Association Rule Based Similarity Measures for the Clustering of Gene Expression Data

Sethi, Prerna

doi:10.2174/1874325001004010063

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…The property of cosines can be used to evaluate the similarity between trends of data progression and has been widely adapted in data mining and clustering strategies [ 24 - 26 ]. With the 20 microarray data points we generated from various regional and temporal embryonic chicken skin samples, we gathered the 20-dimensional data points for each gene, in which the i th dimension represents the gene’s expression in the i th microarray, where i denotes the 1 st to 20 th microarray profiles.…”

Section: Methodsmentioning

confidence: 99%

Emergence of differentially regulated pathways associated with the development of regional specificity in chicken skin

et al. 2015

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BackgroundRegional specificity allows different skin regions to exhibit different characteristics, enabling complementary functions to make effective use of the integumentary surface. Chickens exhibit a high degree of regional specificity in the skin and can serve as a good model for when and how these regional differences begin to emerge.ResultsWe used developing feather and scale regions in embryonic chickens as a model to gauge the differences in their molecular pathways. We employed cosine similarity analysis to identify the differentially regulated and co-regulated genes. We applied low cell techniques for expression validation and chromatin immunoprecipitation (ChIP)-based enhancer identification to overcome limited cell availabilities from embryonic chicken skin.We identified a specific set of genes demonstrating a high correlation as being differentially expressed during feather and scale development and maturation. Some members of the WNT, TGF-beta/BMP, and Notch family known to be involved in feathering skin differentiation were found to be differentially regulated. Interestingly, we also found genes along calcium channel pathways that are differentially regulated. From the analysis of differentially regulated pathways, we used calcium signaling pathways as an example for further verification. Some voltage-gated calcium channel subunits, particularly CACNA1D, are expressed spatio-temporally in the skin epithelium. These calcium signaling pathway members may be involved in developmental decisions, morphogenesis, or epithelial maturation. We further characterized enhancers associated with histone modifications, including H3K4me1, H3K27ac, and H3K27me3, near calcium channel-related genes and identified signature intensive hotspots that may be correlated with certain voltage-gated calcium channel genes.ConclusionWe demonstrated the applicability of cosine similarity analysis for identifying novel regulatory pathways that are differentially regulated during development. Our study concerning the effects of signaling pathways and histone signatures on enhancers suggests that voltage-gated calcium signaling may be involved in early skin development. This work lays the foundation for studying the roles of these gene pathways and their genomic regulation during the establishment of skin regional specificity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-014-1202-9) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Emergence of differentially regulated pathways associated with the development of regional specificity in chicken skin

et al. 2015

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“…is used to monitor expression data under a variety of conditions. In previous decades, gene expression data obtained from various microarray experiments has inspired several applications, including the discovery of differential gene expression for molecular studies or drug therapy response [2], the creation of predictive systems for improved cancer diagnosis [3] and the identification of unknown effect of a specific therapy [4]. However, using this technology to generate gene expression data sometimes leave a number of spots on the array missing [5].…”

Section: Dna Microarray Technologymentioning

confidence: 99%

Improved KNN Imputation for Missing Values in Gene Expression Data

Keerin¹,

Boongoen²

2022

Computers, Materials &Amp; Continua

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The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is the first study that applies the concept of ordered weighted averaging (OWA) operator to such a problem context. In particular, two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models. Using different ratios of missing values from 1%-20% and a set of six published gene expression datasets, the experimental results suggest that new methods usually provide more accurate estimates than those compared methods. Specific to the missing rates of 5% and 20%, the best NRMSE scores as averages across datasets is 0.65 and 0.69, while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84, respectively.

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“…In order to identify potential associations among the various cellular response types the association rule mining approach was used following the well accepted methodology described elsewhere. 14,15 The approach which has been widely used in bioinformatics [33][34][35][36][37][38][39][40][41][42][43][44] is aimed at identifying (from experimental data) whether the occurrence (observation) of certain events implies the occurrence of other (related) events. In the present work, association rule mining 14,33 was conducted with the processed HTS data (i.e., the binary categorization of the cellular response) in order to identify many-to-many relationships among different cellular response types (Table S1, ESI †).…”

Section: Identication Of Cellular Response Relationshipsmentioning

confidence: 99%

“…However, mapping of complex relationships or association rules (among cellular responses) requires the use of advanced data mining techniques. 14,15 In bioinformatics, 33 association rule mining has been demonstrated for extracting complex biological relationships of protein-protein interactions, 34,35 gene expression, [36][37][38][39][40][41][42] and genotype-phenotype mapping. 43,44 Association rule mining can also be benecial for NPs' HTS data analysis in order to identify multiple-to-multiple relationships between various cellular response types.…”

Section: Introductionmentioning

confidence: 99%

Association rule mining of cellular responses induced by metal and metal oxide nanoparticles

Liu

George

Ralló

et al. 2014

Analyst

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Relationships among fourteen different biological responses (including ten signaling pathway activities and four cytotoxicity effects) of murine macrophage (RAW264.7) and bronchial epithelial (BEAS-2B) cells exposed to six metal and metal oxide nanoparticles (NPs) were analyzed using both statistical and data mining approaches. Both the pathway activities and cytotoxicity effects were assessed using high-throughput screening (HTS) over an exposure period of up to 24 h and concentration range of 0.39-200 mg L(-1). HTS data were processed by outlier removal, normalization, and hit-identification (for significantly regulated cellular responses) to arrive at reliable multiparametric bioactivity profiles for the NPs. Association rule mining was then applied to the bioactivity profiles followed by a pruning process to remove redundant rules. The non-redundant association rules indicated that "significant regulation" of one or more cellular responses implies regulation of other (associated) cellular response types. Pairwise correlation analysis (via Pearson's χ(2) test) and self-organizing map clustering of the different cellular response types indicated consistency with the identified non-redundant association rules. Furthermore, in order to explore the potential use of association rules as a tool for data-driven hypothesis generation, specific pathway activity experiments were carried out for ZnO NPs. The experimental results confirmed the association rule identified for the p53 pathway and mitochondrial superoxide levels (via MitoSox reagent) and further revealed that blocking of the transcriptional activity of p53 lowered the MitoSox signal. The present approach of using association rule mining for data-driven hypothesis generation has important implications for streamlining multi-parameter HTS assays, improving the understanding of NP toxicity mechanisms, and selection of endpoints for the development of nanomaterial structure-activity relationships.

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Association Rule Based Similarity Measures for the Clustering of Gene Expression Data

Cited by 5 publications

References 25 publications

Emergence of differentially regulated pathways associated with the development of regional specificity in chicken skin

Emergence of differentially regulated pathways associated with the development of regional specificity in chicken skin

Improved KNN Imputation for Missing Values in Gene Expression Data

Association rule mining of cellular responses induced by metal and metal oxide nanoparticles

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