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Cell type marker enrichment across brain regions and experimental conditions Tan, Powell Patrick Cheng 2012

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Cell type marker enrichment across brain regions and experimental conditions by Powell Patrick Cheng Tan B. Sc. (Honours), Simon Fraser University, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in THE FACULTY OF GRADUATE STUDIES (Bioinformatics) The University Of British Columbia (Vancouver) November 2012 c© Powell Patrick Cheng Tan, 2012 Abstract The first chapter of this thesis explored the dominant gene expression pattern in the adult human brain. We discovered that the largest source of variation can be explained by cell type marker expression. Across brain regions, expression of neuron cell type markers are anti-correlated with the expression of oligodendrocyte cell type markers. Next, we explored gene function convergence and divergence in the adult mouse brain. Our contributions are as follows. First, we provide candidate cell type markers for investigating specific cell type populations. Second, we highlight orthologous genes that show functional divergence between human and mouse brains. In the second chapter, we present our preliminary work on the effects of tissue types and experimen- tal conditions on human microarray studies. First, we measured the expression and differential expression levels of tissue-enriched genes. Next, we identified modules with similar expression levels and differen- tial expression p-values. Our results show that expression levels reflect tissue type variation. In contrast, differential expression levels are more complex, owing to the large diversity of experimental conditions in the data. In summary, our work provides a different perspective on the functional roles of genes in human microarray studies. ii Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Neuron-enriched gene expression patterns are regionally anti-correlated with oligodendrocyte- enriched patterns in the adult mouse and human brain . . . . . . . . . . . . . . . . . . . . . 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Human brain gene expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Mouse brain gene expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.3 Human brain analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.4 Human-mouse comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.6 Additional data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.1 Neuron-enriched and oligodendrocyte-enriched patterns are conserved . . . . . . . . 5 1.3.2 Principal component loadings partly reflect varying cell-type proportions . . . . . . 6 1.3.3 Orthologous genes with positively correlated expression patterns are enriched in cell type markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 iii 2 Large-scale survey of tissue types and experimental conditions across datasets . . . . . . . . 18 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 Data overview and pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.2 Tissue-enriched genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.3 Biclustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.4 GO enrichment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.1 Experimental design and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.2 Tissue-enriched gene expression and differential expression . . . . . . . . . . . . . 21 2.3.3 Modules enriched for biological processes . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 iv List of Tables Table 1.1 Top 25 genes in the oligodendrocyte-enriched gene set of human H0351.2001 sorted by PC1 score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Table 1.2 Top 25 genes in the neuron-enriched gene set of human H0351.2001 sorted by PC1 score. 9 Table 1.3 Neuron to glia ratio comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Table 1.4 Top 25 genes with similar expression patterns between mouse and human . . . . . . . . . 13 Table 1.5 Top 25 genes with anti-correlated expression patterns between mouse and human . . . . 14 Table 1.6 Negatively correlated genes that show discordant patterns in Zeng et al. . . . . . . . . . . 15 Table 1.7 Negatively correlated genes that show discordant patterns in Miller et al. . . . . . . . . . 15 Table 2.1 Nervous tissue datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Table 2.2 Muscle tissue datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Table 2.3 Examples of datasets and RS from “other” tissues . . . . . . . . . . . . . . . . . . . . . 23 Table 2.4 Cancer-related datasets that have low hematopoietic gene expression . . . . . . . . . . . 24 Table 2.5 Top GO biological process in each module from the EE matrix . . . . . . . . . . . . . . 26 Table 2.6 Top 5 GO annotations for the muscle contraction EE module . . . . . . . . . . . . . . . 27 Table 2.7 Muscle contraction EE module RS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Table 2.8 Top GO biological process in each module from the DE matrix . . . . . . . . . . . . . . 30 Table 2.9 Top 5 GO annotations for the generation of precursor metabolites DE module. . . . . . . 32 Table 2.10 Generation of precursor metabolites DE module RS . . . . . . . . . . . . . . . . . . . . 33 v List of Figures Figure 1.1 Analysis workflow of human and mouse gene expression across brain regions . . . . . . 3 Figure 1.2 Gene expression of orthologous genes in the mouse neuron-enriched and oligodendrocyte- enriched patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Figure 1.3 Schematic view of cell type ratios across brain regions . . . . . . . . . . . . . . . . . . 10 Figure 1.4 Correlation distribution between orthologous genes that are expressed . . . . . . . . . . 12 Figure 1.5 Examples of positively and negatively correlated gene expression patterns between mouse and human . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Figure 2.1 Experimental design and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Figure 2.2 EE vs DE tissue-enriched matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Figure 2.3 The distribution of Spearman rank correlations between EE and DE datasets. . . . . . . 25 Figure 2.4 Clustering of GO-enriched EE modules . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Figure 2.5 Muscle contraction EE module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Figure 2.6 Clustering of GO-enriched DE modules . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Figure 2.7 Generation of precursor metabolites DE module . . . . . . . . . . . . . . . . . . . . . . 32 vi Glossary AIBS Allen Institute for Brain Science, a nonprofit organization that makes publicly available large-scale data that pertains to neuroscience which includes in situ images of the mouse brain and human brain microarray ANOVA Analysis of Variance, a set of statistical techniques to identify sources of variability between groups AUC Area Under a Receiver Operating Characteristic Curve, the area under the curve that shows the true positive rate against the false positive rate at different cutoffs, an area of 1.0 shows perfect enrichment while an area of 0.5 indicates no enrichment DE Differential Expression, the difference in expression levels between sample groups represented by a p-value EE Expression, the relative mean expression level of a gene across all samples within a dataset ranging from 0.0 (no expression) to 1.0 (high expression) GEO Gene Expression Omnibus, a public data repository of functional genomics studies GO Gene Ontology, is a set of controlled vocabularies describing gene products in terms of biological processes, cellular components and molecular functions H0351.2001 Allen Human Brain Atlas donor profile of a 24 year old African American male H0351.2002 Allen Human Brain Atlas donor profile of a 39 year old African American male ISA Iterative Signature Algorithm, a biclustering algorithm that iteratively selects genes and samples that are significantly different based on a threshold ISH In situ Hybridization, is an experimental technique where labelled RNA strands hybridize to comple- mentary strands localized in a specific tissue location PC1 First Principal Component, the principal component with the largest variance PCA Principal Component Analysis, a statistical technique that projects high dimensional data to lower dimensions in terms of orthogonal variables called principal components vii RS Result Set, the pair of sample groups within a dataset from which ANOVA was used WM/GM White Matter to Grey Matter Transcript Ratio, the gene expression ratio between gray matter samples and adjacent white matter samples viii Acknowledgments First and foremost, my appreciation goes to Dr. Paul Pavlidis who has been a kind and patient mentor. To Paul, thanks for taking me under your wings and always encouraging me to pursue my intuition. To Dr. Leon French, thanks for giving me a head start towards a productive and exciting thesis project and for providing insightful discussions and feedback on Chapter 1. I would also like to acknowledge Raymond Lim, Tyler Funnell, the Gemma developers and curators for their contributions toward the development of the differential expression matrix in Chapter 2. My thanks go to Elodie Portales-Casamar for providing feedback and suggestions toward this manuscript. To everyone in the Pavlidis Lab, thanks for all the support and camaraderie during my brief time in the lab. I would also like to thank my committee members, Dr. Joerg Gsponer and Dr. Ann-Marie Craig for their helpful feedback toward my work. I also like to thank Dr. Frederic Pio and Dr. Jack Chen for providing my initial bioinformatics training, Dr. Paula Lario and Dr. Anders Ohrn for providing me with the practical experience to work in the industry, and, Dr. Ryan Brinkman, Dr. Wyeth Wasserman, Dr. Virginie Bernard, Dr. Matthew Farrer, and Dr. Carles Vilariño-Güell for the rotation opportunities and mentorship. Also, my thanks go to Sharon Ruschkowski for keeping me on track and to my fellow bioinformatics students for sharing this experience with me. Finally, my research would not have been possible without the generous funding from the Canadian In- stitute for Health Research Bioinformatics Training Program and from Dr. Pavlidis. My data analysis would have not gone forward without the datasets provided by the Allen Institute for Brain Science and the many researchers and donors who donated their data towards the improvement of our scientific understanding. ix Dedication To my fellow friends and colleagues for their camaraderie. To my Mom, Pacita, thanks for helping me stay healthy and keeping me out of the hospital. To my Dad, Lamberto, for providing us with a warm home. And to my Sister, Maria, for filling in the missing gaps and for being the best storyteller I have ever met. x Chapter 1 Neuron-enriched gene expression patterns are regionally anti-correlated with oligodendrocyte-enriched patterns in the adult mouse and human brain 1.1 Introduction Gene expression in the adult mammalian brain is highly complex and poorly understood. Over 80% of all genes are expressed in the central nervous system, often with patterns that vary in time and space [19, 22, 24]. Many genes show patterns that correspond to classical neuroanatomical subdivisions [24]. Others re- flect neurotransmitter systems, and yet others appear to reflect patterns laid down during development [10, 22, 44]. The functional significance of many other patterns is not clear. As the neuroscience com- munity increasingly integrates data across modalities, gaining a deeper understanding of expression patterns is important. One way to gain insight into these patterns is to examine their conservation in evolution. An- other is to dissect them into sub-patterns that reflect different cell types. Progress on both of these fronts is enabled by the availability of large-scale data sets. In this paper we focus on expression patterns in the normal adult human brain, comparing them to expression in the normal adult mouse, extending our recent work [17]. There is a broad expectation that gene expression in the mouse and human should be similar, and the brain is no exception. It is well known that the fundamental anatomical structure and function of the nervous system is common across mammals. This is exemplified by the similarities observed in the gene expression patterns in the subcortical regions of the brain [25, 41]. Gene expression in the cortical regions on the other hand show greater gene expression diversity between mouse and human [45]. Differences in gene expression may be due to the increased number of cortical neurons in primates compared to rodents [20]. However, none of these studies is comprehensive in terms of brain regions or genes and insights into studies that look at cell type compositions have been limited. Within specific brain regions, inverse relationships between 1 cell type expression patterns have been observed in human [33]. However, it is unclear whether expression patterns are also anti-correlated between brain regions. Recently we reported that gene expression across adult mouse brain regions is dominated by patterns associated with neuron and oligodendrocyte marker expression levels [17]. These patterns were identified by seeking strong anti-correlated patterns of gene expression and also by principal component analysis (PCA). PCA captures the dominant patterns in the data in orthogonal variables termed principal components [35]. In the adult mouse brain, higher levels of expression of genes with a neuron-enriched pattern tended to be associated with anterior regions and regions with higher macroconnectivity [17]. The opposite was observed for the oligodendrocyte-enriched pattern. We hypothesized that similar relationships exist in the human brain. To investigate the gene expression patterns in the human brain, we applied PCA to the regional tran- scriptomes of two adult human brains. Based on the first principal component (PC1) scores, we identified two groups of genes that were enriched for neuron cell type markers (the “neuron-enriched” pattern) and oligodendrocyte cell type markers (the “oligodendrocyte-enriched” pattern) respectively. Our results show that the significant portion of the transcriptome can be explained by the expression of neuron and oligoden- drocyte cell type markers which are anti-correlated across brain regions. Moreover, in comparison to mouse subcortical regions, we report homologous genes with similar expression patterns which are also enriched for neuron and oligodendrocyte markers but not astrocyte markers. We also observed homologous genes with differences in expression patterns, the details of these patterns could provide additional insights into functional similarities and differences among mammalian brain lineages. 1.2 Methods We used publicly available datasets and performed two independent analyses to study cell type expression patterns within the human brain and between the mouse and human brain. The overview of the materials and methods used are shown in Figure 1.1. 1.2.1 Human brain gene expression We analyzed the normalized gene expression data from two healthy adult human post-mortem brains down- loaded from the publicly available dataset called the “Allen Human Brain Atlas” provided by the AIBS (Allen Institute for Brain Science) (http://www.brain-map.org/) [19]. Briefly, donor H0351.2001 was a 24 year old African American male and donor H0351.2002 was a 39 year old African American male. For both brains, larger regions were manually macrodissected whereas smaller regions were laser captured microdis- sected. There are 896 brain region samples in the H0351.2001 dataset while the H0351.2002 dataset had 946 samples. The two human datasets were processed and analyzed separately. Sample replicates with the same “structure name” column annotation were averaged, yielding 323 columns for H0351.2001 and 346 columns for H0351.2002. Samples from the left and right hemispheres were kept separate. Samples of white matter tracts (corpus callosum and cingulum bundle) were excluded from both matrices which resulted in 320 columns in the H0351.2001 dataset and 345 columns in the H0351.2002 dataset. Each normalized gene expression matrix contained data for 58,691 probes. We combined multiple probes for the same gene by taking the mean, yielding expression levels for 29,191 genes. 2 Human Principal components analysis (PCA) Mouse Coexpression and PCA Microarray 2 donors In situ hybridization Neuron enriched genes Oligo enriched genes Neuron enriched genes Oligo enriched genes Diverged gene expression Conserved gene expression Human Mouse Compare human and mouse gene expression Ce ll t yp e ma rke rs Ce ll t yp e ma rke rs Ce ll t yp e ma rke rs Figure 1.1: Analysis workflow of human and mouse gene expression across brain regions. Quality control for expressing genes and grey matter tissue samples were applied prior to analysis. Re- gional gene expression patterns were defined using PCA for two human brain microarray data in the first analysis (left). A similar method was applied to mouse ISH data previously as described in French et al. (2011) (right) [17]. The second analysis compares homologous data matrices of human H0351.2001 and mouse (middle). Cahoy et al. (2008) cell type markers were used to define neuron and oligodendrocyte-enriched patterns [9]. 1.2.2 Mouse brain gene expression We used the mouse gene expression data from the “Allen Mouse Brain Atlas” as described in our previous study [16]. Briefly, colorimetric in situ hybridization (ISH) images were collected from adult male, 56-day- old C57BL/6J normal mouse brains [24]. The ISH images were previously quantified and registered to a 3D reference atlas by Ng et al. [30]. The resulting brain region level expression energy (hereafter referred to as gene expression) is defined as the product of the expression area and the expression intensity [31]. Missing values are reported as NAs. The resulting mouse expression matrix has 20,444 genes and 207 brain regions. 3 1.2.3 Human brain analysis For the analysis of the human data (independent of the mouse data), we focused our analysis on regionally variable grey matter expressed genes by discarding genes with standard deviation or mean expression below the 25th percentile. After filtering, the H0351.2001 dataset had 14,595 genes and 320 brain regions while the H0351.2002 dataset had 14,615 genes and 345 brain regions. We mean-centered and scaled the expression of each gene by its standard deviation across brain regions by using the “scale” function in R [38]. The “prcomp” function in R was used to calculate the principal components of the scaled gene expression matrix. PC scores for each gene correspond to the “x” value while PC loadings for each brain region correspond to the “rotation” value of the “prcomp” result. For consistency, we use the convention that the oligodendrocyte markerMOBP has a positive PC1 score. We measured the cell type enrichment in PC1 scores by measuring the area under the curve (AUC) of the receiver operating characteristic curves, in a manner similar to the “wilcox.test” function in R. First, we ranked genes by their PC1 scores. Second, we divided the ranked list of genes into the positive and negative gene sets. This condition depends on the cell type of interest. For example, when we calculate the AUC for neuron markers, those genes that are found in the Cahoy neuron marker list are included in the positive gene set and all other genes are included in the negative gene set. Afterwards, we compare the positive and negative gene sets by calculating the AUC. To maintain positive AUC scores, the signs of the glia PC1 scores and loadings were reversed before AUCs are calculated. 1.2.4 Human-mouse comparisons Human H0351.2001 and mouse brain region names were manually matched using the sample annotations and ontologies provided by the AIBS. Human genes were converted to mouse genes using HomoloGene build 66 [43]. We manually compared each brain region name in the AIBS mouse and human structure ontology files. For this analysis, we averaged the gene expression of both left and right human brain hemispheres with a matching structure name. However, there are many brain regions with structure names that do not match between species. To circumvent this, for each species, each brain region was manually annotated with a parent structure that is common to both species. Gene expression of multiple brain regions with the same parent structure were averaged. For example, the human regions “CA1”-“CA4” were averaged to match the parent structure “Ammon’s horn”. Likewise, the mouse regions “Lateral group of the dorsal thalamus”, “Lateral posterior nucleus of the thalamus”, and “Suprageniculate nucleus” were averaged to match the parent structure “Lateral group of Nuclei, Dorsal Division”. Gene expression values of both matrices were then quantile normalized. Finally, genes with expression levels below the 25th percentile in both species were removed. The resulting matched human and mouse matrices represent expression values of 7,911 genes across 58 subcortical brain regions. We calculated the Spearman rank correlation for each homologous gene. Cell type enrichment of the homologous gene correlation was quantified as AUC in a similar manner to how AUC was calculated from PC1 scores. 4 1.2.5 Statistical analysis We used the “cor.test” function in R to calculate Spearman rank correlations together with matching p- values. P-values were corrected for multiple testing by controlling for the false discovery rate, which are reported as q-values [4]. The distribution of orthologous gene expression pattern correlations was compared to 20 random distributions where human gene labels were shuffled without replacement. Correlations for data with missing values were calculated by using the “pairwise” method of the “cor” function in R [38]. Hierarchical clustering was performed with the “hclust” function in R [38], using Euclidean distances and Ward’s minimum variance method as parameters [42]. Gene ontology analysis for the 100 most positively and negatively correlated expression patterns were performed using DAVID [11]. 1.2.6 Additional data sources Cell type markers were obtained from Cahoy et al. (2008) [9]. Only those marker genes that have at least 10x fold enrichment were used. In H0351.2001, there are a total of 267 neuron, 103 oligodendrocyte and 143 astrocyte cell type markers that are homologous to the mouse study. Similarly, the H0351.2002 dataset has 270 neuron, 104 oligodendrocyte and 145 astrocyte markers. White matter to grey matter (WM/GM) transcript ratios within the anterior cingulate gyrus were obtained from Sibille et al. (2008) [40]. Sibille et al. defined WM/GM transcript ratio for each gene in each brain area as the ratio between the average expression of using all samples in the gray matter area and the average expression of using all samples in the adjacent white matter area. Ratios of multiple probe sets for the same gene were averaged. Glia to neuron cell ratios for the human cerebellum, cerebral cortex and the rest of the brain were obtained from Azevedo et al. (2009) who applied a chemomechanical dissociation technique to purify cells which were labelled by immunohistochemistry [2]. In relation with mouse and human expression pattern differences, the list of 73 genes that show differ- ential expression pattern between mouse and human visual and temporal cortices was obtained from Zeng et al. (2012) [45]. Genes with discordant expression patterns between species were obtained from the list of 49 human-specific markers (genes that are correlated with modules enriched for cell types in human but not in mouse) in the meta-analysis of brain expression performed by Miller and colleagues (2010) [28]. These brain regions include both cortex and subcortical regions. 1.3 Results 1.3.1 Neuron-enriched and oligodendrocyte-enriched patterns are conserved We characterized gene expression profiling data from two adult human brains (identified by the AIBS as donors H0351.2001 and H0351.2002) in a manner comparable to our previous analysis of the adult mouse brain (Figure 1.1). After filtering (see Methods), the H0351.2001 dataset had 14,595 genes while the H0351.2002 dataset had 14,615 genes, 13,250 of which were found in both datasets. For H0351.2001, we obtained 320 brain region samples. Telencephalon accounts for most of the brain region samples (53%), 5 metencephalon (22.1%), diencephalon (11%), myelencephalon (8.1%), and mesencephalon the least (5.9%). The H0351.2002 dataset had 345 samples with similar proportions of major brain divisions as H0351.2001. In H0351.2001, cerebellar samples clustered more closely compared to other brain regions (Figure 1.2), in line with previous observations that cerebellum gene expression is the most unique compared to other major brain divisions [26, 34, 39]. This was less apparent in H0351.2002 (data not shown). Hereafter, we report results based on these filtered datasets. Next, we tested whether genes that express anti-correlated cell-type enriched patterns in mouse are also anti-correlated in humans [17]. We averaged the expression of all human homologs with the mouse neuron- enriched pattern. Similarly, we also averaged the expression pattern of all human homologs with the mouse oligodendrocyte-enriched pattern. As in mouse, the averaged neuron-enriched pattern is anti-correlated with the averaged oligodendrocyte-enriched pattern (H0351.2001 rho = -0.40, P < 0.0001 and H0351.2002 rho = -0.61, P < 0.0001) (Figure 1.2). Genes that show neuron-enriched patterns are predominantly expressed in metencephalon and telencephalon regions while genes in the oligodendrocyte-enriched patterns are not restricted to any major brain division. This conservation of cell type marker enriched patterns is also evident in a PCA of the human data. The first three principal components of H0351.2001 accounted for 15.6%, 11.6%, and 8.31% of the total variance respectively whereas we see a slight decrease in the case of H0351.2002 with 15.2%, 8.07%, and 5.98% of the total variance respectively. The first principal component (PC1) gene scores of the two human datasets are strongly positively correlated (rho = 0.72, P < 0.0001), indicating that overall, the two brains have similar dominant expression patterns, consistent with the findings of Hawrylycz et al. (2012) [19]. We observed that these oligodendrocyte and neuron marker genes tend to have PC1 scores with opposite signs, consistent with our previous study in mouse. We term these as “oligodendrocyte-enriched” and “neuron- enriched” respectively. The top 25 genes in the “oligodendrocyte-enriched” and “neuron-enriched” gene sets are shown in Table 1.1 and Table 1.2 respectively. For each cell type, we measured the cell type enrichment by comparing the PC1 ranks of those cell type marker genes (as determined by Cahoy) against the PC1 ranks of the remaining genes (see Methods). In H0351.2001, neuronal markers showed the highest enrichment (AUC = 0.77), followed by oligodendrocyte markers (AUC = 0.73), and astrocyte markers the least (AUC = 0.66). We found evidence for comparable cell type marker enrichment in H0351.2002 PC1 loadings as well (neuron markers AUC = 0.82, oligodendrocyte markers AUC = 0.81, astrocyte markers AUC = 0.63). By way of comparison, in mouse we had found that PC2 gene loadings showed the highest enrichment for oligodendrocyte markers (AUC = 0.77) and neuron markers (AUC = 0.63) and no enrichment for astrocyte markers (AUC = 0.52) [17]. 1.3.2 Principal component loadings partly reflect varying cell-type proportions The PC1 gene loadings could either be explained by variations in expression levels within cells, or by variations in the ratio of different sub-populations of cells (or some combination of these). To further investigate this, we calculated the correlation between the H0351.2001 PC1 gene loadings and the white matter to gray matter transcript ratio (WM/GM) for 8,088 genes with data for both [40]. The correlation is statistically significant (rho = 0.59, P < 0.0001). Since white matter regions have been excluded from the 6 CABLES2 LIMK1 NRG1 KCTD9 SGPP2 SEMA7A PVALB SYT2 NEFH VAMP1 SCN1A TIAM1 DIP2A DUSP6 GLUL HCN2 LGI3 BCAT1 E2F1 ISCU DDT CENPF PEA15 PDGFRA CPNE2 PTPRZ1 PPAP2B S100A16 ITM2C ADSSL1 RND2 FOS SERPINB1 SLC27A1 ARHGEF10 PLEKHB1 DAAM2 CYP27A1 ELOVL5 S100B GATM CNP CLDN11 ASPA FA2H ENPP2 PLP1 KLK6 ENDOD1 CRYAB SLC44A1 GPRC5B QDPR RNF13 SLC12A2 KALRN CAMK2A GRIA2 EGR3 MEF2C RTN4RL1 SHISA9 CPNE7 GRIK5 CAMKV PRKCG CELF3 GRIA1 HPCAL4 ARMC2 CALB1 PKIA LY6H NNAT Mouse pattern Neuron−enriched Oligo−enriched Brain division Diencephalon Mesencephalon Metencephalon Myelencephalon Telencephalon Figure 1.2: Human H0351.2001 gene expression profile of orthologous genes reported in the mouse neuron and oligodendrocyte-enriched patterns (French et al., 2011) [17]. High and low expres- sion levels are colored in yellow and blue respectively. Rows are genes colored by their homolog cell type enrichment. Columns are brain region samples colored by major brain divisions. Hier- archical clustering was performed using the Ward’s minimum variance method in R [42]. 7 Gene symbol Gene description Entrez ID PC1 Mean StdDev SLC27A1 solute carrier family 27 (fatty acid transporter), member 1 376497 12.67 10.92 0.33 REST RE1-silencing transcription factor 5978 12.46 4.65 0.43 PPARA peroxisome proliferator-activated receptor al- pha 5465 12.41 3.65 0.37 ARHGEF10 Rho guanine nucleotide exchange factor (GEF) 10 9639 12.33 5.19 0.42 TRIM56 tripartite motif-containing 56 81844 12.14 3.93 0.42 EGFR epidermal growth factor receptor 1956 12.12 4.21 0.55 A 24 P943258 AGILENT probe A 24 P943258 (non-RefSeq) NA 12.11 4.92 0.43 A 23 P129258 AGILENT probe A 23 P129258 (non-RefSeq) NA 12.01 13.75 0.51 RBMS2 RNA binding motif, single stranded interacting protein 2 5939 12.00 6.32 0.39 A 24 P316059 AGILENT probe A 24 P316059 (non-RefSeq) NA 11.98 4.83 0.39 GPR75 G protein-coupled receptor 75 10936 11.96 6.83 0.39 PFKFB3 6-phosphofructo-2-kinase/fructose-2,6- biphosphatase 3 5209 11.95 7.15 0.39 C12orf39 chromosome 12 open reading frame 39 80763 11.95 4.65 0.47 MAFIP MAFF interacting protein 727764 11.84 5.13 0.36 CXorf36 chromosome X open reading frame 36 79742 11.82 2.58 0.42 NPAS3 neuronal PAS domain protein 3 64067 11.75 8.08 0.35 SDPR serum deprivation response 8436 11.74 3.43 0.44 LIMS1 LIM and senescent cell antigen-like domains 1 3987 11.74 3.45 0.35 A 24 P475689 AGILENT probe A 24 P475689 (non-RefSeq) NA 11.67 3.67 0.43 BMP7 bone morphogenetic protein 7 655 11.59 7.21 0.38 CTNNA1 catenin (cadherin-associated protein), alpha 1, 102kDa 1495 11.58 6.69 0.41 TJP1 tight junction protein 1 (zona occludens 1) 7082 11.53 8.61 0.34 KIF19 kinesin family member 19 124602 11.53 3.02 0.50 A 23 P134887 AGILENT probe A 23 P134887 (non-RefSeq) NA 11.53 5.72 0.49 F11 coagulation factor XI 2160 11.50 3.10 0.41 Table 1.1: Top 25 genes in the oligodendrocyte-enriched gene set of human H0351.2001 sorted by PC1 score. human data we used, we interpret the WM/GM transcript ratios as variations in cell type proportions within grey matter regions. We visualized the PC1 loadings on the schematic image of the brain using the Allen Brain Explorer 2 (see Methods) (Figure 1.3). Regions where there is high neuron marker expression include inferior frontal gyrus, CA2 and temporal pole. Regions where there is high oligodendrocyte marker expression include globus pallidus, putamen and head of caudate nucleus, in agreement with the enrichment of these regions in myelinated axons [14]. In addition, we calculated the ratio between “oligodendrocyte-enriched” PC1 markers and “neuron- enriched PC1” markers and compared it to the glia to neuron ratio measurements performed by Azevedo et 8 Gene symbol Gene description Entrez ID PC1 Mean StdDev RNF41 ring finger protein 41 10193 -18.90 6.48 0.39 ARF5 ADP-ribosylation factor 5 381 -18.87 7.80 0.47 A 32 P86533 AGILENT probe A 32 P86533 (non-RefSeq) NA -18.49 8.19 0.64 GSTA4 glutathione S-transferase alpha 4 2941 -18.49 7.76 0.34 MMS19 MMS19 nucleotide excision repair homolog ... 64210 -18.26 6.51 0.40 TMEM59L transmembrane protein 59-like 25789 -18.26 6.55 0.71 CLTA clathrin, light chain A 1211 -18.26 9.45 0.38 UBE2K ubiquitin-conjugating enzyme E2K ... 3093 -18.24 9.58 0.36 AP2A2 adaptor-related protein complex 2, alpha 2 sub- unit 161 -18.22 8.50 0.36 NMNAT2 nicotinamide nucleotide adenylyltransferase 2 23057 -18.11 8.75 0.53 LCMT1 leucine carboxyl methyltransferase 1 51451 -18.07 8.87 0.34 PDCD2L programmed cell death 2-like 84306 -17.99 6.54 0.41 LOC727967 similar to block of proliferation 1 727967 -17.92 6.31 0.44 HAGH hydroxyacylglutathione hydrolase 3029 -17.89 8.14 0.50 DHX30 DEAH (Asp-Glu-Ala-His) box polypeptide 30 22907 -17.88 6.94 0.42 RTN1 reticulon 1 6252 -17.87 9.77 0.61 CCT2 chaperonin containing TCP1, subunit 2 (beta) 10576 -17.81 10.38 0.37 PI4KA phosphatidylinositol 4-kinase, catalytic, alpha 5297 -17.80 8.38 0.41 IARS isoleucyl-tRNA synthetase 3376 -17.78 7.50 0.38 ABHD14A abhydrolase domain containing 14A 25864 -17.76 7.74 0.42 PLD3 phospholipase D family, member 3 23646 -17.76 8.49 0.61 ATP6AP1 ATPase, H+ transporting, lysosomal accessory protein 1 537 -17.74 8.99 0.45 C19orf62 chromosome 19 open reading frame 62 29086 -17.66 7.36 0.43 RAB24 RAB24, member RAS oncogene family 53917 -17.64 8.06 0.38 KLHDC3 kelch domain containing 3 116138 -17.63 6.60 0.43 Table 1.2: Top 25 genes in the neuron-enriched gene set of human H0351.2001 sorted by PC1 score. Brain division H0351.2001 H0351.2002 Azevedo et al. 2009 Cerebellum -0.032 ± 0.062 0.013 ± 0.045 0.23 Cerebral grey matter -0.00069 ± 0.058 -0.012 ± 0.059 1.48 Rest of the brain 0.016 ± 0.042 0.015 ± 0.042 11.35 Table 1.3: PC1 brain loadings (mean ± standard deviation) of the two AIBS human datasets and mea- sured glia to neuron ratio from Azevedo et al. (2009) [2] in cerebellum, cerebral grey matter and remaining brain regions. al (2009) [2]. In agreement, in H0351.2001, we find that the human cerebellum, cerebral grey matter and the rest of the brain samples show increasing glia to neuron ratio respectively (Table 1.3). In H0351.2002, the cerebellum shows higher glia to neuron ratio than cerebral grey matter which may be due to individual variability or technical artifacts. Together, these results suggest that gene expression variance in the human brain can partly be explained by variations in cell type composition, though we cannot exclude contributions from changes in expression 9 Figure 1.3: Schematic view of the H0351.2001 human brain showing oligodendrocyte-neuron PC1 marker ratio within each brain region sample. The brain PC1 loadings were obtained from the rotation attribute result object of the “prcomp” function in R. PC1 brain loadings range from 0.03 (orange) to -0.04 (purple) which suggest increasing glia-neuron ratio. Primary and secondary axes correspond to the mri z and mri y coordinates respectively. These dots were manually overlaid onto a brain image from the Allen Brain Explorer 2 software (http://mouse.brain-map. org/static/brainexplorer). In order to visualize subcortical region samples, we have hidden the visualization of the left cerebral hemisphere which causes some cortical samples (such as part of the left temporal cortex) to appear outside of the brain. within cell types. 1.3.3 Orthologous genes with positively correlated expression patterns are enriched in cell type markers In addition to identifying dominant gene expression patterns within each species, we also performed a comparison of gene expression patterns between orthologous gene and brain region samples in mouse and in human AIBS data, focusing on the H0351.2001 dataset which we deem to be the higher quality of the two data sets based on the clustering of cerebellar regions described above. Within data sets, we found that regional expression patterns show greater homogeneity in human (mean Spearman rho = 0.98 ± 0.0079) 10 than in mouse (mean Spearman rho = 0.86 ± 0.022). That is, expression patterns across mouse brain regions were apparently more variable than across human brain regions, possibly for technical reasons. Next, we measured the conservation of gene expression patterns by measuring the correlation for each homologous gene across matched brain regions. Finally, we compared our results with those of other studies by performing enrichment analyses on genes ranked by the strength of their correlation between species. To prepare gene expression matrices of the same size, we limited the analysis to genes expressed above the 25th percentile in both species and brain regions which could be matched between mouse and human, re- sulting in 7,911 genes and 58 subcortical brain regions (see Methods). Major brain regions include the hippocampal formation, cerebral nuclei, thalamus, epithalamus, hypothalamus, midbrain regions, pons, medulla and cerebellum. In this filtered data set, we saw consistent cell type marker enrichment in the PC scores in both mouse and human which indicates that the filtering process did not have a large effect on the data with respect to the patterns described in the previous section (data not shown). We calculated the Spearman rank correlation between pairs of homologous brain regions and found statistically significant positive correlations (mean Spearman rho = 0.31 ± 0.031, P < 0.0001). The three most similar brain regions include Ammon’s horn (rho = 0.40), dentate gyrus (rho = 0.38), and subiculum (rho = 0.35). Brain regions with the poorest correlation include nucleus raphe pontis (rho = 0.21), gracile nucleus (rho = 0.25), and pallidum (rho = 0.25). In terms of genes, we measured the Spearman rank correlation of each homologous gene’s expression levels across matched brain regions. We used these correlation values to rank homologous genes, such that those genes with conserved expression patterns are positively correlated while genes with discordant patterns have either no correlation or are anti-correlated across matched regions. We observed a positive skew in the correlation distribution (mean rho = 0.074, min rho = -0.57, max rho = 0.73; Figure 1.4). To verify whether this skew is significant or not, we compared this correlation distribution with a random distribution obtained by shuffling gene labels (see Methods). There are 53 fewer genes with correlation below -0.30 when compared to random while there are 645 more genes with correlation above 0.30 when compared to random. Together, this indicates that there are more genes with similar expression patterns than not. The top 25 genes with the most positively and negatively correlated gene expression between mouse and human are shown in Table 1.4 and Table 1.5 respectively. Figure 1.5 shows examples of genes with positively and negatively correlated expression levels across brain regions. We note that when only a few (∼10) especially highly correlated brain regions were selected, the distribution became more positively skewed (data not shown), suggesting that more focused comparisons might provide higher resolution results, but it was not obvious how to choose such regions a priori. Since we observed cell-type marker enrichment in the gene expression patterns for each species indepen- dently, we hypothesize that homologous genes that show conserved expression patterns are also enriched for cell type markers. We measured the cell-type marker enrichment using the ranked list of homologus genes, annotated by cell type in Cahoy et al. (2008) [9] (see Methods). In line with our hypothesis, our results show that expression patterns of homologous genes are enriched for neuronal (AUC = 0.74) and oligoden- drocyte (AUC = 0.71) markers, but not astrocyte (AUC = 0.53) markers (see Methods). We interpret this as suggesting that neuronal markers and oligodendrocyte markers are generally more conserved in expression 11 −0.5 0.0 0.5 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5 3. 0 Orthologous gene correlation N = 7911   Bandwidth = 0.02542 D en si ty observed random Figure 1.4: Correlation distribution between orthologous genes that are expressed. Correlation dis- tribution is skewed towards the positive compared to random where human gene labels were shuffled without replacement. patterns in comparison to the non-cell type markers (Table 1.4), consistent with the findings of Miller et al. [28]. In contrast, astrocyte markers were relatively poorly conserved overall, some astrocyte markers show positively correlated patterns (e.g. AGT, GFAP) while others show negatively correlated patterns (e.g. SLC27A1, SCARA3) between mouse and human (Table 1.4 and Table 1.5). We found similar results when using less stringent criterion for selecting genes from the Cahoy data (at least 5x enrichment instead of 10x, data not shown). We performed a Gene Ontology (GO) enrichment analysis for the top 100 genes with the most positively and negatively correlated patterns. Those genes with similar expression patterns are significantly enriched for Gene Ontology (GO) biological processes such as ion transport (GO:0006811), transmission of nerve impulse (GO:0019226), and synaptic transmission (GO:0007268). The top 100 genes with the most negatively correlated patterns are enriched in biological processes such as negative regulation of homeostatic process (GO:0032845), fatty acid oxidation (GO:0019395), and macromolecule catabolic process (GO:0009057). Discordant expression patterns between mouse and human orthologs might indicate interesting func- tional divergences. We identified only 78 genes with reasonably strong negative correlations between mouse 12 Gene symbol Gene description rho Mm Expr Hs Expr KCNC1 potassium voltage-gated channel, Shaw-related subfamily, member 1 0.73 9.93 3.84 SLC17A6 solute carrier family 17 (sodium-dependent inorganic phosphate cotrans- porter), member 6 0.73 7.62 4.65 ZIC1 Zic family member 1 (odd-paired homolog, Drosophila) 0.69 3.71 5.61 PCP4 Purkinje cell protein 4 0.66 8.16 4.69 GABBR2 gamma-aminobutyric acid (GABA) B receptor, 2 0.66 13.81 4.38 CACNA1C calcium channel, voltage-dependent, L type, alpha 1C subunit 0.65 2.63 1.81 CAMK2D calcium/calmodulin-dependent protein kinase II delta 0.63 12.37 3.42 OSBPL5 oxysterol binding protein-like 5 0.63 3.17 2.67 VAT1 vesicle amine transport protein 1 homolog (T. californica) 0.62 1.76 1.73 SLC8A1 solute carrier family 8 (sodium/calcium exchanger), member 1 0.62 7.77 3.70 PLCB4 phospholipase C, beta 4 0.61 7.81 4.30 FOXP2 forkhead box P2 0.61 1.90 1.97 SPOCK1 sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1 0.61 12.21 3.36 C20orf103 chromosome 20 open reading frame 103 0.61 3.47 3.38 KCNQ3 potassium voltage-gated channel, KQT-like subfamily, member 3 0.61 4.16 3.82 GNG4 guanine nucleotide binding protein (G protein), gamma 4 0.60 0.19 0.24 HTR1A 5-hydroxytryptamine (serotonin) receptor 1A 0.60 1.27 0.85 ZMAT4 zinc finger, matrin type 4 0.60 2.62 2.80 ADAM11 ADAM metallopeptidase domain 11 0.60 10.30 3.41 NRN1 neuritin 1 0.60 9.97 5.40 NTNG1 netrin G1 0.59 6.73 6.15 ST8SIA5 ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 5 0.59 5.81 3.90 HCN1 hyperpolarization activated cyclic nucleotide-gated potassium channel 1 0.59 2.62 2.39 PCDH11X protocadherin 11 X-linked 0.59 0.93 0.62 SYN2 synapsin II 0.59 10.29 4.76 Table 1.4: Top 25 genes with similar expression patterns between mouse (Mm) and human H0351.2001 (Hs) sorted by Spearman rank correlation (rho) with q < 0.01. Expr corresponds to the mean expression level across brain regions. and human (rho< -0.3). To seek supporting evidence for these and other negative correlations, we compared our findings to two previous mouse-human comparisons. Zeng et al. (2012) identified 73 genes with patterns considered discordant in the neocortex, including differences in laminar distribution [45]. Of these, 12 are negatively correlated in our study, including one of the 78 meeting a threshold of -0.3 (SLC6A12 rho = -0.31; Table 1.6). Miller and colleagues identified 49 “human-specific” cell-type markers using meta-analysis of microarray data, of which fourteen are negatively correlated in our analysis, of which two are below -0.3 (KIAA0174 rho = -0.36 and ADK rho = -0.32; Table 1.7). Thus despite major differences in methodology and brain regions considered, some previous reports of mouse-human differences are supported by our analysis. 1.4 Discussion We studied the dominant gene expression patterns across the human brain and observed similar complemen- tarity between “neuron/oligodendrocyte” enriched patterns as we previously identified in the mouse [17]. 13 Gene symbol Gene description rho q-value Mm Expr Hs Expr TMEM2 transmembrane protein 2 -0.57 0.001 0.28 0.95 ABCA8 ATP-binding cassette, sub-family A (ABC1), member 8 -0.45 0.020 0.40 0.50 RPIA ribose 5-phosphate isomerase A -0.44 0.026 0.26 0.57 USP28 ubiquitin specific peptidase 28 -0.44 0.029 2.26 1.55 WARS2 tryptophanyl tRNA synthetase 2, mitochondrial -0.43 0.030 0.46 0.38 SMOC2 SPARC related modular calcium binding 2 -0.42 0.037 0.58 1.16 CROT carnitine O-octanoyltransferase -0.41 0.039 2.20 1.54 KIAA1279 KIAA1279 -0.41 0.044 7.40 3.66 ACOX2 acyl-CoA oxidase 2, branched chain -0.40 0.068 0.49 0.42 AGGF1 angiogenic factor with G patch and FHA domains 1 -0.39 0.079 0.28 0.11 MRPS34 mitochondrial ribosomal protein S34 -0.39 0.062 2.17 1.20 TMLHE trimethyllysine hydroxylase, epsilon -0.39 0.057 2.62 1.44 CTR9 Ctr9, Paf1/RNA polymerase II complex component, ... -0.38 0.072 2.86 1.60 TNFRSF11B tumor necrosis factor receptor superfamily, member 11b -0.38 0.097 0.25 0.60 C2orf29 chromosome 2 open reading frame 29 -0.38 0.073 1.40 0.75 NCF4 neutrophil cytosolic factor 4, 40kDa -0.38 0.078 0.21 0.20 PAICS phosphoribosylaminoimidazole carboxylase, ... -0.38 0.075 1.02 0.94 CEP164 centrosomal protein 164kDa -0.37 0.078 0.91 0.61 CECR5 cat eye syndrome chromosome region, candidate 5 -0.37 0.098 2.40 1.02 KIAA0174 KIAA0174 -0.36 0.100 4.34 2.09 ATRX alpha thalassemia/mental retardation syndrome X-linked -0.36 0.090 6.84 1.85 DNAJC5 DnaJ (Hsp40) homolog, subfamily C, member 5 -0.36 0.097 15.11 3.70 FRMD4A FERM domain containing 4A -0.36 0.097 5.73 1.89 RAP1GAP RAP1 GTPase activating protein -0.36 0.092 14.17 3.32 CDK4 cyclin-dependent kinase 4 -0.36 0.590 1.35 1.23 Table 1.5: Top 25 genes with anti-correlated expression patterns between mouse (Mm) and human H0351.2001 (Hs) sorted by Spearman rank correlation (rho). Expr corresponds to the mean ex- pression level across brain regions. Our analysis also shows that in situ data from mouse can be meaningfully compared to microarray data from human. As Lee et al. (2008) pointed out, comparisons between ISH and microarray data are chal- lenging due to technical differences such as probe sequence sensitivity and specificity, dynamic range and normalization and mapping of ISH data [23]. Despite these technical differences, we report gene expression pattern similarities as exemplified by the anti-correlation between neuron and oligodendrocyte enriched pat- terns. Our interpretation of the cell-type enriched pattern in human is similar to our previous interpretation in mouse [17]. A simple explanation is that neurons and glia vary in inverse proportions across brain regions in both human and mouse, which shows an anterior-posterior gradient (Figure 1.3). However, it is difficult to fully verify this because we currently have limited information on the details of the size and proportions of cell types within each brain region sampled. The strength of the cell type marker enrichment suggests that many other genes, while not reported as cell type markers by Cahoy et al. (2008), are likely to be expressed in a cell type enriched manner. Genes in this category include ones we predict based on our readings to be expressed in neurons such as neural 14 Gene symbol Gene description rho q-value Mm Expr Hs Expr SLC6A12 solute carrier family 6 (neurotransmitter transporter, betaine/GABA), member 12 -3.13E-01 0.18 0.26 4.43 GPR85 G protein-coupled receptor 85 -2.66E-01 0.29 4.94 5.48 CACNG8 calcium channel, voltage-dependent, gamma subunit 8 -1.64E-01 0.60 3.07 5.21 COL6A1 collagen, type VI, alpha 1 -1.42E-01 0.68 2.47 6.42 KIAA1370 KIAA1370 -1.27E-01 0.72 1.71 5.56 CLCN2 chloride channel 2 -1.08E-01 0.77 1.83 4.09 MFGE8 milk fat globule-EGF factor 8 protein -8.71E-02 0.82 0.75 3.64 PDYN prodynorphin -6.68E-02 0.87 2.31 3.75 PCDH20 protocadherin 20 -3.60E-02 0.94 2.45 4.95 LGALS1 lectin, galactoside-binding, soluble, 1 -8.12E-03 0.99 1.44 9.01 SNCG synuclein, gamma (breast cancer-specific protein 1) -2.37E-03 1.00 6.76 9.92 SLC6A7 solute carrier family 6 (neurotransmitter transporter, L-proline), member 7 -4.31E-04 1.00 4.49 3.75 Table 1.6: Negatively correlated genes that show discordant patterns in Zeng et al. Genes are sorted by increasing q-value. Mm Expr and Hs Expr correspond to the mean expression level across mouse and human brain regions respectively. Gene symbol Gene description rho q-value Mm Expr Hs Expr KIAA0174 KIAA0174 -3.62E-01 0.10 4.34 5.17 ADK adenosine kinase -3.21E-01 0.15 5.47 7.89 P2RX7 purinergic receptor P2X, ligand-gated ion channel, 7 -2.90E-01 0.23 0.25 6.41 HSPA8 heat shock 70kDa protein 8 -2.03E-01 0.49 19.90 12.10 LEPROT leptin receptor overlapping transcript -9.69E-02 0.80 0.44 8.02 CBFB core-binding factor, beta subunit -8.12E-02 0.84 0.21 5.49 INPP1 inositol polyphosphate-1-phosphatase -6.90E-02 0.87 4.61 6.09 TGFBR3 transforming growth factor, beta receptor III -4.40E-02 0.92 2.38 5.70 COL4A5 collagen, type IV, alpha 5 -4.18E-02 0.92 0.29 5.37 RNF103 ring finger protein 103 -2.35E-02 0.96 2.59 7.92 UQCRC2 ubiquinol-cytochrome c reductase core protein II -2.32E-02 0.96 11.48 7.10 PSEN1 presenilin 1 -1.71E-02 0.97 0.45 6.35 DYNC1I2 dynein, cytoplasmic 1, intermediate chain 2 -8.67E-03 0.99 12.95 9.65 CHD1L chromodomain helicase DNA binding protein 1-like -8.31E-04 1.00 0.21 6.39 Table 1.7: Negatively correlated genes that show discordant patterns in Miller et al. Genes are sorted by increasing q-value. Mm Expr and Hs Expr correspond to the mean expression level across mouse and human brain regions respectively 15 −4 −2 0 2 4 − 4 − 2 0 2 4 SLC17A6 Mouse (relative expression level) H um an  (r ela tiv e  e xp re ss io n le ve l) Diencephalon Mesencephalon Metencephalon Myelencephalon Telencephalon −4 −2 0 2 4 − 4 − 2 0 2 4 MOG Mouse (relative expression level) H um an  (r ela tiv e  e xp re ss io n le ve l) −4 −2 0 2 4 − 4 − 2 0 2 4 AGT Mouse (relative expression level) H um an  (r ela tiv e  e xp re ss io n le ve l) −4 −2 0 2 4 − 4 − 2 0 2 4 USP28 Mouse (relative expression level) H um an  (r ela tiv e  e xp re ss io n le ve l) −4 −2 0 2 4 − 4 − 2 0 2 4 ATRX Mouse (relative expression level) H um an  (r ela tiv e  e xp re ss io n le ve l) −4 −2 0 2 4 − 4 − 2 0 2 4 FRMD4A Mouse (relative expression level) H um an  (r ela tiv e  e xp re ss io n le ve l) Figure 1.5: Examples of positively and negatively correlated gene expression patterns between mouse and human H0351.2001. Dots represent brain region samples colored by major brain divisions. Three genes with expression patterns that are positively correlated are shown at the top (SLC17A6 rho = 0.73, MOG rho = 0.39, AGT rho = 0.44) while negatively correlated gene expression pat- terns are shown at the bottom (USP28 rho = -0.44, ATRX rho = -0.36, FRMD4A rho = -0.36). All six genes have q-values< 0.3. Expression levels are scaled and centered at zero for visualization. epidermal growth factor-like 2 (NELL2), reticulon 4 receptor (RTN4R), potassium channel, subfamily K, member 1 (KCNK1), and glutaminase (GLS) as well as ones we predict to be expressed in oligodendrocytes such as chloride intracellular channel 4 (CLIC4), crystallin, alpha B (CRYAB), prostaglandin D2 synthase 21kDa (PTGDS), quinoid dihydropteridine reductase (QDPR) and G protein-coupled receptor, family C, group 5, member B (GPRC5B). Using a literature review, we have confirmed some of these, suggesting their absence from the lists given by Cahoy et al. to be due to technical factors or the choice of cells used in their study. For example, ISH of the adult mouse and rat brains show RTN4R (reticulon 4 receptor or Nogo receptor) is strongly expressed within neurons of the neocortex, hippocampal formation, and granule cells of the cerebellum [21]. On the other hand, it is also apparent that what we term the “oligodendrocyte-enriched” and “neuron-enriched” patterns are not purely populated by genes specific for those cell types. For example in the H0351.2001 dataset, TMEM163, CNTN1, and TMEM2 are Cahoy oligodendrocyte marker genes but are found close to neuronal markers in our PCA, while the converse is true for the neuronal markers ST8SIA2 16 and GPR12. This complexity presumably in part reflects sub-populations of neurons which have a different physical or regulatory relationship to glial cells than those which occur in the “neuron-enriched” pattern, or vice versa. A second goal of our study was to identify similarities and differences in expression pattern between mouse and human brains. Our overall conclusion is that the similarities vastly outnumber the differences. We found that the similarities are most striking for genes which are known to be enriched in neurons and oligodendrocytes (Table 1.4 and Figure 1.5). In contrast, markers of astrocytes demonstrate more differ- ences between mouse and human. In mouse, astrocyte markers were equally represented in both “neuron- enriched” and “oligodendrocyte-enriched” patterns [17]. In contrast, in the human data, astrocyte markers coordinately vary in expression levels considerably across regions. Astrocytes support the metabolically de- manding tasks of neurons by recycling neurotransmitters and maintaining ion homeostasis in the brain [8]. The enrichment seen in humans could be caused by the increased complexity found only in human astrocytes [32] or by the higher astrocyte to neuron ratio observed with increasing brain complexity [29]. Aside from astrocyte markers, we found evidence for other genes showing discordant expression patterns. For example, the mouse ATRX (alpha thalassemia/mental retardation syndrome X-linked) expression pattern is negatively correlated with human (rho = -0.36, q = 0.09) (Figure 1.5 and Table 1.5). In adult mouse, this gene has a higher expression in the medulla compared to the amygdala, while the opposite is true in human. We cau- tion that from the available data it is difficult to determine which of the differences we observe reflect true biological differences (e.g., different species isoforms), and which are due to differences between ISH and microarray. However, the partial overlap of our negative correlations with previous reports of mouse-human differences [28, 45] suggests that some the other differences we report are worthy of further study. In summary, using PCA, we provide a candidate list of cell type markers which could be useful for targeting specific cell types or specific regional patterns of interest. In addition, we report correlations for the regional expression of genes between mouse and human which can be useful in the development of mouse disease models or in the study of the molecular evolution of the brain. Future studies that explore the different regulatory mechanisms of genes with discordant expression patterns might provide insights into the evolution of brain structure and function. Furthermore, future high-resolution large-scale studies that examine gene expression in developing mouse and human brain will uncover genes that are only active in early development and thus provide a better understanding of human brain evolution. 17 Chapter 2 Large-scale survey of tissue types and experimental conditions across datasets 2.1 Introduction Genes are regulated in different ways. For example, housekeeping genes (e.g. HSP90 [18]) are expressed at similar levels in nearly every cell under normal conditions. On the other hand, other genes are known to be expressed in only a few cell types. For example, the neurotransmitter GABA (gamma-aminobutyric acid) is found in both synaptic vesicles in GABAergic neurons in the brain and also in synaptic like microvesicles secreted from pancreatic beta-cells [7, 15]. Whether GABA is also expressed in other cell types remains unclear. Aside from genes differentially expressed (DE) in different cell types, genes are also DE under different experimental conditions (e.g. disease state, sampling time point or drug treatment). Sample groups are often chosen with a specific biological question in mind. For example, control sam- ples are compared to neurological disease samples with the goal of identifying disease-associated genes [36]. However, this targeted approach limits our understanding to comparisons within these two groups that were selected a priori. A subset of DE genes may also be DE in seemingly unrelated diseases. An understanding of how diseases are related is helpful for drug re-purposing where old drugs are applied to new diseases. It may also be useful to apply knowledge from a well-studied disease to a less-studied one provided that both diseases share a common biological pathway. The idea of integrating microarray datasets is not new. Large-scale microarray studies such as Lukk et al. [27] (Array Expression ID: E-MTAB-62) have combined thousands of datasets to identify DE genes between biological groups such as cell type, tissue type, disease state and cell lines. Lukk et al. grouped samples by using sample annotations which were provided by the submitters of the dataset. They identified tissue-enriched genes by comparing the gene’s expression in each tissue group against the global mean expression level. Aside from using sample annotations, the gene expression content can also be used as well. Engreitz et al. [12] formulated this problem in a data-driven fashion. First, they created a library to search against by calculating the DE profile of each experiment. Next, the DE profile of the input query was compared against 18 the library of DE profiles based on a weighted Pearson correlation similarity measure. To better explore gene function in human and identify which experimental conditions affect it, we com- piled a list of publicly available human microarray datasets in an unsupervised manner. From these datasets, we created matrices that represent gene expression levels (EE) and DE levels. We compared these two matri- ces in two ways. First, we investigated if tissue-enriched genes cluster by tissue type. Second, we identified modules that are enriched for biological processes. The preliminary work and conclusions presented here will provide additional insights into future large-scale gene expression investigations. 2.2 Methods 2.2.1 Data overview and pre-processing Both EE and DE matrices were obtained from the human microarray datasets deposited in Gemma [46]. We term each comparison between two sample groups in each dataset as a “result set (RS)”, e.g. GSE12860 has two RS, control vs. treatment and rheumatoid arthritis fibroblast vs. normal fibroblast. Matrix rows correspond to genes annotated in the microarray platform. EE columns correspond to datasets while DE columns correspond to RS. Since most datasets have only one RS (91%), we refer to RS by their Gene Ex- pression Omnibus (GEO) series ID in our results [3]. We measured gene expression levels at the resolution of each dataset. Expression levels in the EE matrix were obtained by calling the “dEDVRank” webservice in Gemma where genes were averaged across all samples in each dataset and normalized from zero (low expression) to one (high expression) across all genes in the dataset. Since we are also interested in gene expression changes within a dataset, the Gemma framework was used to calculate the p-values in the DE matrix. Briefly, a one-way analysis of variance (ANOVA) was performed for all the genes in each RS. The following filters were applied to the human microarray studies. First, we selected RS that were derived from the GPL570 and GPL96 platforms which corresponds to the Affymetrix Human Genome U133 Plus 2.0 Array and U133A Array respectively. These platforms are the most common human microarray platforms in Gemma. Restricting our analysis to within these two similar platforms also reduces variability between different microarray platforms. Moreover, since these platforms have similar probe sets, there were fewer instances of probe sets with missing values across RS. Those RS with missing values in more than 10% of the total number of probe sets were excluded. Missing values could be attributed to the filtering process applied by submitters of the dataset. We also checked for missing values across rows. Those probe sets with missing values in more than 10% of the total RS were excluded. These probe sets mapped to RNA genes and pseudogenes (e.g. MIR4680, TEN1-CDK3, and A2MP1). Moreover, we chose RS with annotations that relate to disease state, treatment, and sampling time point, excluding organism part (e.g., different brain regions) from our analysis. Aside from missing values, we also removed those RS with too many (q-values less than 0.05 for more than 50% of the genes, e.g. GSE16385) or too few (no q-values less than 0.3, e.g. GSE12644) DE probe sets. We excluded RS (e.g. GSE13501 and GSE7753 (Spearman rho = 0.66, P < 0.001) that we deemed as outliers due to their high correlations with other RS across all probe sets (Spearman rho > 75th percentile). We believe that these highly correlated RS will always be clustered together regardless of which genes were selected and therefore are not as informative. Finally, the 19 p-value histogram distribution of DE genes are expected to be similar to a beta-uniform mixture distribution [37]. We developed a simple heuristic to evaluate this metric where we subtracted the gene counts from the thirteenth histogram bin from the gene counts of the first histogram bin. P-values were binned every 0.1. Those datasets that differ by more than 100 gene counts were excluded from our analysis. This eliminated 28 RS from the DE matrix (e.g. GSE16385 and GSE11839). Finally, we filtered the EE matrix by selecting those genes and datasets from the filtered DE matrix. 2.2.2 Tissue-enriched genes The list of tissue-enriched genes were selected from Lukk et al. [27]. Briefly, Lukk et al. integrated microarray datasets to from a final expression matrix of ∼14,000 genes times ∼5,000 samples. They com- pared each tissue group to the global mean by performing a one-way ANOVA. From this list of genes, we selected those genes that were upregulated (t-statistic greater than the 75th percentile) in the brain, muscle and hematopoietic system meta groups. We call these genes as “tissue-enriched” and annotated these genes as brain-enriched, muscle-enriched and hematopoietic system-enriched genes respectively. Moreover, the two genes (PDE4DIP and ARHGAP19) that were upregulated in more than one tissue type were excluded from our analysis. RS were grouped as brain, muscle and “other” by manually curating the sample tissue type annotation. We combined those RS with the same dataset by averaging p-values across tissue-enriched genes. Finally, the tissue-enriched DE and EE matrices have the same number of rows (844 genes) and columns (163 datasets). 2.2.3 Biclustering The Iterative Signature Algorithm (ISA) (isa2 version 0.3.1-1 R package http://cran.r-project.org/web/packages/ isa2/index.html) was used to identify modules in both the EE and DE matrices [5]. First, DE p-values were −log10 transformed prior to biclustering. Modules were created by setting the random seed to 1, number of seeds to 100 and direction up for both rows and columns. We adjusted the parameters to meet the fol- lowing criteria: first, the number of clusters must be small (< 50) and second, the size of the clusters must be reasonable (∼100 rows and ∼10 columns). DE modules were identified using a row threshold of 4 and a column threshold of 1. EE modules were identified using a row and column thresholds of 2.5 and 1.5 respectively. For both cases, removal of similar modules was performed by calling the “isa.unique” function with a correlation limit of 0.6. 2.2.4 GO enrichment Gene Ontology (GO) enrichment analysis was performed using the topGO (Version 2.8.0) Bioconductor R package (http://www.bioconductor.org/packages/2.10/bioc/html/topGO.html). We used all the human gene annotations from the org.Hs.eg.db database in Bioconductor as our background gene list. For each module, we performed the Biological Process GO enrichment by selecting all the genes in the module as our input genes. The classic algorithm was run using the Fisher’s Exact Test as the test statistic. The classic algorithm scores each GO group independently of its neighbouring GO groups and is also independent of the test statistic used [1]. The top (most significant) GO group was assigned to each module. 20 2.2.5 Statistical analysis Agglomerative hierarchical clustering was performed using theWard method with Euclidean distances [13]. In each iteration, the Ward method minimizes the variance within newly formed clusters. Correlations were calculated using Spearman rank correlations and the default two sided alternative hypothesis was used for calculating Spearman rho p-values. 2.3 Results 2.3.1 Experimental design and analysis Our workflow is summarized in Figure 2.1. We performed strict quality checks and selected high quality hu- man microarray datasets deposited in Gemma, a database and framework for analyzing expression profiling studies (see Methods) [46]. Initially, the DE matrix had 64,260 genes (64,594 probe sets) across 1,298 RS. Further data processing and quality checks reduced the matrix to 12,881 genes (12,892 probe sets) and 180 RS (see Methods). Genes with two probe sets included CCR2, DDX39B, EIF2D, FXYD6-FXYD2, HSFX1, MICA, RAD21L1, RBMXL1, SBF1P1, TRMT1L and ZNF559-ZNF177. We obtained the corresponding EE matrix by measuring the corresponding gene expression levels in each dataset. In our first analysis, we analysed the tissue-enriched subset of each matrix (844 genes) (see Methods). Next, using all 12,881 genes, we identified EE and DE modules by applying the ISA biclustering algorithm in an unsupervised manner. Each module was then annotated with a biological processes by applying GO enrichment analysis. 2.3.2 Tissue-enriched gene expression and differential expression We asses the quality of the datasets by clustering gene expression and differential expression of tissue- enriched genes. We hypothesize that tissue-enriched genes are highly expressed in datasets that use the corresponding tissue as sample source. From the study of Lukk et al. [27], we found 844 tissue-enriched genes in our dataset (526 brain-enriched genes, 214 heamtopoeitic system-enriched genes and 104 muscle- enriched genes). In the EE matrix, there are 15 (9%) brain datatasets, 11 (7%) muscle datatasets and 137 (84%) “other” datatasets (Tables 2.1, 2.2 and 2.3 respectively). We found similar proportions of RS in the DE matrix (16 brain RS, 11 muscle RS and 153 “other” RS). Our results show that the EE matrix reflects experimental source tissue type more so than the DE matrix. As shown in Figure 2.2a, muscle-enriched genes such as MYL3, FXYD1, and DES are highly expressed in datasets where muscles are used as the tissue source (e.g. GSE9103 and GSE1551; Table 2.2) with low expression in non-muscle datasets. Likewise, brain-enriched genes such as S100B, NEFL, and GRIA2 are highly expressed in datasets where nervous tissues or cell lines are used (e.g. GSE1993 and GSE21858; Table 2.1) with low expression in non-nervous related datasets. This reflects the high-quality of our datasets such that the majority of tissue-enriched genes are highly expressed in datasets with similar tissue anno- tations. However, there are a few muscle-enriched genes PDK2, PTP4A3, TMOD1, brain-enriched genes CALM3, PPP3CA, PPP3CB, CNP, B3GNT1, MAPK8IP3, and hematopoietic system-enriched genes such as RAB8A, DDX5, GNAI3 which are highly expressed in almost all datasets, except for a few cancer and 21 Figure 2.1: Experimental design and analysis GEO.ID Tissue source Sample Size GSE1993 Glioblastoma and Astrocytoma frozen primary human tissue 65 GSE21858 Frontal and temporal cortex 8 GSE5388 Human postmortem brain tissue 61 GSE11208 Peripheral ganglion 11 GSE5389 Human post-mortem brain tissue 21 GSE12460 Neuroblastic tumor 64 GSE24072 Fresh glioma tissue 32 GSE4773 SK-N-MC neuroblastoma cells 21 GSE1297 Hippocampal CA1 tissue 31 GSE19728 Normal brain tissue, glioblastoma, and astrocytoma 21 GSE17440 Frontal cortex 8 GSE7621 Postmortem human substantia nigra 25 GSE20168 Postmortem brain prefrontal cortex 29 GSE20141 Substantia Nigra pars compacta 18 GSE2732 Human brain neuronal SH-SY5Y cell lines 18 Table 2.1: Nervous tissue datasets. These datasets involve brain tissues or neuronal cell lines. Tissue sources were manually curated from GEO. 22 GEO.ID Tissue source Sample Size GSE9103 quadriceps (Vastus Lateralis) muscle biopsy samples 40 GSE10161 cardiac biopsies 27 GSE1551 muscle biopsies 23 GSE24235 biceps brachii muscle biopsies 28 GSE1869 human heart 37 GSE3112 muscle biopsies 40 GSE11686 arm (Extensor and Flexor carpi radialis) muscles 16 GSE13070 quadriceps (Vastus Lateralis) muscle 364 GSE6798 quadriceps (Vastus Lateralis) muscle 29 GSE1145 human heart 107 GSE14901 quadriceps 72 Table 2.2: Muscle tissue datasets. Tissue sources were manually curated from GEO. GEO.ID Name RS GSE16844 Integrated pathways for neutrophil recruitment and inflamma- tion in leprosy erythema nodosum lep- rosum VS Lepromatous Leprosy GSE12860 Antirheumatic Drug Response in Human Chondrocytes: Po- tential Molecular Targets to Stimulate Cartilage Regeneration Rheumatoid arthritis syn- ovial fibroblasts vs. Nor- mal donor synovial fibrob- lasts GSE15132 Riboflavin depletion impairs cell proliferation in intestinal cells: Identification of mechanisms and consequences Control VS Ribodeficient GSE9971 CYP3A5 Gene Expression is Associated with Early Recur- rence of Non-small Cell Lung Cancer recurrent vs non-recurrent (control) non-small cell lung cancer GSE15132 Riboflavin depletion impairs cell proliferation in intestinal cells: Identification of mechanisms and consequences 24 H VS 48 H VS 72 H GSE26713 Integrated transcript and genome analyses reveal NKX2-1 and MEF2C as potential oncogenes in T-ALL normal bone marrow con- trol vs pediatric T-ALL Table 2.3: Examples of datasets and RS from “other” tissues. These datasets were not classified as brain nor muscle. inflammatory related datasets (e.g. GSE16844 and GSE12860; Table 2.4). These genes may be highly expressed in more than one tissue type under normal conditions. It may also be caused by mutations that upregulate gene expression. In contrast to the EE matrix, it is not immediately obvious that tissue-enriched genes are differentially expressed in those RS that use the same tissue type (Figure 2.2b). At first glance, the EE and DE matrices do not seem to have anything in common. However, a closer inspection of the heatmap shows that the hematopoietic-system enriched cluster which corresponds to the bottom EE cluster, has more differentially expressed genes compared to genes in other clusters. On the other hand, most brain-enriched genes (top cluster) show little expression and are not DE in most datasets. Next, we calculated the Spearman rank 23 (a) EE matrix (b) DE matrix Figure 2.2: EE vs DE tissue-enriched matrices. (a) EE values are the relative average expression. EE values were binned from 0 (low) to 1 (high). EE rows and columns were clustered using the Ward method. (b) DE values were −log10 transformed. DE values were binned from 0 (low) to 2.5 (high). DE rows and columns have the same order as that of the EE heatmap in (a). GEO.ID Name Tissue source GSE13849 Expression Signatures in Polyarticular JIA Show Heterogeneity and Offer a Molecular Classification of Disease Subsets blood GSE2405 NIH/NIAID Neutrophil Response to A. phagocytophilum leukocytes GSE20266 Salivary Transcriptomic and Proteomic Biomarkers for Breast Cancer De- tection saliva GSE22377 mRNA expression data from human adenocarcinomas of the stomach intestine GSE11341 Lung selective gene responses to alveolar hypoxia lung GSE28796 Gene expression profiles of pretreatment biopsies from dose-dense- docetaxel-treated breast cancers breast Table 2.4: Cancer-related datasets that have low hematopoietic gene expression. Tissue sources were manually curated from GEO. 24 Dataset correlation Fr eq ue nc y −0.2 0.0 0.2 0.4 0.6 0 10 20 30 Figure 2.3: The distribution of Spearman rank correlations between EE and DE datasets. correlation between matching EE and DE datasets across genes. Figure 2.3 shows the correlation distribution is positively skewed. We interpret this as more datasets show agreement in both EE and DE levels than not. 2.3.3 Modules enriched for biological processes We hypothesize that genes and experimental datasets cluster together in modules because genes are either expressed in the same tissue type or share a common functional pathway. From the matrix of 12,881 genes, we are interested in finding modules that represent a common biological process. An EE module is a cluster of genes with high expression values across a subset of datasets while a DE module is a cluster of genes that are significantly differentially expressed across a subset of RS. A close inspection of these modules may provide novel insights toward genes or disease states. Traditional clustering methods such as hierarchical clustering only allows one gene to exclusively belong to a single cluster. Moreover genes are clustered using all samples in the matrix while genes can be expressed or differentially expressed in only a subset of conditions. To address these issues, we applied the ISA biclustering algorithm [5]. Biclustering is a technique that overcomes these limitations by simultaneously clustering rows and columns. ISA has been used to identify regulatory modules (a sub-cluster of genes and samples) in the yeast transcriptional network [5]. The algorithm uses a set of random genes as input and iteratively selects genes and samples that are significantly different based on a threshold. The algorithm ends when the average expression across genes and samples have become very similar as indicated by a 25 GO.ID Term Significant classicFisher Module.ID Rows Cols GO:0006936 muscle contraction 50 < 1e-30 3 175 16 GO:0006955 immune response 53 < 1e-30 4 148 19 GO:0019226 transmission of nerve impulse 36 4.7e-26 1 149 14 GO:0006968 cellular defense response 2 0.0016 2 39 14 Table 2.5: Top GO biological process in each module from the EE matrix. The table is sorted by increasing p-value from the Fisher’s Exact Test statistic for gene over representation. The number of significant genes found in each group are shown. Rows represent the number of genes and Cols represent the number of datasets in each module. high Pearson correlation. These set of genes and samples are now part of the same module. We applied the ISA algorithm to both EE and DE matrices separately. Finally, we applied GO enrichment analysis to each module for biological interpretation. EE modules cluster by source tissue type We identified four EE modules after applying ISA on the EE matrix (Table 2.5). On average, most modules have over 100 genes and over 10 datasets. Figure 2.4 displays the hierarchical clustering of EE modules based on overlapping genes. This clustering shows that there is greater overlap between brain and muscle related genes than hematopoietic system genes which is consistent with the clustering of biological groups in Supplementary Figure 4a of Lukk et al. [27]. We have chosen to take a closer look at Module 3, the muscle contraction module (GO:0006936) as an example. The Top 5 GO annotations for this module is available in Table 2.6. The expression levels of genes within this module is more homogeneous relative to randomly chosen genes outside this module (Fig- ure 2.5). Datasets that belong to this module involve muscle or heart tissues (e.g., GSE1551 and GSE13070) with a few exceptions (Table 2.7). These exceptions include cancer-related datasets such as GSE2405 (from leukocytes), GSE22377 (from intestine), GSE11341 (from lung cells) and GSE28796 (from breast) (Ta- ble 2.4). In addition to the muscle-enriched genes (e.g., MYH6, TTN and MYL2), we also identified genes that are known to be highly expressed in the brain such as CAMK2A, AQP4, and S100A1 and other genes that are not muscle-enriched (e.g., HSPB2, PPP1R1A and EBF2). This is perhaps due to the involvement of calcium signalling pathways in both muscle contraction and neuronal transmission [6]. DE modules have diverse biological processes We hypothesize that differential expression can offer additional insights toward gene function in addition to tissue type specificity. To test this, we first applied the same biclustering parameters to the DE matrix (see Methods). This resulted in 33 DE modules, which on average have ∼387 genes and ∼3 RS. These modules have approximately three times the number of genes compared to the EE modules. This makes it difficult to meaningfully compare between EE and DE modules. To circumvent this, we applied stricter biclustering parameters to the DE matrix and found 37 modules with comparable module sizes. Table 2.8 shows the top GO annotations for each module. Our results show that there are more DE modules compared 26 cellular defense response | GO:0006968 | 02 immune response | GO:0006955 | 04 transmission of nerve impulse | GO:0019226 | 01 muscle contraction | GO:0006936 | 03 1.422 1.426 1.430 hclust (*, "w a rd") M odule Height Figure 2.4: Clustering of GO-enriched EEmodules. Tree leaves are labelled with the following format: GO Term | GO.ID |Module.ID. GO.ID Term Significant classicFisher bicluster GO:0006936 muscle contraction 50 < 1e-30 3 GO:0003012 muscle system process 51 < 1e-30 3 GO:0006941 striated muscle contraction 26 < 1e-30 3 GO:0061061 muscle structure development 38 1.3e-29 3 GO:0007517 muscle organ development 35 1.0e-28 3 GO:0003008 system process 65 3.1e-22 3 GO:0003009 skeletal muscle contraction 11 2.1e-18 3 GO:0050879 multicellular organismal movement 12 8.4e-18 3 GO:0050881 musculoskeletal movement 12 8.4e-18 3 GO:0014706 striated muscle tissue development 21 6.8e-17 3 Table 2.6: Top 5 GO annotations for the muscle contraction EE module 27 GEO.ID Description RS GSE9103 Skeletal Muscle Transcript Profiles in Trained or Sedentary Young and Old Subjects sedentary VS trained GSE10161 Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass aortic stenosis vs. healthy control GSE1551 dermatomyositis dermatomyositis VS healthy GSE24235 Skeletal muscle gene expression in response to resistance ex- ercise: sex specific regulation resting vs 24 hrs post acute resistance exercise GSE1869 Ischemic and Nonischemic CM and NF Hearts non-ischemic vs ischemic cardiomyopathy GSE13849 Expression Signatures in Polyarticular JIA Show Hetero- geneity and Offer a Molecular Classification of Disease Sub- sets Healthy (control group) vs. juvenile idiopathic arthritis GSE3112 Plasma Cells in Muscle in Inclusion Body Myositis and Polymyositis inclusion body myositis VS polymyositis VS nor- mal control GSE11686 Unique Transcriptional Profile in Wrist Muscles From Cere- bral Palsy Patients cerebral palsy VS healthy control GSE13070 Human Insulin Resistance and Thiazolidinedione-Mediated Insulin Sensitization preClamp vs postClamp vs noClamp GSE6798 Reduced expression of mitochondrial oxidative metabolism genes in skeletal muscle of women with PCOS control vs insulin- resistant polycystic ovary syndrome *GSE2405 NIH/NIAID Neutrophil Response to A. phagocytophilum control vs anaplasma phagocytophilum GSE1145 changes in cardiac transcription profiles brought about by heart failure ischemic, idiopathic di- lated and normal hearts GSE14901 Limb immobilization induces a coordinate down-regulation of mitochondrial and other metabolic pathways in men and women pre or post-cast *GSE22377 mRNA expression data from human adenocarcinomas of the stomach diffuse adenocarcinoma vs intestinal adenocarci- nomas *GSE11341 Lung selective gene responses to alveolar hypoxia Normoxia VS 3 H Hy- poxia VS 24 H Hypoxia VS 48 H Hypoxia *GSE28796 Gene expression profiles of pretreatment biopsies from dose- dense-docetaxel-treated breast cancers pathological complete re- sponse (pCR) vs residual disease (NR) Table 2.7: Muscle contraction EE module RS. All datasets use muscle or heart as source tissue except for datasets marked with *. 28 0.2 0.6 1 Value 80 0 18 00 Color Key and Histogram Co un t module 3 Figure 2.5: Muscle contraction EE module. Rows are genes and columns are datasets. Those genes and datasets that belong to the module are highlighted in black. Genes and datasets that do not belong to the module were randomly chosen. to EE modules which reflects the increased variation found in experimental conditions relative to tissue types. For example, we find GSE9103 and GSE1551 in two separate DE modules (DE Module 18 and DE Module 1 respectively) even though both datasets are found in only one EE module (Module 3). Finally, similar to the EE modules, we clustered the DE modules based on gene overlap as shown in Figure 2.6. In comparison to the GO EE hierarchy, the GO DE hierarchy also includes a module enriched in “immune response genes” (GO:0006955). Moreover, the GO DE hierarchy also includes modules related to the “M phase” (GO:0000279) and “response to DNA damage stimulus” (GO:0006974) which suggests the involvement of a common set of genes with respect to the “immune response genes” enriched module. We observe a few outliers as well (“generation of precursor metabolites and energy” (GO:0006091) and “translational elongation” (GO:0006414)). These biological processes might involve a specialized set of genes that is different from those of other biological processes. As a use case, we selected DE module 18 (GO:0006091, generation of precursor metabolites and en- ergy). To visualize the quality of this module’s clustering, Figure 2.7 shows the differential expression of all genes in this module compared a random set of genes of the same size. The top 5 GO annotations for this module is available in Table 2.9 which include closely related biological processes such as “oxidative phosphorylation” and “cellular respiration”. Similar to the muscle contraction EE module before, this DE module also includes a few muscle-related RS such as GSE9103, GSE10161, GSE11686, and GSE14901 29 GO.ID Term Significant Pval Module Rows Cols GO:0006414 translational elongation 49 < 1e-30 36 87 23 GO:0006091 generation of precursor metabolites and energy 54 < 1e-30 18 114 19 GO:0000279 M phase 49 < 1e-30 10 98 23 GO:0006955 immune response 51 < 1e-30 1 143 14 GO:0007156 homophilic cell adhesion 15 2.5e-19 13 56 16 GO:0007586 digestion 13 1.9e-17 11 62 4 GO:0034470 ncRNA processing 13 3.7e-16 33 41 19 GO:0006695 cholesterol biosynthetic process 8 1.8e-15 29 35 13 GO:0006955 immune response 29 4.7e-15 34 110 12 GO:0006334 nucleosome assembly 8 6.5e-10 2 79 3 GO:0048259 regulation of receptor-mediated endo- cytosis 6 1.3e-09 12 88 4 GO:0007186 G-protein coupled receptor protein signalng pathway 15 2.8e-09 3 78 12 GO:0006974 response to DNA damage stimulus 16 4.3e-09 19 112 2 GO:0016339 calcium-dependent cell-cell adhesion 5 1.9e-08 24 61 13 GO:0048869 cellular developmental process 31 3.1e-08 20 97 1 GO:0009611 response to wounding 15 2.9e-07 15 83 12 GO:0006915 apoptosis 28 6.8e-07 14 148 5 GO:0019219 regulation of nucleobase-containing compound metabolic process 22 1.6e-06 37 60 16 GO:0007154 cell communication 18 8.6e-06 9 68 15 GO:0050994 regulation of lipid catabolic process 3 2.0e-05 26 31 7 GO:0065008 regulation of biological quality 24 5.1e-05 31 101 2 GO:0042776 mitochondrial ATP synthesis coupled proton transport 3 7.5e-05 5 114 4 GO:0001539 ciliary or flagellar motility 3 7.7e-05 17 122 4 GO:0007267 cell-cell signaling 12 8.2e-05 25 71 4 GO:0006887 exocytosis 6 0.00014 30 96 3 GO:0051967 negative regulation of synaptic trans- mission, glutamatergic 2 0.00014 16 55 12 GO:0007215 glutamate signaling pathway 3 0.00014 32 77 6 GO:0009987 cellular process 83 0.00037 8 114 5 GO:2000027 regulation of organ morphogenesis 4 0.00039 35 93 4 GO:0010739 positive regulation of protein kinase A signaling cascade 2 0.00041 7 113 1 GO:0045449 regulation of transcription 21 0.00048 4 71 5 GO:0022617 extracellular matrix disassembly 2 0.0005 23 112 1 GO:0010467 gene expression 39 0.00056 22 122 2 GO:0006814 sodium ion transport 5 0.0012 28 114 5 GO:0021761 limbic system development 3 0.0013 6 82 2 GO:0007601 visual perception 6 0.0017 21 121 2 GO:0007283 spermatogenesis 4 0.0049 27 70 10 Table 2.8: Top GO biological process in each module from the DE matrix. The table is sorted by increasing p-value from the Fisher’s Exact Test statistic for gene over representation. Significant column indicates the number of significant genes in the module that were found in the GO group. 30 generation of precursor metabolites and ... | GO:0006091 | 18 translational elongation | GO:0006414 | 36 cell communication | GO:0007154 | 09 regulation of nucleobase, nucleoside, nu... | GO:0019219 | 37 extracellular matrix disassembly | GO:0022617 | 23 spermatogenesis | GO:0007283 | 27 response to wounding | GO:0009611 | 15 regulation of organ morphogenesis | GO:2000027 | 35 negative regulation of synaptic transmis... | GO:0051967 | 16 visual perception | GO:0007601 | 21 cellular developmental process | GO:0048869 | 20 positive regulation of protein kinase A ... | GO:0010739 | 07 digestion | GO:0007586 | 11 G−protein coupled receptor protein signa... | GO:0007186 | 03 sodium ion transport | GO:0006814 | 28 glutamate signaling pathway | GO:0007215 | 32 regulation of transcription | GO:0045449 | 04 calcium−dependent cell−cell adhesion | GO:0016339 | 24 gene expression | GO:0010467 | 22 nucleosome assembly | GO:0006334 | 02 mitochondrial ATP synthesis coupled prot... | GO:0042776 | 05 regulation of lipid catabolic process | GO:0050994 | 26 homophilic cell adhesion | GO:0007156 | 13 regulation of biological quality | GO:0065008 | 31 ciliary or flagellar motility | GO:0001539 | 17 cell−cell signaling | GO:0007267 | 25 exocytosis | GO:0006887 | 30 regulation of receptor−mediated endocyto... | GO:0048259 | 12 apoptosis | GO:0006915 | 14 cellular process | GO:0009987 | 08 ncRNA processing | GO:0034470 | 33 immune response | GO:0006955 | 01 immune response | GO:0006955 | 34 limbic system development | GO:0021761 | 06 cholesterol biosynthetic process | GO:0006695 | 29 M phase | GO:0000279 | 10 response to DNA damage stimulus | GO:0006974 | 19 1.1 1.2 1.3 1.4 1.5 1.6 1.7 hclust (*, "w a rd") M odule Height Figure 2.6: Clustering of GO-enriched DE modules. Tree leaves are labelled with the following for- mat: GO Term | GO.ID |Module.ID (Table 2.10) and muscle-enriched genes such as ACTN2, CPT1B, and CKMT2. We also find brain-related RS in this module such as GSE5388, GSE1297, and GSE2732 (Table 2.10). We suspect that some of these genes are involved in both metabolism and neuronal systems. To verify this, we compared our list of DE genes in Module 18 with those genes from another study that reported a list of proteins linked to metabolic abnormalities in the dorsolateral prefrontal cortex of bipolar disorder post-mortem brain tissues [36]. As ex- pected, we found 5 out of 46 Pennington et al. genes in common (ATP5B, ATP5C1, ATP5D, UQCRC1, and SUCLA2; P< 0.0001, hypergeometric). Since these genes are also DE in non-brain related RS (Table 2.10), these genes are probably involved in different pathways as well. 31 0 1 2 3 Value 0 10 00 Color Key and Histogram Co un t module 18 Figure 2.7: Generation of precursor metabolites DE module. Values correspond to −log10(P-value). Rows are genes and columns are RS. Those genes and RS that belong to the module are high- lighted in black. Genes and RS that do not belong to the module were randomly chosen. GO.ID Term Significant classicFisher bicluster GO:0006091 generation of precursor metabolites and ... 54 < 1e-30 18 GO:0006119 oxidative phosphorylation 30 < 1e-30 18 GO:0045333 cellular respiration 27 < 1e-30 18 GO:0022900 electron transport chain 26 < 1e-30 18 GO:0015980 energy derivation by oxidation of organi... 28 < 1e-30 18 GO:0022904 respiratory electron transport chain 20 1.20E-028 18 GO:0042773 ATP synthesis coupled electron transport 19 4.90E-028 18 GO:0042775 mitochondrial ATP synthesis coupled elec... 19 4.90E-028 18 GO:0055114 oxidation reduction 35 7.30E-023 18 GO:0006120 mitochondrial electron transport, NADH t... 15 1.20E-022 18 Table 2.9: Top 5 GO annotations for the generation of precursor metabolites DE module. 32 GEO.ID Description RS GSE15132 Riboflavin depletion impairs cell proliferation in intestinal cells: Identification of mechanisms and consequences Control VS Ribodeficient GSE9103 Skeletal Muscle Transcript Profiles in Trained or Sedentary Young and Old Subjects sedentary VS trained GSE10161 Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass aortic stenosis vs. healthy control GSE5388 Adult postmortem brain tissue (dorsolateral prefrontal cortex) from subjects with bipolar disorder and healthy controls Control group VS Bipolar Disorder GSE6927 Gingival Epithelial Cell Transcriptional Responses to Com- mensal and Opportunistic Oral Microbial Species. affected vs control group GSE2443 Prostate cancer - comparison of androgen-dependent vs androgen-independent prostate cancer GSE12288 Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease Coronary artery disease vs. healthy GSE11686 Unique Transcriptional Profile in Wrist Muscles From Cere- bral Palsy Patients cerebral palsy VS healthy control GSE9006 Gene expression in PBMCs from children with diabetes sampling timepoint GSE5900 Gene Expression of Bone Marrow Plasma Cells from Healthy Donors (N=22), MGUS (N=44), and Smoldering Myeloma (N=12) Control (healthy) vs. smoldering myeloma GSE25518 Testis developmental gene expression in cryptorchid boys at risk of azoospermia cryptorchidism vs control GSE29605 Gene expression data from chronic lymphocytic leukemia samples control vs mutated IgVH GSE14901 Limb immobilization induces a coordinate down-regulation of mitochondrial and other metabolic pathways in men and women pre or post-cast GSE13762 Comparative gene expression profile of 1,25- dihydroxyvitamin D3-treated human monocyte-derived dendritic cells Vehicle Treated VS 1,25 dihydroxyvitamin D3 GSE30499 Inhibition of nonsense-mediated RNA decay by the tumor mi- croenvironment promotes tumorigenesis 0 vs 1.5 vs 3 vs 4.5 hr GSE21942 Expression data from peripheral blood mononuclear cells in multiple sclerosis patients and controls Multiple sclerosis vs con- trol group GSE1297 Incipient Alzheimer’s Disease: Microarray Correlation Anal- yses Control vs. AD GSE16581 Genomic landscape of meningiomas: gene expression benign vs anaplastic GSE2732 Global gene expression pattern of human brain neuronal (SH- SY5Y) cell lines exposed to sarin (GB). Sarin Treated VS Control group Table 2.10: Generation of precursor metabolites DE module RS 33 2.4 Discussion Microarray experiments are often designed to identify genes that are involved in a particular biological phenomenon. For example, genes that are differentially expressed between blood samples from control and Parkinson’s disease patients may play an important role in the disease. However, which of these DE genes are also differentially expressed in other neurological disease such as Alzheimer’s disease is also of interest. To this end, we have integrated hundreds of curated human microarray datasets and investigated expres- sion and differential expression of genes across datasets and result sets. Our preliminary results show that as expected, tissue-enriched genes are highly expressed in those samples with the same tissue type. With respect to modules, there are fewer EE modules than DE modules. We attribute differences in the biologi- cal process of EE modules to tissue type variation. In contrast, there are more differences in experimental conditions and this is reflected in the biological process of each DE module. We highlight possible extensions to our current work. First, most of the datasets in our study are related to cancer. As more datasets from studies such as drug-effects, neurological and developmental disorders become available, re-analysis of additional datasets may uncover new relationships between different bio- logical pathways. Second, our current work can be applied to gene expression studies of model organisms such as mouse as well. Model organisms allow researchers to discover gene function through controlled genetic manipulations which are not possible in human. Third, the corresponding fold change of differen- tially expressed genes can be incorporated. Genes that change expression in the same direction could be co-regulated by the same transcription factors. Fourth, the interpretation of differentially expressed genes can be confounded by experimental artifacts. Examples of experimental artifacts include differences in reagents, equipment, or the time the experiment was conducted. Batch correction can be incorporated dur- ing data processing or experimental designs can be revisited during data analysis. In our current work, we used the ISA biclustering algorithm for identifying modules in our data. While other methods could also be applied, we focused on designing a simple workflow for discovering biological knowledge from the data that is currently available. In summary, we have provided a glimpse into gene expression and differential expression of many seemingly unrelated datasets and experimental conditions in humans. 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