Microarray Data Analysis

Fridlyand, Jane

doi:10.1007/978-1-4614-1347-9_15

Cited by 7 publications

(2 citation statements)

References 9 publications

(8 reference statements)

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“…The gene array data were normalized with the Robust Multi-Array Average (RMA) method (Irizarry et al, 2003) implemented in the statistical software R (version 3.6.0) using the Brainarray custom Chip Description Files (CDF) so that intensity values are not summarized for each probe set but directly for each Entrez Gene ID (Dai et al, 2005). Raw data and transcriptomic signatures were uploaded to the NCBI Gene Expression Omnibus (GEO) (Barrett et al, 2013) repository under the accession numbers GSE146489.…”

Section: Data Normalizationmentioning

confidence: 99%

Adult female rats perinatally exposed to perfluorohexane sulfonate (PFHxS) and a mixture of endocrine disruptors display increased body/fat weights without a transcriptional footprint in fat cells

et al. 2021

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Section: Data Normalizationmentioning

confidence: 99%

Adult female rats perinatally exposed to perfluorohexane sulfonate (PFHxS) and a mixture of endocrine disruptors display increased body/fat weights without a transcriptional footprint in fat cells

et al. 2021

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“…In the first case, we have as a result the matrix of light intensities, the values of which are proportional to expression of appropriate gene (level of gene activity). Transformation of these light intensity into expression values assumes implementation of four steps: background correction [7][8][9][10], normalization [7,[11][12][13][14][15], PM correction and summarization [7,13,16,17]. In the second case, the initial data is presented as a matrix of genes count, the values of which are varied in very wide range.…”

Section: Introductionmentioning

confidence: 99%

Technique of Gene Expression Profiles Extraction based on the Complex Use of Clustering and Classification Methods

Babichev¹,

Škvor²

2020

Preprint

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In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ensemble of binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles: the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps: in beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using SOTA clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters were these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was selection of the optimal cluster at each of the hierarchical levels which corresponded to minimum value of the quality criterion. At the next step, we have implemented classification procedure of the examined objects using four well known binary classifiers: logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC analysis using criteria included as the components the errors of both the first and the second kinds. The final decision concerning extraction of the most informative subset of gene expression profiles was taken based on the use of fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

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Technique of Gene Expression Profiles Selection Based on SOTA Clustering Algorithm Using Statistical Criteria and Shannon Entropy

Babichev

Khamula

Durnyak

et al. 2020

Advances in Intelligent Systems and Computing

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Microarray Data Analysis

Cited by 7 publications

References 9 publications

Adult female rats perinatally exposed to perfluorohexane sulfonate (PFHxS) and a mixture of endocrine disruptors display increased body/fat weights without a transcriptional footprint in fat cells

Adult female rats perinatally exposed to perfluorohexane sulfonate (PFHxS) and a mixture of endocrine disruptors display increased body/fat weights without a transcriptional footprint in fat cells

Technique of Gene Expression Profiles Extraction based on the Complex Use of Clustering and Classification Methods

Technique of Gene Expression Profiles Selection Based on SOTA Clustering Algorithm Using Statistical Criteria and Shannon Entropy

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