Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays

Literature aided determination of data quality and statistical significance threshold for gene expression studies

Background

Gene expression data are noisy due to technical and biological variability. Consequently, analysis of gene expression data is complex. Different statistical methods produce distinct sets of genes. In addition, selection of expression p-value (EPv) threshold is somewhat arbitrary. In this study, we aimed to develop novel literature based approaches to integrate functional information in analysis of gene expression data.

Methods

Functional relationships between genes were derived by Latent Semantic Indexing (LSI) of Medline abstracts and used to calculate the function cohesion of gene sets. In this study, literature cohesion was applied in two ways. First, Literature-Based Functional Significance (LBFS) method was developed to calculate a p-value for the cohesion of differentially expressed genes (DEGs) in order to objectively evaluate the overall biological significance of the gene expression experiments. Second, Literature Aided Statistical Significance Threshold (LASST) was developed to determine the appropriate expression p-value threshold for a given experiment.

Results

We tested our methods on three different publicly available datasets. LBFS analysis demonstrated that only two experiments were significantly cohesive. For each experiment, we also compared the LBFS values of DEGs generated by four different statistical methods. We found that some statistical tests produced more functionally cohesive gene sets than others. However, no statistical test was consistently better for all experiments. This reemphasizes that a statistical test must be carefully selected for each expression study. Moreover, LASST analysis demonstrated that the expression p-value thresholds for some experiments were considerably lower (p < 0.02 and 0.01), suggesting that the arbitrary p-values and false discovery rate thresholds that are commonly used in expression studies may not be biologically sound.

Conclusions

We have developed robust and objective literature-based methods to evaluate the biological support for gene expression experiments and to determine the appropriate statistical significance threshold. These methods will assist investigators to more efficiently extract biologically meaningful insights from high throughput gene expression experiments.

Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays

Probe defects are a major source of noise in gene expression studies. While existing approaches detect noisy probes based on external information such as genomic alignments, we introduce and validate a targeted probabilistic method for analyzing probe reliability directly from expression data and independently of the noise source. This provides insights into the various sources of probe-level noise and gives tools to guide probe design.

Conditional deletion of the Lkb1 gene in the mouse mammary gland induces tumour formation

Heterozygous germline mutations in the LKB1 (STK11) gene cause Peutz-Jeghers syndrome (PJS), an autosomal dominant disorder characterized by hamartomatous polyposis of the gastrointestinal tract and an increased risk of colorectal, breast, and other cancers. To model the role of LKB1 mutation in mammary tumourigenesis, we have used a conditional gene targeting strategy to generate a mouse in which exons encoding the kinase domain of Lkb1 were deleted specifically in the mammary gland. Mammary gland tumours developed in these mice with a latency of 46-85 weeks and occurred in the thoracic or inguinal glands. These tumours were grade 2 invasive ductal carcinomas or solid papillary carcinomas with histological features similar to those described in breast cancers arising in patients with PJS. This mouse model of Lkb1 deficiency provides a potentially useful tool to investigate the role of Lkb1 in tumourigenesis and to guide the development of therapeutic approaches.

Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies

A novel statistical procedure is presented that uses probe-level information on Affymetrix exon arrays to detect differential splicing.

The recent advent of exon microarrays has made it possible to reveal differences in alternative splicing events on a global scale. We introduce a novel statistical procedure that takes full advantage of the probe-level information on Affymetrix exon arrays when detecting differential splicing between sample groups. In comparison to existing ranking methods, the procedure shows superior reproducibility and accuracy in distinguishing true biological findings from background noise in high agreement with experimental validations.

Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms

Benzene is an established cause of leukemia, and possibly lymphoma, in humans, but the underlying molecular pathways remain largely undetermined. We used two microarray platforms to identify global gene expression changes associated with well-characterized occupational benzene exposure in the peripheral blood mononuclear cells (PBMC) of a population of shoe-factory workers. Differential expression of 2692 genes (Affymetrix) and 1828 genes (Illumina) was found and the concordance was 50% (based on an average fold-change > or =1.3 from the two platforms), with similar expression ratios among the concordant genes. Four genes (CXCL16, ZNF331, JUN and PF4), which we previously identified by microarray and confirmed by real-time PCR, were among the top 100 genes identified by both platforms in the current study. Gene ontology analysis showed overrepresentation of genes involved in apoptosis among the concordant genes while pathway analysis identified pathways related to lipid metabolism. The two-platform approach allows for robust changes in the PBMC transcriptome of benzene-exposed individuals to be identified.

Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

MOTIVATION

Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.

RESULTS

We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.