A nitty-gritty aspect of correlation and network inference from gene expression data

Background  

All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels.     

Power and sample size estimation in microarray studies

Background  

Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (π ₁) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes.     

High-throughput detection of mutations responsible for childhood hearing loss using resequencing microarrays

Background  

Despite current knowledge of mutations in 45 genes that can cause nonsyndromic sensorineural hearing loss (SNHL), no unified clinical test has been developed that can comprehensively detect mutations in multiple genes. We therefore designed Affymetrix resequencing microarrays capable of resequencing 13 genes mutated in SNHL (GJB2, GJB6, CDH23, KCNE1, KCNQ1, MYO7A, OTOF, PDS, MYO6, SLC26A5, TMIE, TMPRSS3, USH1C). We present results from hearing loss arrays developed in two different research facilities and highlight some of the approaches we adopted to enhance the applicability of resequencing arrays in a clinical setting.     

A putative RNA-interference-based immune system in prokaryotes: computational analysis of the predicted enzymatic machinery, functional analogies with eukaryotic RNAi, and hypothetical mechanisms of action

Background  

All archaeal and many bacterial genomes contain Clustered Regularly Interspaced Short Palindrome Repeats (CRISPR) and variable arrays of the CRISPR-associated (cas) genes that have been previously implicated in a novel form of DNA repair on the basis of comparative analysis of their protein product sequences. However, the proximity of CRISPR and cas genes strongly suggests that they have related functions which is hard to reconcile with the repair hypothesis.     

Meta-analysis of breast cancer microarray studies in conjunction with conserved <Emphasis Type="Italic">cis</Emphasis>-elements suggest patterns for coordinate regulation

Background  

Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays.     

A verification protocol for the probe sequences of Affymetrix genome arrays reveals high probe accuracy for studies in mouse,human and rat

Background  

The Affymetrix GeneChip technology uses multiple probes per gene to measure its expression level. Individual probe signals can vary widely, which hampers proper interpretation. This variation can be caused by probes that do not properly match their target gene or that match multiple genes. To determine the accuracy of Affymetrix arrays, we developed an extensive verification protocol, for mouse arrays incorporating the NCBI RefSeq, NCBI UniGene Unique, NIA Mouse Gene Index, and UCSC mouse genome databases.     

Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios

Background  

Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples.     

High resolution discovery and confirmation of copy number variants in 90 Yoruba Nigerians

Background  

Copy number variants (CNVs) account for a large proportion of genetic variation in the genome. The initial discoveries of long (> 100 kb) CNVs in normal healthy individuals were made on BAC arrays and low resolution oligonucleotide arrays. Subsequent studies that used higher resolution microarrays and SNP genotyping arrays detected the presence of large numbers of CNVs that are < 100 kb, with median lengths of approximately 10 kb. More recently, whole genome sequencing of individuals has revealed an abundance of shorter CNVs with lengths < 1 kb.     

Refining gene signatures: a Bayesian approach

Background  

In high density arrays, the identification of relevant genes for disease classification is complicated by not only the curse of dimensionality but also the highly correlated nature of the array data. In this paper, we are interested in the question of how many and which genes should be selected for a disease class prediction. Our work consists of a Bayesian supervised statistical learning approach to refine gene signatures with a regularization which penalizes for the correlation between the variables selected.     

TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays

Background  

High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.     

Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

Background  

To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.     

Selective maintenance of Drosophila tandemly arranged duplicated genes during evolution

Background

The physical organization and chromosomal localization of genes within genomes is known to play an important role in their function. Most genes arise by duplication and move along the genome by random shuffling of DNA segments. Higher order structuring of the genome occurs in eukaryotes, where groups of physically linked genes are co-expressed. However, the contribution of gene duplication to gene order has not been analyzed in detail, as it is believed that co-expression due to recent duplicates would obscure other domains of co-expression.

Results

We have catalogued ordered duplicated genes in Drosophila melanogaster, and found that one in five of all genes is organized as tandem arrays. Furthermore, among arrays that have been spatially conserved over longer periods than would be expected on the basis of random shuffling, a disproportionate number contain genes encoding developmental regulators. Using in situ gene expression data for more than half of the Drosophila genome, we find that genes in these conserved clusters are co-expressed to a much higher extent than other duplicated genes.

Conclusions

These results reveal the existence of functional constraints in insects that retain copies of genes encoding developmental and regulatory proteins as neighbors, allowing their co-expression. This co-expression may be the result of shared cis-regulatory elements or a shared need for a specific chromatin structure. Our results highlight the association between genome architecture and the gene regulatory networks involved in the construction of the body plan.     

A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array

Background  

DNA copy number aberration (CNA) is one of the key characteristics of cancer cells. Recent studies demonstrated the feasibility of utilizing high density single nucleotide polymorphism (SNP) genotyping arrays to detect CNA. Compared with the two-color array-based comparative genomic hybridization (array-CGH), the SNP arrays offer much higher probe density and lower signal-to-noise ratio at the single SNP level. To accurately identify small segments of CNA from SNP array data, segmentation methods that are sensitive to CNA while resistant to noise are required.     

Singular value decomposition-based regression identifies activation of endogenous signaling pathways in vivo

Background

The physical organization and chromosomal localization of genes within genomes is known to play an important role in their function. Most genes arise by duplication and move along the genome by random shuffling of DNA segments. Higher order structuring of the genome occurs in eukaryotes, where groups of physically linked genes are co-expressed. However, the contribution of gene duplication to gene order has not been analyzed in detail, as it is believed that co-expression due to recent duplicates would obscure other domains of co-expression.

Results

We have catalogued ordered duplicated genes in Drosophila melanogaster, and found that one in five of all genes is organized as tandem arrays. Furthermore, among arrays that have been spatially conserved over longer periods than would be expected on the basis of random shuffling, a disproportionate number contain genes encoding developmental regulators. Using in situ gene expression data for more than half of the Drosophila genome, we find that genes in these conserved clusters are co-expressed to a much higher extent than other duplicated genes.

Conclusions

These results reveal the existence of functional constraints in insects that retain copies of genes encoding developmental and regulatory proteins as neighbors, allowing their co-expression. This co-expression may be the result of shared cis-regulatory elements or a shared need for a specific chromatin structure. Our results highlight the association between genome architecture and the gene regulatory networks involved in the construction of the body plan.