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Table 1 Contruction of simulation parameters for artificial gene expression data from the multivariate normal distribution

From: Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots

Setting

Group 1

Group 2

Outliers

Parameter values

1

μ=0 d

-

μ 1=f c⊗J d

f c=0.5,0.75

   

μ 2=(f c,−f c)T⊗J d/2

ρ=0.75

   

μ 3=(f c,−f c,f c,−f c)T⊗J d/4

τ=0.01,0.05

2

μ=0 d

κ=5·J d

μ 1=f c⊗J d

f c=1.0,1.5

   

μ 2=(f c,−f c)T⊗J d/2

ρ=0.75

   

μ 3=(f c,−f c,f c,−f c)T⊗J d/4

τ=0.05,0.1.,0.2

   

κ 1=κ+(f c⊗J d )

 
   

κ 2=κ+((f c,−f c)T⊗J d/2)

 
   

κ 3=κ+((f c,−f c,f c,−f c)T⊗J d/4)

 
  1. Setting 1 represents data for one group with three outliers. Setting 2 represents data for two group, each with three outliers. The last columns shows the simulation parameters that are varied. The mean vectors in group 1 and group 2 for regular observations are given by μ and κ, and those mean vectors for outlying observations are given by μ 1, μ 2, μ 3 and κ 1, κ 2, κ 3. In this notation, J L denotes a vector of ones of length L and ⊗ denotes the symbol for the Kronecker product
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BMC Bioinformatics

ISSN: 1471-2105

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