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Pavlidis, Paul — 2019-03-12 Methods are presented for detecting differential expression using statistical hypothesis testing methods including analysis of variance (ANOVA). Practicalities of experimental design, power, and sample size are discussed. Methods for multiple testing correction and their application are described. Instructions for running typical analyses are given in the R programming environment. R code and the sample data set used to generate the examples are available at http://microarray.cpmc.columbia.edu/pavlidis/pub/aovmethods/.

Pavlidis, Paul; Noble, William S. — 2019-03-12 <p>BACKGROUND:<br> We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression.<p> <p>RESULTS:<br> Our analysis revealed many additional genes that might be involved in behavioral differences between the two mouse strains and functional differences between the six brain regions. Using conservative statistical criteria, we identified at least 63 genes showing strain variation and approximately 600 genes showing regional variation. Unlike ad hoc methods, ours have the additional benefit of ranking the genes by statistical score, permitting further analysis to focus on the most significant. Comparison of our results to the previous studies and to published reports on individual genes show that we achieved high sensitivity while preserving selectivity.<p> <p>CONCLUSIONS:<br> Our results indicate that molecular differences between the strains and regions studied are larger than indicated previously. We conclude that for large complex datasets, ANOVA and feature selection, alone or in combination, are more powerful than methods based on fold-change thresholds and other ad hoc selection criteria.<p>

Rogic, Sanja; Pavlidis, Paul — 2019-03-12 Numerous studies have been performed to examine gene expression patterns in the rodent hippocampus in the kindling model of epilepsy. However, recent reviews of this literature have revealed limited agreement among studies. Because this conclusion was based on retrospective comparison of reported "hit lists" from individual studies, we hypothesized that re-analysis of the original expression data would help address this concern. In this paper, we reanalyzed four genome-wide expression studies of excitotoxin-induced kindling in rat and performed a statistical meta-analysis. The meta-analysis revealed over 800 genes which show significant change in expression 24 h after initial seizure induction, and 59 genes altered after 10 days. To evaluate our results in light of previous work, we assembled a reference list of genes formed from a consensus of the published literature. Our profiles include most of the genes in this reference list, and most of the additional genes are from pathways or biological processes previously recognized to be altered in kindling. In addition our results emphasized expression changes in lipid metabolism and protein degradation pathways. We conclude that a cautious re-analysis of published expression data can help illuminate genes and pathways underling kindling. Supplementary Material is available at http://www.chibi.ubc.ca/faculty/pavlidis/meta-analysis-of-brain-kindling/.

Tebaykin, Dmitry; Tripathy, Shreejoy J.; Binnion, Nathalie; Li, Brenna; Gerkin, Richard C.; Pavlidis, Paul — 2019-03-05 Patch-clamp electrophysiology is widely used to characterize neuronal electrical phenotypes. However, there are no standard experimental conditions for in vitro whole-cell patch-clamp electrophysiology, complicating direct comparisons between datasets. Here, we sought to understand how basic experimental conditions differ among labs and how these differences might impact measurements of electrophysiological parameters. We curated the compositions of external bath solutions (ACSF), internal pipette solutions, and other methodological details such as animal strain and age from 509 published neurophysiology articles studying rodent neurons. We found that very few articles used the exact same experimental solutions as any other and some solution differences stem from recipe inheritance from adviser to advisee as well as changing trends over the years. Next, we used statistical models to understand how the use of different experimental conditions impacts downstream electrophysiological measurements such as resting potential and action potential width. While these experimental condition features could explain up to 43% of the study-to-study variance in electrophysiological parameters, the majority of the variability was left unexplained. Our results suggest that there are likely additional experimental factors that contribute to cross-laboratory electrophysiological variability, and identifying and addressing these will be important to future efforts to assemble consensus descriptions of neurophysiological phenotypes for mammalian cell types.

Mancarci, B. Ogan; Toker, Lilah; Tripathy, Shreejoy J; Li, Brenna; Rocco, Brad; Sibille, Etienne; Pavlidis, Paul — 2019-03-05 Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single cell RNA-sequencing studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at Neuroexpresso.org.

Gillis, Jesse; Pavlidis, Paul — 2019-03-05 Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.

Mistry, Meeta; Gillis, Jesse; Pavlidis, Paul — 2019-03-05 Numerous studies have examined gene expression profiles in post-mortem human brain samples from individuals with schizophrenia compared to healthy controls, to gain insight into the molecular mechanisms of the disease. While some findings have been replicated across studies,there is a general lack of consensus of which genes or pathways are affected. It has been unclear if these differences are due to the underlying cohorts, or methodological considerations. Here we present the most comprehensive analysis to date of expression patterns in the prefrontal cortex of schizophrenic compared to unaffected controls. Using data from seven independent studies, we assembled a data set of 153 affected and 153 control individuals. Remarkably, we identified expression differences in the brains of schizophrenics that are validated by up to seven laboratories using independent cohorts. Our combined analysis revealed a signature of 39 probes that are up-regulated in schizophrenia and 86 down-regulated. Some of these genes were previously identified in studies that were not included in our analysis, while others are novel to our analysis. In particular, we observe gene expression changes associated with various aspects of neuronal communication, and alterations of processes affected as a consequence of changes in synaptic functioning. A gene network analysis predicted previously unidentified functional relationships among the signature genes. Our results provide evidence for a common underlying expression signature in this heterogeneous disorder.

French, Leon; Pavlidis, Paul — 2019-03-05 The electronic linking of neuroscience information, including data embedded in the primary literature, would permit powerful queries and analyses driven by structured databases. This task would be facilitated by automated procedures that can identify biological concepts in journals. Here we apply an approach for automatically mapping formal identifiers of neuroanatomical regions to text found in journal abstracts, applying it to a large body of abstracts from the Journal of Comparative Neurology (JCN). The analyses yield over 100,000 brain region mentions, which we map to 8,225 brain region concepts in multiple organisms. Based on the analysis of a manually annotated corpus, we estimate mentions are mapped at 95% precision and 63% recall. Our results provide insights into the patterns of publication on brain regions and species of study in JCN but also point to important challenges in the standardization of neuroanatomical nomenclatures. We find that many terms in the formal terminologies never appear in a JCN abstract, and, conversely, many terms that authors use are not reflected in the terminologies. To improve the terminologies, we deposited 136 unrecognized brain regions into the Neuroscience Lexicon (NeuroLex). The training data, terminologies, normalizations, evaluations, and annotated journal abstracts are freely available at http://www.chibi.ubc.ca/WhiteText/.

Jacobson, Matthew; Sedeño-Cortés, Adriana Estela; Pavlidis, Paul — 2019-03-11 Data and analysis of Gene Ontology annotations, to support reproducibility of results presented in the above cited preprint. There are two major parts to the data. The first is an analysis of the contents of the database supporting https://gotrack.msl.ubc.ca/ and represents direct downloads of files from that site at the time of our analysis. The second, concerning the analysis of the effects of changes in GO over time on enrichment analysis, includes python scripts and intermediate data and analysis files.

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 <p>MOTIVATION:<br> Gene networks have been used widely in gene function prediction algorithms, many based on complex extensions of the 'guilt by association' principle. We sought to provide a unified explanation for the performance of gene function prediction algorithms in exploiting network structure and thereby simplify future analysis.<p> <p>RESULTS:<br> We use co-expression networks to show that most exploited network structure simply reconstructs the original correlation matrices from which the co-expression network was obtained. We show the same principle works in predicting gene function in protein interaction networks and that these methods perform comparably to much more sophisticated gene function prediction algorithms.<p> <p>AVAILABILITY AND IMPLEMENTATION:<br> Data and algorithm implementation are fully described and available at http://www.chibi.ubc.ca/extended. Programs are provided in Matlab m-code.<p> <p>CONTACT:<br> paul@chibi.ubc.ca<p>

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 Many previous studies have shown that by using variants of "guilt-by-association", gene function predictions can be made with very high statistical confidence. In these studies, it is assumed that the "associations" in the data (e.g., protein interaction partners) of a gene are necessary in establishing "guilt". In this paper we show that multifunctionality, rather than association, is a primary driver of gene function prediction. We first show that knowledge of the degree of multifunctionality alone can produce astonishingly strong performance when used as a predictor of gene function. We then demonstrate how multifunctionality is encoded in gene interaction data (such as protein interactions and coexpression networks) and how this can feed forward into gene function prediction algorithms. We find that high-quality gene function predictions can be made using data that possesses no information on which gene interacts with which. By examining a wide range of networks from mouse, human and yeast, as well as multiple prediction methods and evaluation metrics, we provide evidence that this problem is pervasive and does not reflect the failings of any particular algorithm or data type. We propose computational controls that can be used to provide more meaningful control when estimating gene function prediction performance. We suggest that this source of bias due to multifunctionality is important to control for, with widespread implications for the interpretation of genomics studies.

French, Leon; Tan, Powell Patrick Cheng; Pavlidis, Paul — 2019-03-11 Recent research in C. elegans and the rodent has identified correlations between gene expression and connectivity. Here we extend this type of approach to examine complex patterns of gene expression in the rodent brain in the context of regional brain connectivity and differences in cellular populations. Using multiple large-scale data sets obtained from public sources, we identified two novel patterns of mouse brain gene expression showing a strong degree of anti-correlation, and relate this to multiple data modalities including macroscale connectivity. We found that these signatures are associated with differences in expression of neuronal and oligodendrocyte markers, suggesting they reflect regional differences in cellular populations. We also find that the expression level of these genes is correlated with connectivity degree, with regions expressing the neuron-enriched pattern having more incoming and outgoing connections with other regions. Our results exemplify what is possible when increasingly detailed large-scale cell- and gene-level data sets are integrated with connectivity data.

Mistry, Meeta; Pavlidis, Paul — 2019-03-11 Expression profiling of post-mortem human brain tissue has been widely used to study molecular changes associated with neuropsychiatric diseases as well as normal processes such as aging. Changes in expression associated with factors such as age, gender or postmortem interval are often more pronounced than changes associated with disease. Therefore in addition to being of interest in their own right, careful consideration of these effects are important in the interpretation of disease studies. We performed a large meta-analysis of genome-wide expression studies of normal human cortex to more fully catalogue the effects of age, gender, postmortem interval and brain pH, yielding a "meta-signature" of gene expression changes for each factor. We validated our results by showing a significant overlap with independent gene lists extracted from the literature. Importantly, meta-analysis identifies genes which are not significant in any individual study. Finally, we show that many schizophrenia candidate genes appear in the meta-signatures, reinforcing the idea that studies must be carefully controlled for interactions between these factors and disease. In addition to the inherent value of the meta-signatures, our results provide critical information for future studies of disease effects in the human brain.

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 <p>BACKGROUND: Differential coexpression is a change in coexpression between genes that may reflect 'rewiring' of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time) and extensively test its validity and usefulness.<p> <p>RESULTS: Our method is based on the use of the Haar basis set to efficiently represent changes in coexpression at multiple time scales, and thus represents a principled and generalizable extension of the idea of differential coexpression to life stage data. We used published microarray studies categorized by age to test the methodology. We validated the methodology by testing our ability to reconstruct Gene Ontology (GO) categories using our measure of differential coexpression and compared this result to using coexpression alone. Our method allows significant improvement in characterizing these groups of genes. Further, we examine the statistical properties of our measure of differential coexpression and establish that the results are significant both statistically and by an improvement in semantic similarity. In addition, we found that our method finds more significant changes in gene relationships compared to several other methods of expressing temporal relationships between genes, such as coexpression over time.<p> <p>CONCLUSION: Differential coexpression over age generates significant and biologically relevant information about the genes producing it. Our Haar basis methodology for determining age-related differential coexpression performs better than other tested methods. The Haar basis set also lends itself to ready interpretation in terms of both evolutionary and physiological mechanisms of aging and can be seen as a natural generalization of two-category differential coexpression.<p> <p>CONTACT: paul@bioinformatics.ubc.ca.<p>

Bhuiyan, Shamsuddin; Ly, Sophia; Phan, Minh; Huntington, Brandon; Hogan, Ellie; Liu, Chao Chun; Liu, James; Pavlidis, Paul — 2019-03-11 Systematic evaluation of splice isoform function to determine genes with functionally distinct splice isoforms based on experimental evidence. Framework and analysis of curation provided in the above preprint. The data are provided in two parts. First part is the PubMed IDs of all curated studies and their annotations based on our curation framework. Second, are the genes with literature evidence of functionally distinct splice isoforms, and further notes about functional distinctness.

Wan, Xian; Pavlidis, Paul — 2019-03-12 As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.

Barnes, Michael; Freudenberg, Johannes; Thompson, Susan; Aronow, Bruce; Pavlidis, Paul — 2019-03-12 The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms.

Verbitsky, Miguel; Yonan, Amanda L.; Malleret, Gael; Kandel, Eric R; Gilliam, T. Conrad; Pavlidis, Paul — 2019-03-12 We have carried out a global survey of age-related changes in mRNA levels in the C57BL/6NIA mouse hippocampus and found a difference in the hippocampal gene expression profile between 2-month-old young mice and 15-month-old middle-aged mice correlated with an age-related cognitive deficit in hippocampal-based explicit memory formation. Middle-aged mice displayed a mild but specific deficit in spatial memory in the Morris water maze. By using Affymetrix GeneChip microarrays, we found a distinct pattern of age-related change, consisting mostly of gene overexpression in the middle-aged mice, suggesting that the induction of negative regulators in the middle-aged hippocampus could be involved in impairment of learning. Interestingly, we report changes in transcript levels for genes that could affect synaptic plasticity. Those changes could be involved in the memory deficits we observed in the 15-month-old mice. In agreement with previous reports, we also found altered expression in genes related to inflammation, protein processing, and oxidative stress.

Fortelny, Nikolaus; Pavlidis, Paul; Butler, Georgina; Overall, Christopher — 2019-03-05 Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features. Thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of non-proteolytic and non-inhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task, and thereby highlight limitations of computational interaction prediction methods.

Melka, Melkaye G.; Pavlidis, Paul; Gillis, Jesse; Bernard, Manon; Abrahamowicz, Michal; Chakravarty, M. Maller; Leonard, Gabriel T.; Perron, Michel; Richer, Louis; Veillette, Suzanne; Banaschewski, Tobias; Barker, Gareth J.; Buchel, Christian; Conrod, Patricia; Flor, Herta; Heinz, Andreas; Garavan, Hugh; Bruhl, Rudiger; Mann, Karl; Artiges, Eric; Lourdusamy, Anbarasu; Lathrop, Mark; Loth, Eva; Schwartz, Yannick; Frouin, Vincent; Rietschel, Marcella; Smolka, Michael N.; Strohle, Andreas; Gallinat, Jurge; Struve, Maren; Lattka, Eva; Waldenberger, Melanie; Schumann, Gunter; Gaudet, Daniel; Paus, Tomas; Pausova, Zdenka — 2019-03-05 Genetic variations in fat mass- and obesity (FTO)-associated gene, a well-replicated gene locus of obesity, appear to be associated also with reduced regional brain volumes in elderly. Here, we examined whether FTO is associated with total brain volume in adolescence, thus exploring possible developmental effects of FTO. We studied a population-based sample of 598 adolescents recruited from the French Canadian founder population in whom we measured brain volume by magnetic resonance imaging. Total fat mass was assessed with bioimpedance and body mass index was determined with anthropometry. Genotype-phenotype associations were tested with Merlin under an additive model. We found that the G allele of FTO (rs9930333) was associated with higher total body fat [TBF (P = 0.002) and lower brain volume (P = 0.005)]. The same allele was also associated with higher lean body mass (P = 0.03) and no difference in height (P = 0.99). Principal component analysis identified a shared inverse variance between the brain volume and TBF, which was associated with FTO at P = 5.5 × 10(-6). These results were replicated in two independent samples of 413 and 718 adolescents, and in a meta-analysis of all three samples (n = 1729 adolescents), FTO was associated with this shared inverse variance at P = 1.3 × 10(-9). Co-expression networks analysis supported the possibility that the underlying FTO effects may occur during embryogenesis. In conclusion, FTO is associated with shared inverse variance between body adiposity and brain volume, suggesting that this gene may exert inverse effects on adipose and brain tissues. Given the completion of the overall brain growth in early childhood, these effects may have their origins during early development.