Karen Schlauch

Photo of Karen Schlauch

Director, Nevada Center for Bioinformatics
Department of Biochemistry and Molecular Biology
University of Nevada/Mail Stop 330
1664 N. Virginia Street
Reno,  Nevada   89557

Office: (775) 784-6236
Fax: 784-1419

Building: Howard Medical Science,  Office 146A

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BS: University of Illinois, 1989 Mathematics/Theory of Computation
MS: Eastern Illinois University, 1991 & New Mexico State University, 1994 Mathematics
PhD: New Mexico State University, 1998 Mathematics


Statistical Methods of Analyzing Gene Expression Data

Expression data are generated by hybridizing transcripts to microarrays or gene chips from tissues under controlled conditions. If one gene regulates (up or down) another gene, or both are involved in a biochemical pathway, the profile of their expressions over time will correlate. Expression data are often analyzed using clustering procedures: clusters represent sets of genes displaying coordinately regulated expression profiles. As expression data contain significant amounts of random variation, and as clusters are dependent on the procedure applied, the assignment of confidence measures to clusters is useful. Specifically, we have implemented an algorithm in the statistical programming language R that assigns confidence measures to groupings of genes obtained by clustering routines. By the use of permutation testing and convex hull methods to simulate pseudo-random gene expression data sets, statistics are obtained from these randomly generated sets to provide a basis for comparison to the original data.

My contribution to the GeneX OpenSource gene expression database and software system [] consists of several gene expression normalization and analysis programs, two of which presents a novel approach to clustering techniques. These methods are being generalized for applications to microarray data generated on different technology platforms (Affymetrix, NimbleGen™, and custom two-color cDNA arrays). Enhancements are being made to include metrics that provide the researcher with (more) biologically meaningful results.

Experimental Design and Normalization Methods

As the accumulation of genetic data continues to grow at a rapid speed, there is a need for immediate data analysis methods to assess experiments as they are in progress. Properties of the experimental design, which provide control and understanding of the source of variation in both signal and noise, affect the manner in which data should be analyzed and appropriate models constructed. The ultimate aim of any gene expression data analyst is to be involved in the experimental design of the microarray. Too often, experiments are placed in the analysts' hands without proper design. Poorly designed experiments most often result in meaningless analysis results, and always increase the efforts (and creativity) of the analyst. I am currently developing several experimental designs of plant and human array experiments, with several sets of both positive and negative controls, and am assessing their performance within different experiments.

Graph-theoretic Modeling of Temporal Gene Expression Data

The analysis of large amounts of microarray data is a significant challenge for the researcher. The parallel assay of thousands of data points, not all of which are independent, across a number of temporal states, provides an interesting platform for statistical analyses and the construction of models. To identify clusters within temporal gene expression profiles is equivalent to finding patterns in time series data. Although standard hierarchical clustering techniques can be applied to this type of data, no standard tools to identify such patterns exist. I have developed a graph-theoretic approach for constructing putative functional network models that suggest hypotheses about functions of unknown genes. This technique has been applied to several experiments of Dr. John Cushman at the University of Nevada Reno, with promising results. Specifically, the experiments measure the expression levels of the common ice plant, Mesembryanthemum crystallinum, under abiotic stress. Ice plant is a facultative halophyte, which can shift from C3 to Crassulacean acid metabolism (CAM) photosynthesis in response to environmental stress conditions such as water stress or conditions of hypersalinity. By understanding the complex adaptive mechanisms of this plant, a long-term goal is the deployment of these processes in agriculturally important crops to improve drought and salinity tolerance. An innovative distance metric is under development to provide a measure of similarity between any pair of genes in a more biologically grounded manner than commonly utilized distance metrics. Using these similarity relations, a bi-directional graph is generated by connecting genes based on their degree of similarity. From this graph one can detect "clusters" within the structure of the graph’s connectivity. These clusters provide hypotheses of gene function and interaction, and guide in the association of genes with biochemical pathway changes involved in stress responses and adaptive mechanisms of the organism under study. An on-going study focuses also on the post-analysis findings and the biological meaning behind clusters, an often-neglected step in microarray analysis.

Modeling Gene Interactions with Combinatorial Methods

Complex networks are often used to model hierarchical social, biological or communication systems, as well as genetic systems. As a first approximation, Boolean networks are often used. As part of my research at the Virginia Bioinformatics Institute with Professor Reinhard Laubenbacher, we developed a method of encoding a Boolean network as a collection of simplicial complexes. We also established a combinatorial analogue of the homotopy theory of topological spaces to analyze these simplicial complexes. The resulting combinatorial invariants provide information on the dynamics of the network. By representing genetic relationships via (Boolean) network structures, applications of combinatorial homotopy theory may reveal overall network behavior and patterns of influence within and across gene subgroups.

Visualization of Microarray Gene Expression Data

An artificial heatmap of the intensity levels of a 2-color cDNA microarray is generated for each channel, and for the background-corrected ratio values. This image allows the user to quickly determine whether any spatial variation appears on the array, or whether control spots are behaving as predicted. Similarly, the tool is applicable to high density oligonucleotide arrays, such as those made by Affymetrix and Nimblegen™. This technique provides the researcher with a bird's eye view of each array in the experiment. The software is written in the R programming language, and is very simple to use and implement.

Visualization of Haplotype Sharing and Fine Mapping using SNP Data

For the analysis of data stemming from our high-throughput genotyping experiments, we have developed a tool that automates the selection of SNPs for fine-mapping genetic associations. The tool generates a graph of genotypes from phased chromosomes that are grouped by haplotype via a hierarchical clustering approach to display long-range linkage disequilibrium patterns for a given allele of interest. We are currently using phased chromosome data from the HapMap project, and among other things, highlight those SNPs included on the Affymetrix 100K SNP GeneChip. These graphs make it possible to identify the haplotypes on which an associated SNP occurs and identify the region likely to contain the causative variant for a given association.

A separate module within HapMapper identifies SNPs that serve to distinguish haplotypes, as well as those in strong linkage disequilibrium with an associated allele, and those that are proxies for other SNPs in the region. These data are integrated into the visual display, aiding in the selection of SNPs for fine mapping haplotypes that contain the associated allele. The software is written in R and has been implemented for our use in fine-mapping several regions of interest.


Rattanakon S, Ghan R, Gambetta GA, Deluc LG, Schlauch KA, Cramer GR. 2016, Abscisic acid transcriptomic signal varies with grapevine organ., BMC Plant Biol.;16(1):72. PMID:27001301 PMCID: PMC4802729  
Schlauch KA, Khaiboullina SF, De Meirleir KL, Rawat S, Petereit J, Rizvanov AA, Blatt N, Mijatovic T, Kulick D, Palotás A, Lombardi VC. 2016, Genome-wide association analysis identifies genetic variations in subjects with myalgic ncephalomyelitis/chronic fatigue syndrome., Transl Psychiatry 2016 Feb 9. PMID: 26859813  
Petereit J, Smith S, Harris FC Jr, Schlauch KA. 2016, Petal: Co-expression network modelling in R., BMC Syst Biol.;10 Suppl 2:51. PMID: 27490697.  
Hopper DW, Ghan R, Schlauch KA, Cramer GR. 2016, Transcriptomic network analyses of leaf dehydration responses identify highly connect ABA and ethylene signaling hubs in three grapevine species differing in drought tolerance., BMC Plant Biol.;16(1):118. PMID: 27215785.  
Izuora K, Ezeanolue E, Schlauch K, Neubauer M, Gewelber C, Umpierrez G. 2015, Impact of periodontal disease on outcomes in diabetes., Contemporary Clinical Trials 41, 93-99. PMID: 25623292 PMCID: PMC4380752  
Ulrich CC, Quilici DR, Schlauch KA, Burkin HR, Buxton IL. 2015, LC/MS/MS data analysis of the human uterine smooth muscle S-nitrosoproteome fingerprint in pregnancy, labor, and preterm labor., Data Brief. Aug 1;4:591-4. PMID: 26322325. PMCID: PMC4543089  
Kaur G, Markley B, Schlauch K, Izuora KE. 2015, Outcomes of Less Intensive Glycemic Target for a Subcutaneous Insulin Protocol in Hospitalized Patients., Am J Med Sci. 2015 Dec;350(6):442-6. PMID: 26445303  
Ulrich C, Quilici DR, Schlauch KA, Buxton IL. 2015, Proteomic network analysis of human uterine smooth muscle in pregnancy, labor, and preterm labor., Integr Mol Med. Aug;2(4):261-269. PMID: 26413312 PMCID: PMC4582795  
Fennell AY, Schlauch KA, Gouthu S, Deluc LG, Khadka V, Sreekantan L, Grimplet J, Cramer GR, Mathiason KL. 2015, Short day transcriptomic programming during induction of dormancy in grapevine., Front Plant Sci. 4;6:834. PMID: 26582400 PMCID: PMC4632279  
Borland AM, Hartwell J, Weston DJ, Schlauch KA, Tschaplinski TJ, Tuskan GA, Yang X, Cushman JC. 2014, Engineering crassulacean acid metabolism to improve water-use efficiency., Trends Plant Sci. 2014 May;19(5):327-38. doi: 10.1016/j.tplants.2014.01.006.  
Tillett RL, Wheatley MD, Tattersall EA, Schlauch KA, Cramer GR, Cushman JC. 2012, The Vitis vinifera C-repeat binding protein 4 (VvCBF4) transcriptional factor enhances freezing tolerance in wine grape., Plant Biotechnology Journal 2012 10, pp. 105–124.  
Tillett RL, Erguel A, Albion R, Schlauch KA, Cramer GR, Cushman JC. 2011, Identification of tissue-specific, abiotic stress responsive gene expression patterns in wine grape (Vitis vinifera L.) based curation and mining of large-scale EST data sets., BMC Plant Biology 2011, 11:86.  
Mittler, T, Levy, M, Feller, C, Schlauch, K, 2010, MULTBLAST: A web application for multiple BLAST searches, Bioinformation, 5 (5) 224-226, 2010  
Ad N, Henry L, Schlauch K, Holmes SD, Hunt S., 2010, The CHADS score role in managing anticoagulation after surgical ablation for atrial fibrillation., The Annals of Thoracic Surgery, 90(4):1257-62.  
Kuhn AR, Schlauch K, Lao R, Halayko AJ, Gerthoffer WT, Singer CA. 2009, MicroRNA Expression in Human Airway Smooth Muscle Cells: Role of miR-25 in Regulation of Airway Smooth Muscle Phenotype., American Journal of Respiratory Cell and Molecular Biology 2009 Jun 18.  
Miller G, Schlauch K, Tam R, Cortes D, Torres MA, Shulaev V, Dangl JL, Mittler R. 2009, The plant NADPH oxidase RBOHD mediates rapid systemic signaling in response to diverse stimuli., Science Signal 18;2(84):ra45.  
Book or Chapter(s) in Books
Schlauch, KA, Grimplet, J, Cushman JC, Cramer, GC. 2011, Transcriptomics analysis methods: microarray data processing, analysis and visualization using the Affymetrix GeneChip® Vitis vinifera genome array., Methods & Results in Grapevine Research, Serge Delrot, Hipolito Medrano Gil, Etti Or, Luigi Bavaresco, Stella Grando editors. 2010