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Tensor-Decomposition-based Unsupervised Feature Extraction in Single-cell Multiomics Data Analysis

Y-h. Taguchi
October 31, 2021

Tensor-Decomposition-based Unsupervised Feature Extraction in Single-cell Multiomics Data Analysis

Presentation at ICBBS2021
http://www.icbbs.org/
at 31th Oct. 2021
(On line presentation)

Published paper is
Taguchi, Y.-h.; Turki, T. Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis. Genes 2021, 12, 1442. https://doi.org/10.3390/genes12091442

Y-h. Taguchi

October 31, 2021
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  1. Tensor-Decomposition-based Unsupervised Feature Extraction
    in Single-cell Multiomics Data Analysis
    Y-h. Taguchi
    Chuo University, Tokyo, Japan.
    and
    Turki Turki
    King Abdulaziz University, Jeddah, Saudi Arabia
    Taguchi, Y.-h.; Turki, T. Tensor-Decomposition-Based
    Unsupervised Feature Extraction in Single-Cell Multiomics Data
    Analysis. Genes 2021, 12, 1442.
    https://doi.org/10.3390/genes12091442

    View full-size slide

  2. Introduction
    It is difficult to integrate multiomics single cell data set because
    1) The number of features is huge (~108 for site-wise measurements)
    2) Full of missing data (only a few percentages of non-missing values
    for site-wise measurements).
    3) Difficult to integrate distinct number of features, mRNA ~ 104.

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  3. Conventional approaches:
    Conventional approaches:
    Give up integrating multiomics data.
    (Analyze individual omics data separately).
    Screening filled features (i.e. excluding features with missing values)
    Filling missing values artificially (e.g., using Bayes predictors)
    The proposed approach:
    The proposed approach:
    Integrate multiomics data sets full of missing values as well as
    associated with distinct number of features without any pre-process
    (as it is) using tensor decomposition (TD).

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  4. GSE154762: Dataset 1
    GSE154762: Dataset 1
    Number of cells: 899
    Gene expression+DNA mathylation+DNA accessibility
    GSE121708: Dataset 2
    Number of cells: 852 (758 for gene expression)
    Gene expression+DNA mathylation+DNA accessibility

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  5. PreProcess
    Gene expression: nothing
    DNA methylation: -1:unmethylated, 0:missing values, 1:metylated
    DNA accessibility: average over every 200 nucleotide regions.
    (It is four histone proteins + a linker protein)
    Standardized:
    Gene expression: zero mean, variance of 1
    DNA methylation and accessibility for data set 1:
    Mean absolute values is one
    Those for data set 2: nothing (because of heterogeneity)

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  6. For data set 1 or 2:
    x
    ijk
    ∈ℝN
    k
    ×M ×3
    N
    k
    : Number of features of kth omics data:
    k=1: gene expression, k=2: DNA methylation, k=3: DNA accessibility
    M:number of cells.
    Since N
    k
    s are not common we need to adjust N
    k
    s into one value.

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  7. Full of missing values
    Full of missing values

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  8. x
    ijk
    =∑
    l=1
    L
    λl
    u
    lik
    u
    l jk
    x
    ljk
    =∑
    i=1
    N
    k x
    ijk
    u
    lik
    ∈ℝL× M×K
    Apply TD to x
    ljk
    to get
    where we emply L=10
    x
    ljk
    =∑
    l
    1
    =1
    L

    l
    2
    =1
    M

    l
    3
    =1
    3
    G(l
    1
    l
    2
    l
    3
    )u
    l
    1 l
    u
    l
    2
    j
    u
    l
    3
    k

    View full-size slide

  9. What is tensor decomposition(TD)?
    Expand tensor as a series of product of vectors,
    x
    ijk
    l:reduced
    dimension
    j:cells
    k:multiomics
    G
    k
    j
    l
    l
    1
    l
    2
    l
    3
    =
    u
    l
    1
    l
    u
    l
    2
    j
    u
    l
    3
    k
    u
    l
    1
    i
    u
    l
    2
    j
    u
    l
    3
    k
    x
    ljk
    ≃∑
    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
    l
    u
    l
    2
    j
    u
    l
    3
    k

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  10. Select u
    l2j
    associated with classification, s.
    Data set 1:human oocyte maturation
    Classification: Cell types
    Data set 2:four time points of the mouse embryo
    Classification: time points
    a
    l2s
    ,b
    l2
    : regression coefficients
    δ
    js
    =1 when j ∈ s, otherwise =0
    Check which u
    l2j
    is coincident with classes, s.
    u
    l
    2
    j
    =a
    l
    2
    s
    δjs
    +b
    l
    2

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  11. 18 (for data set 1) and 12 (for data set 2) u
    l2j
    are significantly
    correlated with classifications.
    UMAP was applied to top 30 u
    l2j
    and we got two dimensional
    embedding as can be seen in the following slides.

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  12. We also performed gene selections and biological validation of
    them using enrichment analysis. But no time to present them.
    Conclusions:
    Conclusions:
    We have applied TD to integration of single cell multiomics data
    sets.
    Without specific preprocessing, TD successfully obtained low
    dimensional embedding with which UMAP can generate
    embedding coincident with classification.

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