Identifying the best metrics to find the best quality clusters of genes from gene expression data
Authors: 
Raihanoor Reza Rayon, Joydhriti Choudhury, Md. Tawhidul Islam, Tanzima Rahman Roshni, Faisal Bin Ashraf, Rasif Ajwad, Md Abdul MottalibAuthors Info & Claims
Raihanoor Reza Rayon, Joydhriti Choudhury, Md. Tawhidul Islam, Tanzima Rahman Roshni, Faisal Bin Ashraf, Rasif Ajwad, Md Abdul MottalibAuthors Info & ClaimsPages 43 - 48
Abstract
With the recent advancement of computing technique and data availability in the field of computational biology, it has been a great opportunity for the scientists to find the evolutionary relation among the living beings in terms of their genotypic and phenotypic attributes. Microarray, one of the efficient ways to store the expression level of genes in the living being, can be used to create groups from a set of genes based on their phenotypic information. This information plays an important role in pathway analysis, disease prediction, target identification in drug design and many other important functionalities and applications in biology. However, it has become a great challenge over time to select a particular distance metric to calculate the similarity between the genes. In this work, we have studied 16 possible combinations of metrics to find the groups of similar genes in terms of their expression level by building their phylogenetic relation and keeping the most related genes together. Moreover, we have validated our findings by evaluating the output of the same trials on different data sets. We have found that, for grouping the similar genes together by building a Phylogenetic Tree, Maximum Distance Metric and Average Linkage tends to give the best quality.
References
[1]
H. Anton and C. Rorres. Elementary linear algebra. anton textbooks, 1994.
[2]
A. Bairoch and R. Apweiler. The swiss-prot protein sequence database and its supplement trembl in 2000. Nucleic acids research, 28(1):45--48. 2000.
[3]
C. D. Cantrell. Modern mathematical methods for physicists and engineers. Cambridge University Press, 2000.
[4]
C. Chen, K. Grennan, J. Badner, D. Zhang, E. Gershon, L. Jin, and C. Liu. Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PloS one, 6(2):e17238, 2011.
[5]
S. Craw. Manhattan distance. Encyclopedia of Machine Learning and Data Mining, pages 790--791. 2017.
[6]
P.-E. Danielsson. Euclidean distance mapping. Computer Graphics and image processing, 14(3):227--248. 1980.
[7]
P. D'haeseleer. How does gene expression clustering work? Nature biotechnology, 23(12):1499, 2005.
[8]
B. S. Everitt, S. Landau, M. Leese, and D. Stahl. Hierarchical clustering. Cluster analysis, 5, 2011.
[9]
V. Greiff, P. Bhat, S. C. Cook, U. Menzel, W. Kang, and S. T. Reddy. A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status. Genome medicine, 7(1):49, 2015.
[10]
N. T. Gupta, K. D. Adams, A. W. Briggs, S. C. Timberlake, F. Vigneault, and S. H. Kleinstein. Hierarchical clustering can identify b cell clones with high confidence in ig repertoire sequencing data. The Journal of Immunology, 198(6):2489-2499, 2017.
[11]
A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: a review. ACM computing surveys (CSUR), 31(3):264--323. 1999.
[12]
S. C.Johnson. Hierarchical clustering schemes. Psychometrika, 32(3):241--254. 1967.
[13]
T. Klove, T.-T. Lin, S.-C. Tsai, and W.-G. Tzeng. Permutation arrays under the chebyshev distance. IEEE Transactions on Information Theory, 56(6):2611--2617. 2010.
[14]
I. Letunic and P. Bork. Interactive tree of life (itol): an online tool for phylogenetic tree display and annotation. Bioinformatics, 23(1):127--128. 2006.
[15]
I. Letunic and P. Bork. Interactive tree of life v2: online annotation and display of phylogenetic trees made easy. Nucleic acids research, 39(suppl_2):W475-W478, 2011.
[16]
X. Lin, R. Wang, J. Zhang, X. Sun, Z. Zou, S. Wang, and M. Jin. Insights into human astrocyte response to h5n1 infection by microarray analysis. Viruses, 7(5):2618--2640. 2015.
[17]
E. Lord, A. B. Diallo, and V. Makarenkov. Classification of bioin-formatics workflows using weighted versions of partitioning and hierarchical clustering algorithms. BMC bioinformatics, 16(1):68, 2015.
[18]
H. Ma and A.-P. Zeng. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics, 19(2):270--277. 2003.
[19]
W. P. Maddison. A method for testing the correlated evolution of two binary characters: are gains or losses concentrated on certain branches of a phylogenetic tree? Evolution, 44(3):539--557. 1990.
[20]
M. Pop and S. L. Salzberg. Bioinformatics challenges of new sequencing technology. Trends in genetics, 24(3):142--149. 2008.
[21]
D. C. Richter, F. Ott, A. F. Auch, R. Schmid, and D. H. Huson. MetasimâĂŤa sequencing simulator for genomics and metagenomics. PloS one, 3(10):e3373, 2008.
[22]
R. Wang, X.-F. Chen, and Y.-Q. Shu. Prediction of non-small cell lung cancer metastasis-associated micrornas using bioinformatics. American journal of cancer research, 5(1):32, 2015.
[23]
J. H. Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301):236--244. 1963.
[24]
C. N. White, D. W. Chan, and Z. Zhang. Bioinformatics strategies for proteomic profiling. Clinical biochemistry, 37(7):636--641. 2004.
[25]
C. Xu and Z. Su. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics, 31(12):1974-1980, 2015.
[26]
Z. Zhu, Y.-S. Ong, and M. Dash. Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognition, 40(11):3236--3248, 2007.
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- Identifying the best metrics to find the best quality clusters of genes from gene expression data
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Published In
December 2019
169 pages
ISBN:9781450372183
DOI:10.1145/3383783
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- Sun Yat-Sen University
- Seoul National University
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Published: 17 April 2020
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ICBRA '19
ICBRA '19: 2019 6th International Conference on Bioinformatics Research and Applications
December 19 - 21, 2019
Seoul, Republic of Korea
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