A conceptual approach to gene expression analysis enhanced by visual analytics

Abstract

The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.

Visual Identification of Inconsistency in Pattern

“…Such missing information can be denoted by "unavailable" or empty space, among others. Inconsistent data relating to gene expressions in tissues of different developmental stages are reported in [17,19]. Finally, a Radiologist chest x-ray report can be used to detect aortic unfolding which is mostly associated with systemic hypertension.…”

“…Contradictions on the other hand, differ from one dataset to another, depending on the semantic definition of the data in the dataset. Interestingly, there are dedicated Applications such as CUBIST [19], ConTra [20], and R Package VIM [21] which enables the visualisation of the amount or pattern of contradiction and missingness in a noisy dataset. Inconsistent data whose pattern involves mutually exclusive type of contradictions is depicted by ConTra.…”

“…There are other tools which enables the visualisation of inconsistencies as explained in [19,22,23]. A graphical tool is proposed in [22] that highlight inconsistent instances in the network such as the highlights of direct comparisons that strongly drive other treatment effect estimates and hot spots of network inconsistency.…”

Formal concept analysis for knowledge discovery from biological data

“…Melo and collaborators [15] proposed an FCA-based approach combined with association rule and visual analytics to find out overlapping groups of genes in gene expression and analyzed it in an analytical tool called CUBIST. The workflow of CUBIST involves querying a semantic databases and transforming the result into formal context and then it is visualized as a concept lattice and associated charts.…”

A Distribution Model with Pattern Structure in Formal Concept Analysis for Meteorological Data Minging

“…Modern meteorological observation method makes the meteorological department has accumulated a lot of forms of meteorological data [9], which is a typical big data [10]: 1) the various types of data, including the stations data focus on observation data of the ground, high altitude, solar radiation, agricultural meteorological stations and stations data of statistical processing products; the lattice data focus on assimilation analysis data of numerical model and n umerical inversion products of remote sensing detection; image data focus on satellite cloud pictures and radar image; comprehensive meteorological data composed of comprehensive information of a certain area or range which is subject oriented and made up with a variety of data.…”