Biclustering fMRI time series: a comparative study

Abstract

Background The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. Results This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. Conclusions This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.

Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes

“…In future work, it could be extended to other kinds of data, such as functional data, to which AA was also extended [51]. Note that biclustering analysis of time series is used in many fields such as neuroscience [79] and engineering [80]; therefore, biAA could also be used for the same problems. Biarchetypoid analysis could also be introduced in the same way that archetypoid analysis was defined [50], where biarchetypes are not determined by mixtures of observations and features, but by concrete elements of the data set.…”

Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment

“…To compare the performances of biclustering algorithms, several internal measures can be employed to evaluate the identified biclusters (Castanho et al 2022;Madeira and Oliveira 2004). The first statistic is the within-bicluster variance (VAR; Hartigan 1972),…”

Evaluation of Bicluster Analysis Results in Capture Fisheries Using the BCBimax Algorithm

“…The percentage of provinces that can be grouped into 11 biclusters is around 73.5%. This is expected to be able to provide additional information about the province and the diversity of fish species that cannot be grouped in combination (4,3). The Liu and Wang index will help further analyze the proportion of similarity of bicluster membership from all combination sub-matrixes that have been formed, as well as provide additional information in determining the optimal bicluster to be selected.…”

“…Rows and columns that have the same characteristic pattern are expected to be able to contribute more to providing helpful information. Therefore, a clustering technique was developed that handles the limitations of classical cluster analysis in conducting two-way clustering called Biclustering [4].…”