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Table 4 For each method, the first two columns show the number of nonzero elements in the first two estimated coefficient loadings of three datasets, the Affymetrix, the Agilent, and the protein dataset respectively. Next four columns contain pseudo-eigenvalues calculated using the estimated coefficient loadings from the training dataset. Last four columns include proportions of pseudo-eigenvalues to the sum of total eigenvalues for each dataset

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

 

# of nonzeros

Pseudo Eigenvalues

% of variability explained

   

test dataset

whole dataset

test dataset

whole dataset

 

1st

2nd

1st

1st + 2nd

1st

1st + 2nd

1st

1st + 2nd

1st

1st + 2nd

mCIA

(491,488,94)

(491,488,94)

36065.92

33447.03

282991.70

218372.50

0.088

0.169

0.129

0.229

smCIA

(250,30,20)

(100,80,15)

31161.89

21283.77

208966.30

157045.80

0.076

0.127

0.095

0.167

ssmCIA

(300,80,15)

(400,15,30)

34611.11

36793.08

239050.80

239050.80

0.084

0.173

0.109

0.218