal defect, Tetrology of Fallot (TOF)] and normal human heart samples. Three of these differentially amplified fragments matched cDNA sequences coding for proteins of unknown function in humans: hCALO (human homologue of calossin), NP79 (coding for a nuclear protein of 79KD) and SUN2 (Sad-1 unc-84 domain protein 2). The other four fragments were from known human genes: apolipoprotein J, titin, dystrophin and protein kinase C-delta. Northern blot analysis confirmed that all of these genes are expressed in the human heart. The results of RAP-PCR were reconfirmed by quantitative RT-PCR in TOF and control heart samples. Both techniques showed the levels of expression of hCALO, NP79 and SUN2 to be comparable in TOF and control samples and the level of expression of dystrophin and titin, both coding for cytoskeletal proteins, to be significantly upregulated in TOF samples. In summary, we have shown that the RAP-PCR technique is useful in the identification of differentially expressed gene from biopsy samples of human CHD tissues. In this manner, we have identified three novel genes implicated in the normal function of the human heart and two known genes upregulated in TOF samples.     

Identification of differentially expressed genes associated with 5-methyltryptophan resistance in rice mutants

5-Methyltryptophan (5MT), a tryptophan analog, resistant M4 rice mutants with high free amino acid contents were obtained through in vitro mutagenesis. To evaluate the 5MT resistance mechanism, a cDNA library was constructed by using the leaves and roots of the 5MT resistant mutant plants. Expressed sequenced tags (ESTs) of 1 019 randomly selected clones were analyzed and then assembled 588 unigens. A total of 389 unigenes had significant homologies with known protein sequences at the NCBI database and the remaining 199 unigenes were designated unidentified genes. These unigens were grouped into 13 categories according to their putative functions. Of the 233 randomly selected clones, 25 were identified as differentially expressed genes between 5MT resistant and 5MT sensitive wild type plants. For further study of the differential expression of the genes, expression patterns of 12 genes related to various biological functions were evaluated in response to 5MT treatment in both the resistant plants and sensitive plants. All of the tested 12 genes exhibited higher expression levels in mutant plants than wild type plants under the 5MT inhibition. These expression patterns of the 12 genes suggested that the genes related to 5MT resistance in the rice mutants have a variety of functions, and yield remarkably diverse expression patterns upon 5MT treatment. Many genes that were identified tend to be related to defense and stress responses, suggesting “cross-talking“ between biotic/abiotic stresses including the 5MT treatment. Therefore, 5MT resistant mutants might be of value for identifying genes related to plant defenses and stresses.     

FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

Background

Candidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs.

Results

We analyzed all human microarray studies from the Gene Expression Omnibus, and calculated the observed frequency of differential expression, which we called differential expression ratio, for every human gene. Analysis conducted in a comprehensive list of curated disease genes revealed a positive association between differential expression ratio values and the likelihood of harboring disease-associated variants. By considering highly differentially expressed genes, we were able to rediscover disease genes with 79% specificity and 37% sensitivity. We successfully distinguished true disease genes from false positives in multiple GWASs for multiple diseases. We then derived a list of functionally interpolating SNPs (fitSNPs) to analyze the top seven loci of Wellcome Trust Case Control Consortium type 1 diabetes mellitus GWASs, rediscovered all type 1 diabetes mellitus genes, and predicted a novel gene (KIAA1109) for an unexplained locus 4q27. We suggest that fitSNPs would work equally well for both Mendelian and complex diseases (being more effective for cancer) and proposed candidate genes to sequence for their association with 597 syndromes with unknown molecular basis.

Conclusions

Our study demonstrates that highly differentially expressed genes are more likely to harbor disease-associated DNA variants. FitSNPs can serve as an effective tool to systematically prioritize candidate SNPs from GWASs.