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Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

Y-h. Taguchi
September 24, 2021

Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

Y-h. Taguchi

September 24, 2021

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  1. Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

    Y-h. Taguchi, Department of Physics, Chuo Univeristy, Tokyo, Japan Turki Turki, Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia the 8th Annual Congress of the European Society for Translational Medicine on novel therapeutics in solid tumors (EUSTM-2021) during 20-26 September, 2021.
  2. I have published a book on this topics from Springer

    international. I am glad if the audience can buy it and learn my method. Y-h. Taguchi, Unsupervised Feature Extraction Applied to Bioinformatics --- A PCA and TD Based Approach --- Springer International (2020)
  3. What is a tensor? Scholar x: a number Vector x

    i : a set of scholars in line Matrix x ij : a set of scholars aligned in a table (i.e. rows and columns) Tensor x ijk : a set of scholars aligned in an array more then two rows x ijk i j k 1 (1,2,3,4,...) (1 2 3 4 5 6 7 8 9 )
  4. Tensor is suitable to store genomics data: Gene expression :x

    ijk ∈ ℝN⨉M⨉K N genes ⨉ M persons ⨉ K tissues x ijk i:genes j:persons k:tissues
  5. What is tensor decomposition(TD)? Expand tensor as a series of

    product of vectors, x ijk i:genes j:persons k:tissues G k j i l 1 l 2 l 3 = u l 1 i u l 2 j u l 3 k u l 1 i u l 2 j u l 3 k x ijk ≃∑ l 1 =1 L 1 ∑ l 2 =2 L 2 ∑ l 3 =1 L 3 G (l 1 l 2 l 3 )u l 1 i u l 2 j u l 3 k
  6. Advantages of tensor decomposition(TD): We can know “Dependence of x

    ijk upon i” → u l1i “Dependence of x ijk upon j” → u l2j “Dependence of x ijk upon k” → u l3k ← Healthy control vs patient ← tissue specificity Gene selection ↑ We can answer the question : Which genes are expressed between healthy controls and patients in tissue specific manner?
  7. 8 Interpretation….. j:samples Healthy control Patients ul2j For some specific

    l2 For some specific l3 k:tissues Tissue specific expression ul3k
  8. 9 i:genes ul1i tDEG: tissue specific Differentially Expressed Genes Healthy

    controls < Patients tDEG: tDEG: Healthy controls > Patients For some specific l1 with max |G(l1l2l3)| If G(l1l2l3)>0 Fixed
  9. The purpose: Integrating epigenetic multiomics data (DNA methylation, histone modification,

    ChIP-seq, ATAC-seq, etc…) is always problematic, because their causal relationship unclear. This prevents us from developing the suitable model to understand what the relationship between them. In this talk, I apply Tensor Decomposition (TD) based unsupervised Feature Extraction (FE) to epigetic multiomics data in fully unsupervised manner.
  10. • • • 25000 Nucleotide acid ChIP-seq Histone modification Chromatin

    Accessibility • • • 24 Chromosome ••• chr2 • • • • • • chr1 chrY • • • N (=123,817 ~ 3 ⨉ 109 / 25,000) regions 24
  11. Epigenetic multiomics data set of prostate cancer is formatted as

    a tensor: x ijkm ∈ℝN×24×2×3 i: ith 25000 Nucleotide acid regions j: jth epigenetic data k: k=1: normal, k=2:tumor m: mth biological replicates
  12. Applying TD to x ijkm x ijkm ≃∑ l 1

    =1 L 1 ∑ l 2 =1 L 2 ∑ l 3 =1 L 3 ∑ l 4 =1 L 4 G (l 1 l 2 l 3 l 4 )u l 1 j u l 2 k u l 3 m u l 4 i G : weight of contribution of individual terms to x ijkm u l1j : the l 1 th unit vector represents j (epigenetics) dependence u l2k : the l 2 th unit vector represents k (normal vs tumor) dependence u l3m : the l 3 th unit vector represents m (biological replicates) dependence u l4i : the l 4 th unit vector represents i (25000 NA region) dependence
  13. u 1j (l 1 =1) u 2j (l 1 =2)

    1st and 2nd vectors attributed to j (epigenetics) 10 samples 10 samples Open DNA Active mark Inactive mark Prostate cancer activation
  14. 2nd vector attributed to normal vs tumor (l 2 =2)

    normal tumor 1st vector attributed to biological replicates (l 3 =1) k m Distinct between tumor and normal Common among biological replicates
  15. Seek which l 4 is associated with l 1 =1,

    l 2 =2, l 3 =1 G(1,2,1,l 4 ) l 4 l 4 =8 epigenetics normal vs tumor biological replicates 25000 NA region
  16. Region (i) selection l 4 =8 Attribute P-values to ith

    region with assuming u l4i obeys Gaussian (null hypothesis) using cumulative χ2 distribution. P i s are collected by Benjamini-Hochberg criterion. 1447 regions associated with adjusted P i less than 0.01 are selected. P(p i ) 1-p i 0 1
  17. 1447 genomic regions can discriminate between epigenetics as well as

    normal vs prostate with linear discriminant analysis (error ~ 37.5%)
  18. 1785 protein-coding genes included in these 1447 genomic regions are

    uploaded to Metascape DsigNet category of Metascape Prostate Neoplasms
  19. Top ranked three compounds whose treatment upregulates 1785 genes included

    in 1447 genomic regions are promising drugs for the treatment of prostate cancer. It is worthwhile investigating lower ranked compounds, too.
  20. Conclusion In this study, we have applied the recently proposed

    TD based unsupervised FE to integrated analysis of prostate cancer multiomics data sets. TD based unsupervised FE selected genomic regions whose value correctly discriminate not only kind of epigeneitc data but also normal tissues from tumors. 1785 genes are significantly related to prostate cancer. TD based unsupervised FE can identify promising compounds that can be used for prostate cancer treatment.
  21. None of authors declare any conflict of interest. My contact

    information: E-mail: [email protected] URL: https://researchmap.jp/Yh_Taguchi/ Linkedin: https://www.linkedin.com/in/y-h-taguchi-164900b4/