title: Stepwise iterative maximum likelihood clustering approach.
abstract: BACKGROUND
  Biological/genetic data is a complex mix of various forms or topologies which makes it quite difficult to analyze. An abundance of such data in this modern era requires the development of sophisticated statistical methods to analyze it in a reasonable amount of time. In many biological/genetic analyses, such as genome-wide association study (GWAS) analysis or multi-omics data analysis, it is required to cluster the plethora of data into sub-categories to understand the subtypes of populations, cancers or any other diseases. Traditionally, the k-means clustering algorithm is a dominant clustering method. This is due to its simplicity and reasonable level of accuracy. Many other clustering methods, including support vector clustering, have been developed in the past, but do not perform well with the biological data, either due to computational reasons or failure to identify clusters.
  
  
  RESULTS
  The proposed SIML clustering algorithm has been tested on microarray datasets and SNP datasets. It has been compared with a number of clustering algorithms. On MLL datasets, SIML achieved highest clustering accuracy and rand score on 4/9 cases; similarly on SRBCT dataset, it got for 3/5 cases; on ALL subtype it got highest clustering accuracy for 5/7 cases and highest rand score for 4/7 cases. In addition, SIML overall clustering accuracy on a 3 cluster problem using SNP data were 97.3, 94.7 and 100 %, respectively, for each of the clusters.
  
  
  CONCLUSIONS
  In this paper, considering the nature of biological data, we proposed a maximum likelihood clustering approach using a stepwise iterative procedure. The advantage of this proposed method is that it not only uses the distance information, but also incorporate variance information for clustering. This method is able to cluster when data appeared in overlapping and complex forms. The experimental results illustrate its performance and usefulness over other clustering methods. A Matlab package of this method (SIML) is provided at the web-link http://www.riken.jp/en/research/labs/ims/med_sci_math/ .
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. alok.fj@gmail.com.|CREST, JST, Yokohama, 230-0045, Japan. alok.fj@gmail.com.|Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. alok.fj@gmail.com.
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|CREST, JST, Yokohama, 230-0045, Japan.|Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
authors.forename: Daichi
authors.initials: D
authors.lastname: Shigemizu
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Boroevich
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
authors.forename: Yosvany
authors.initials: Y
authors.lastname: López
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
authors.forename: Yoichiro
authors.initials: Y
authors.lastname: Kamatani
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
authors.forename: Michiaki
authors.initials: M
authors.lastname: Kubo
authors.affiliation: RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. tatsuhiko.tsunoda@riken.jp.|CREST, JST, Yokohama, 230-0045, Japan. tatsuhiko.tsunoda@riken.jp.|Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan. tatsuhiko.tsunoda@riken.jp.
authors.forename: Tatsuhiko
authors.initials: T
authors.lastname: Tsunoda
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D006801:Humans; D016013:Likelihood Functions; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide