ArticleScientific reports2019
Principal Component Analysis applied directly to Sequence Matrix.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed.
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- Mapping evolutionary paradigm of Oropouche virus driven by dinucleotide bias and context-dependent codon bias.Virulence · 2025Article
- Evolutionary paradigm of distinct viral coding regions of BVDV associated with different nucleotide usage patterns.BMC veterinary research · 2025Article
- The Evolution of Function in the DNA-Binding Domain of the CRP/FNR Family.Genome biology and evolution · 2025Article
- Genetic surveillance reveals low but sustained malaria transmission with clonal replacement in Sao Tome and Principe.Communications medicine · 2025Article
- Analysis of Piscine orthoreovirus genotype 1 genomes collected over a 32-year period (1988-2020) suggests a fitness peak in salmon hosts with minimal evidence for temporal divergence.Virus evolution · 2025Article
- Combination of bulk RNA and single-cell sequencing unveils PANoptosis-related immunological ecology hallmarks and classification for clinical decision-making in hepatocellular carcinoma.Scientific reports · 2024Article
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- Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) enables precise and efficient phylogenetic estimation in viruses.Virus evolution · 2024Article
- Domestication in dry-cured meatEvolutionary applications · 2023Article
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- Rhinovirus dynamics across different social structures.Npj viruses · 2023Article
- Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches.Frontiers in veterinary science · 2023Article
- Mapping QTL associated with resistance toFrontiers in plant science · 2023Article
- Principal Component and Structural Element Analysis Provide Insights into the Evolutionary Divergence of Conotoxins.Biology · 2022Article
- Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.
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