Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation

Taguchi, Y-h.; Turki, Turki

doi:10.1371/journal.pone.0251032

Cited by 4 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we used a data-driven approach based on TD as a multivariate analysis method applied to multi-omics datasets. TD enables data-driven analyses such as data dimensionality reduction, classification, and potential FE [9,10] and has been widely applied to omics studies [3,7,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Omberg et al integrated genome-scale mRNA expression data from three cell cycle time courses in yeast to identify genes and the differential effects of gene-mediated biological processes on cell cycle progression using TD.…”

Section: Introductionmentioning

confidence: 99%

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

et al. 2023

View full text Add to dashboard Cite

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.

show abstract

Section: Introductionmentioning

confidence: 99%

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Cell division involves the division of one cell into two genetically identical daughter cells, and is tightly controlled during differentiation processes. During the cell-division process, gene transcription must be initially terminated and then reactivated once cell division is complete ( Taguchi and Turki 2021 ). DNA replication determines the timing of mitosis.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-based network analysis of cell cycle-related genes in response to blue and red light in maize

Liu,

Zhang,

Liu

2023

AoB PLANTS

View full text Add to dashboard Cite

In maize, blue and red light are key environmental factors regulating cell-cycle progression. We used transcriptomics to investigate and compare differential gene expression under the four light conditions: red light, blue light, red converted to blue and blue converted to red. A total of 23 differentially expressed genes were identified. The gene–gene interaction analysis indicated a significant interaction between four unidentified genes, 100191551, pco143873, 100284747 and pco060490, and cell-cycle-related genes. Using multiple sequence alignment analysis and protein structure comparisons, we show here that these four unidentified genes were characterized as ALP1-like, ALP1, cyclin P1-1 and AEBP2, respectively. By constructing a protein–protein interaction network, we inferred that 100191551 and pco143873 are potentially regulated to avoid DNA damage by abiotic stress response factors in the cell cycle. The gene 100284747 regulates the cell cycle in response to phosphate starvation signalling. The gene pco060490 potentially negatively regulates the cell cycle through the mediation of Histone H3 and CYCD6 in response to red light. In conclusion, the cell-cycle-related genes are sensitive to blue and red light, and four novel functional genes may be involved in the cell cycle.

show abstract

“…Here, we used a data-driven approach based on TD as a multivariate analysis method applied to multi-omics datasets. TD enables data-driven analyses such as data dimensionality reduction, classification, and potential FE (9,10) and has been widely applied to omics studies (Alter and Golub, 2005 Yahyanejad, 2019 (7,(11)(12)(13)(14)(15)(16). Omberg et al (2007) integrated genome-scale mRNA expression data from three cell cycle time courses in yeast to identify genes and the differential effects of gene-mediated biological processes on cell cycle progression using TD.…”

Section: Introductionmentioning

confidence: 99%

“…TD also has been applied to analyses within each omics (17–21) and among multiple omics (3,22,23). Recently, the application of TD-based unsupervised FE was proposed (Taguchi, 2017a, 2017b, 2017c; Taguchi and Turki, 2019, 2021; Taguchi, 2020). Taken together, the data-driven analyses using TD in these studies have shown that biologically meaningful features can be extracted by a data-driven non-biased method from datasets with various modes.…”

Section: Introductionmentioning

confidence: 99%

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

Fujita

Karasawa

Hironaka

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant/variable) and time dependence (sustained/transient). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.

show abstract

Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation

Cited by 4 publications

References 49 publications

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

Transcriptome-based network analysis of cell cycle-related genes in response to blue and red light in maize

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

Contact Info

Product

Resources

About