Automated multidimensional phenotypic profiling using large public microarray repositories

Abstract

Phenotypes are complex, and difficult to quantify in a highthroughput fashion. The lack of comprehensive phenotype data can prevent or distort genotype-phenotype mapping. Here, we describe ''PhenoProfiler,'' a computational method that enables in silico phenotype profiling. Drawing on the principle that similar gene expression patterns are likely to be associated with similar phenotype patterns, PhenoProfiler supplements the missing quantitative phenotype information for a given microarray dataset based on other well-characterized microarray datasets. We applied our method to 587 human microarray datasets covering >14,000 samples, and confirmed that the predicted phenotype profiles are highly consistent with true phenotype descriptions. PhenoProfiler offers several unique capabilities: (i) automated, multidimensional phenotype profiling, facilitating the analysis and treatment design of complex diseases; (ii) the extrapolation of phenotype profiles beyond provided classes; and (iii) the detection of confounding phenotype factors that could otherwise bias biological inferences. Finally, because no direct comparisons are made between gene expression values from different datasets, the method can use the entire body of cross-platform microarray data. This work has produced a compendium of phenotype profiles for the National Center for Biotechnology Information GEO datasets, which can facilitate an unbiased understanding of the transcriptome-phenome mapping. The continued accumulation of microarray data will further increase the power of PhenoProfiler, by increasing the variety and the quality of phenotypes to be profiled.genotype-phenotype association ͉ phenotype prediction ͉ phenotype profiling

Gesture Facilitates the Syntactic Analysis of Speech

“…On the basis of such production data, Kelly et al (2010b) have recently put forward the idea that gesture and speech show a similar obligatory coupling during comprehension. In their integrated-systems hypothesis, they state that “gesture and speech mutually and obligatorily interact with one another to enhance language comprehension; that is, gesture influences the processing of speech, speech influences the processing of gesture, and this integration is mandatory.” However, so far the integrated-systems hypothesis only applied to the semantic–conceptual level (Xu et al, 2009). The additional information present in gesture has been shown to facilitate language comprehension (Holle and Gunter, 2007; Holle et al, 2010; Wu and Coulson, 2010) and a semantically incongruent gesture–speech pairing interferes with gesture (Kelly et al, 2010b) as well as with language processing (Özyürek et al, 2007; Kelly et al, 2010a,b).…”

Gesture’s Neural Language

“…For example, bilateral MTGp activity is stronger when people see metaphoric (Kircher et al, 2009) or iconic (Green et al, 2009; Willems et al, 2009) gestures than when they see either speech or gestures alone. In response to emblems, MTGp activity has been found in each the left (Lui et al, 2008; Villarreal et al, 2008) and right (Nakamura et al, 2004) hemispheres, as well as bilaterally (Lotze et al, 2006; Xu et al, 2009). Lesion studies have also corroborated this area’s importance in recognizing an action’s meaning (Kalenine et al, 2010).…”

“…However, to characterize complex, integrative functions by one-to-one alliances with individual regions, without also acknowledging those neural mechanisms that might enable relationships among particular regions, loses sight of the brain’s dynamic and interconnected nature. For example, the same brain areas may exhibit activity in different tasks or in response to similar information from different mediums (e.g., each symbolic gestures and spoken language; Xu et al, 2009). Similarly, the brain can exhibit distributed function that is evoked by presentation from the same medium (e.g., co-speech gestures).…”

Translation of disease associated gene signatures across tissues

“…Molecular biomarkers for a disease are often investigated by profiling the disease-affected tissue (Alon et al, 1999;Dudoit et al, 2002;Ross et al, 2008). Recent developments in microarray experiments include; multiple outcomes (such as subtype classification and survival prediction, Cai et al, 2010), analysis across multiple experiments/datasets (Xu et al, 2009) and profiling of an alternative tissue, when the disease-related tissue is not readily available or the alternative tissue is more easily accessible. Achiron and Gurevich (2006) performed a microarray experiment on Peripheral Blood Mononuclear Cells (PBMC) as an alternative to brain tissue to investigate gene signatures for multiple sclerosis, a chronic inflammatory demyelinating autoimmune disease affecting the central nervous system.…”