Comparison of Gene Expression Signature Using Rank Based Statistical Inference

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• 关键词：Expression signature ; Perturbation ; Prototype rank list ; RRHO ; Secretory pathway
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9874
• 期：1
• 页码：28-41
• 全文大小：1,950 KB
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• 作者单位：Kumar Parijat Tripathi (16)
  Sonali Gopichand Chavan (17)
  Seetharaman Parashuraman (17)
  Marina Piccirillo (16)
  Sara Magliocca (16)
  Mario Rosario Guarracino (16)
  
  16. Laboratory for Genomics, Transcriptomics and Proteomics (LAB-GTP), High Performance Computing and Networking Institute (ICAR), National Research Council (CNR), Via Pietro Castellino 111, 80131, Naples, Italy
  17. Institute of Protein Biochemistry, National Research Council of Italy (CNR), Via Pietro Castellino 111, 80131, Napoli, Italy
  
• 丛书名：Computational Intelligence Methods for Bioinformatics and Biostatistics
• ISBN：978-3-319-44332-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9874

文摘

To understand the unique characteristics of biological state or phenotype, such as disease or cellular homeostasis, it is of vital importance to analyze the behavior of global gene expression. In the field of transcriptomics, gene expression patterns under the corresponding phenotypic state could be used as a proxy to determine the physiological and chemical response from the cellular system of an organism. Studying these kinds of patterns helps us to unveil the response of molecular machinery of cell, and predict regulation of a particular metabolic pathway. To understand the biological implication of these gene expression signatures is still an open question. In the present work, we are studying the behaviour of gene expression signatures of 22 knock down (perturbed) genes involved in secretory pathways, in distinct human cancer cell lines at different time scales, with the help of rank based statistical approach. The aim of our work is to compare the consequence of these gene perturbations at the transcriptional level, independently from the specific cell line effects, and categorize these perturbations to understand the inter connected network with in these perturbed genes and their shared influence on the regulation of sectretory pathway. To achieve this goal, we compared the gene expression signatures with respect to each perturbation per cell lines, using three different approaches implying non parametric rank based statistics. In the first approach, we generated prototype rank lists (PRLs) from gene expression data from given perturbation experiments, and calculated distance between expression signatures using pattern matching similarity based on kolmogorov-Smirnov statistic. In second approach, we implemented rank-rank hyper-geometric overlap maps (RRHO) for the identification of statistically significant overlapping genes between gene-expression signatures with respect to 22 genes perturbation experiments. Finally, we carried out gene set enrichment analysis (GSEA) on the previously obtained PRLs for the respective perturbations, and identify statistically significant KEGG pathways for which expression signatures of these 22 pertubations are enriched. Based on the comparative study of gene expression signature, 22 pertubations are clustered into 4 groups. Our results show that the transcriptional response with respect to each perturbation does not have an independent behavior, but perturbations with in each cluster share a common transcriptional response. Sister perturbations in each cluster have a cumulative role in shaping up the behaviour of cellular system.