Heterogeneous Gene Data for Classifying Tumors

2011 ◽  
pp. 550-554
Author(s):  
Benny Yiu-ming Fung ◽  
Vincent To-yee Ng
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Single Gene ◽  
Data Sets ◽  
Expression Data ◽  
Classification Models ◽  
Experimental Conditions ◽  
Data Set ◽  
Single Data ◽  
Gene Data

When classifying tumors using gene expression data, mining tasks commonly make use of only a single data set. However, classification models based on patterns extracted from a single data set are often not indicative of an entire population and heterogeneous samples subsequently applied to these models may not fit, leading to performance degradation. In short, it is not possible to guarantee that mining results based on a single gene expression data set will be reliable or robust (Miller et al., 2002). This problem can be addressed using classification algorithms capable of handling multiple, heterogeneous gene expression data sets. Apart from improving mining performance, the use of such algorithms would make mining results less sensitive to the variations of different microarray platforms and to experimental conditions embedded in heterogeneous gene expression data sets.

Defining Immune Response Signatures in DLBCL As Potential Predictive Biomarkers for Outcome to Immunotherapy

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2663-2663
Author(s):  
Matthew A Care ◽  
Stephen M Thirdborough ◽  
Andrew J Davies ◽  
Peter W.M. Johnson ◽  
Andrew Jack ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Network Analysis ◽  
Gene Expression Data ◽  
Research Funding ◽  
Data Sets ◽  
Expression Data ◽  
Data Set ◽  
Gene Correlation ◽  
Cancer Types

Abstract Purpose To assess whether comparative gene network analysis can reveal characteristic immune response signatures that predict clinical response in Diffuse large B-cell lymphoma (DLBCL). Background The wealth of available gene expression data sets for DLBCL and other cancer types provides a resource to define recurrent pathological processes at the level of gene expression and gene correlation neighbourhoods. This is of particular relevance in the context of cancer immune responses, where convergence onto common patterns may drive shared gene expression profiles. Where existing and novel immunotherapies harness the immune response for therapeutic benefit such responses may provide predictive biomarkers. Methods We independently analysed publically available DLBCL gene expression data sets and a wide compendium of gene expression data from diverse cancer types, and then asked whether common elements of cancer host response could be identified from resulting networks. Using 10 DLBCL gene expression data sets, encompassing 2030 cases, we established pairwise gene correlation matrices per data set, which were merged to generate median correlations of gene pairs across all data sets. Gene network analysis and unsupervised clustering was then applied to define global representations of DLBCL gene expression neighbourhoods. In parallel a diverse range of solid and lymphoid malignancies including; breast, colorectal, oesophageal, head and neck, non-small cell lung, prostate, pancreatic cancer, Hodgkin lymphoma, Follicular lymphoma and DLBCL were independently analysed using an orthogonal weighted gene correlation network analysis of gene expression data sets from which correlated modules across diverse cancer types were identified. The biology of resulting gene neighbourhoods was assessed by signature and ontology enrichment, and the overlap between gene correlation neighbourhoods and WGCNA derived modules associated with immune/host responses was analysed. Results Amongst DLBCL data, we identified distinct gene correlation neighbourhoods associated with the immune response. These included both elements of IFN-polarised responses, core T-cell, and cytotoxic signatures as well as distinct macrophage responses. Neighbourhoods linked to macrophages separated CD163 from CD68 and CD14. In the WGCNA analysis of diverse cancer types clusters corresponding to these immune response neighbourhoods were independently identified including a highly similar cluster related to CD163. The overlapping CD163 clusters in both analyses linked to diverse Fc-Receptors, complement pathway components and patterns of scavenger receptors potentially linked to alternative macrophage activation. The relationship between the CD163 macrophage gene expression cluster and outcome was tested in DLBCL data sets, identifying a poor response in CD163 -cluster high patients, which reached statistical significance in one data set (GSE10846). Notably, the effect of the CD163-associated gene neighbourhood which correlates with poor outcome post rituximab containing immunochemotherapy is distinct from the effect of IFNG-STAT1-IRF1 polarised cytotoxic responses. The latter represents the predominant immune response pattern separating cell of origin unclassifiable (Type-III) DLBCL from either ABC or GCB DLBCL subsets, and is associated with a trend toward positive outcome. Conclusion Comparative gene expression network analysis identifies common immune response signatures shared between DLBCL and other cancer types. Gene expression clusters linked to CD163 macrophage responses and IFNG-STAT1-IRF1 polarised cytotoxic responses are common patterns with apparent divergent outcome association. Disclosures Davies: CTI: Honoraria; GIlead: Consultancy, Honoraria, Research Funding; Mundipharma: Honoraria, Research Funding; Bayer: Research Funding; Takeda: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Roche: Honoraria, Research Funding; GSK: Research Funding; Pfizer: Honoraria; Celgene: Honoraria, Research Funding. Jack:Jannsen: Research Funding.

scholarly journals Defining a landscape of molecular phenotypes using a simple single sample scoring method

2017 ◽  
Author(s):  
Momeneh Foroutan ◽  
Dharmesh D. Bhuva ◽  
Kristy Horan ◽  
Ruqian Lyu ◽  
Joseph Cursons ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Single Sample ◽  
Data Sets ◽  
Molecular Signatures ◽  
Expression Data ◽  
Scoring Method ◽  
Data Set ◽  
Sample Composition ◽  
Molecular Phenotypes

AbstractBackgroundGene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition across a data set (e.g. varying numbers of samples for different cancer subtypes). To address these issues we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore.ResultsWe have developed a rank-based single sample scoring method, implemented as a Bioconductor package. We use multiple cancer data sets to compare it against widely-used scoring methods, including GSVA, z-scores, PLAGE, and ssGSEA. Our approach does not depend upon background samples and thus the scores are stable regardless of the composition and number of samples in the gene expression data set. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (nsamples < 25). We show that the computational time for singscore is faster than current implementations of GSVA and ssGSEA, and is comparable with that of z-score and PLAGE. The singscore package also produces visualisations and interactive plots that enable exploration of molecular phenotypes.ConclusionsThe single sample scoring method described here is independent of sample composition in gene expression data and thus it provides stable scores that are less likely to be influenced by unwanted variation across samples. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

scholarly journals Case-Based Retrieval Framework for Gene Expression Data

2015 ◽  
Vol 14 ◽  
pp. CIN.S22371 ◽  
Author(s):  
Ali Anaissi ◽  
Madhu Goyal ◽  
Daniel R. Catchpoole ◽  
Ali Braytee ◽  
Paul J. Kennedy
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Childhood Leukemia ◽  
Data Sets ◽  
Expression Data ◽  
Data Set ◽  
Cancer Data ◽  
Leukemia Data ◽  
Case Based

Background The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process. Methods This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. Results The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children's Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. Conclusion The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps.

scholarly journals Graph Theoretic Techniques for Clustering and Biclustering gene expression data.

2012 ◽  
pp. 173-181
Author(s):  
Prangyaparamita Mohapatra ◽  
Tripti Swarnkar
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Clustering Algorithms ◽  
Expression Data ◽  
Microarray Technology ◽  
Clustering Methods ◽  
Experimental Conditions ◽  
Data Set

DNA microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes during biological processes and across collections of related samples. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results of the application of standard clustering methods to genes are limited. These limited results are imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the gene expression matrix have been proposed to date. This simultaneous clustering, usually designated by biclustering, seeks to find submatrices that are subgroups of genes and subgroups of columns, where the genes exhibit highly correlated activities for every condition. This type of algorithms has also been proposed and used in other fields, such as information retrieval and data mining. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches.

scholarly journals A fuzzy logic approach to analyzing gene expression data

2000 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
PETER J. WOOLF ◽  
YIXIN WANG
Keyword(s):  
Gene Expression ◽  
Fuzzy Logic ◽  
Gene Expression Data ◽  
Expression Data ◽  
Heuristic Rules ◽  
Yeast Gene ◽  
Data Set ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Novel Algorithm

Woolf, Peter J., and Yixin Wang. A fuzzy logic approach to analyzing gene expression data. Physiol Genomics 3: 9–15, 2000.—We have developed a novel algorithm for analyzing gene expression data. This algorithm uses fuzzy logic to transform expression values into qualitative descriptors that can be evaluated by using a set of heuristic rules. In our tests we designed a model to find triplets of activators, repressors, and targets in a yeast gene expression data set. For the conditions tested, the predictions made by the algorithm agree well with experimental data in the literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger number of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data.

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals ExAtlas: An interactive online tool for meta-analysis of gene expression data

2015 ◽  
Vol 13 (06) ◽  
pp. 1550019 ◽  
Author(s):  
Alexei A. Sharov ◽  
David Schlessinger ◽  
Minoru S. H. Ko
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Fixed Effects ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Gene Set ◽  
Public Data

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users’ own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher’s methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein–protein interaction) are pre-loaded and can be used for functional annotations.

scholarly journals Searching for master regulators of transcription in a human gene expression data set

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Alfonso Buil ◽  
Alexandre Perera-Lluna ◽  
Ramon Souto ◽  
Juan M Peralta ◽  
Laura Almasy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Human Gene ◽  
Expression Data ◽  
Data Set ◽  
Master Regulators ◽  
Human Gene Expression
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