Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data

Min, Eun Jeong; Long, Qi

doi:10.1186/s12859-020-3455-4

BMC Bioinformatics

Table 1 Simulation designs for each scenario and corresponding true loading vectors. All true vectors are normalized to have l₂-norm 1

From: Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data

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n_en=(3,4,5)
	n_el=10	n_el=20
σ²=1.2	scenario 1	scenario 2
	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
σ²=2.5	scenario 3	scenario 4
	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
n_en=(5,5,5)
	n_el=10	n_el=20
σ²=1.2	scenario 5	scenario 6
	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
σ²=2.5	scenario 7	scenario 8
	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)	\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)