Principal Component Analysis applied directly to Sequence Matrix.

ABSTRACT: Sequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As a

SUBMITTER: Konishi T 

PROVIDER: S-EPMC6917774 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature