Towards the uniform distribution of null P values on Affymetrix microarrays

Estimating the P value from the overall distribution of scores on the microarray can produce P values that are much closer to a uniform distribution

Background correction using dinucleotide affinities improves the performance of GCRMA

BACKGROUND: High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly. RESULTS: We introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R(2)) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets. CONCLUSION: Modifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing

The Gut-Brain Axis in Healthy Females: Lack of Significant Association between Microbial Composition and Diversity with Psychiatric Measures

This study examined associations between the composition and diversity of the intestinal microbiota and measures of depression, anxiety, eating disorder psychopathology, stress, and personality in a group of healthy adult females

Highlights of the 2012 Research Workshop: Using Nutrigenomics and Metabolomics in Clinical Nutrition Research

No Abstract

Determining gene expression on a single pair of microarrays

<p>Abstract</p> <p>Background</p> <p>In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments.</p> <p>Results</p> <p>PINC treats each pair of probes within a probeset as an independent measure of gene expression using the Bayesian framework of the Cyber-T algorithm and then assigns a corrected p-value for each gene comparison.</p> <p>The p-values generated by PINC accurately control False Discovery rate on Affymetrix control data sets, but are small enough that family-wise error rates (such as the Holm's step down method) can be used as a conservative alternative to false discovery rate with little loss of sensitivity on control data sets.</p> <p>Conclusion</p> <p>PINC outperforms previously published methods for determining differentially expressed genes when comparing Affymetrix microarrays with N = 1 in each condition. When applied to biological samples, PINC can be used to assess the degree of variability observed among biological replicates in addition to analyzing isolated pairs of microarrays.</p

Stochastic changes over time and not founder effects drive cage effects in microbial community assembly in a mouse model

Maternal transmission and cage effects are powerful confounding factors in microbiome studies. To assess the consequences of cage microenvironment on the mouse gut microbiome, two groups of germ-free (GF) wild-type (WT) mice, one gavaged with a microbiota harvested from adult WT mice and another allowed to acquire the microbiome from the cage microenvironment, were monitored using Illumina 16S rRNA sequencing over a period of 8 weeks. Our results revealed that cage effects in WT mice moved from GF to specific pathogen free (SPF) conditions take several weeks to develop and are not eliminated by the initial gavage treatment. Initial gavage influenced, but did not eliminate a successional pattern in which Proteobacteria became less abundant over time. An analysis in which 16S rRNA sequences are mapped to the closest sequenced whole genome suggests that the functional potential of microbial genomes changes significantly over time shifting from an emphasis on pathogenesis and motility early in community assembly to metabolic processes at later time points. Functionally, mice allowed to naturally acquire a microbial community from their cage, but not mice gavaged with a common biome, exhibit a cage effect in Dextran Sulfate Sodium-induced inflammation. Our results argue that while there are long-term effects of the founding community, these effects are mitigated by cage microenvironment and successional community assembly over time, which must both be explicitly considered in the interpretation of microbiome mouse experiments