The role of complementary bipartite visual analytical representations in the analysis of SNPs: a case study in ancestral informative markers
Published Date:Jun 2012
Source:J Am Med Inform Assoc. 2012; 19(e1):e5-e12.
Analysis Of Variance
Human-computer Interaction And Human-centered Computing
Linking The Genotype And Phenotype
Polymorphism, Single Nucleotide
Research And Applications
Systems To Support And Improve Diagnostic Accuracy
Text And Data Mining Methods
Uncertain Reasoning And Decision Theory
Visualization Of Data And Knowledge
Funding:R21OH009441-01A2/OH/NIOSH CDC HHS/United States
UL1 TR000071/TR/NCATS NIH HHS/United States
UL1RR029876/RR/NCRR NIH HHS/United States
Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case–control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships.
To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes.
The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease.
The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.
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