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PhysioSpace: relating gene expression experiments from heterogeneous sources using shared physiological processes.

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Academic year: 2017

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Figure 2.    Overview over the PhysioSpace algorithm.    (A) Data from a new experiment is transformed to remove ellipticity and  the  resulting  fold-change  vector  is  compared  to  a compendium  of  signatures  from  prior  experiments  using  a robust
Table 1. Datasets for the PhysioSpace generation.
Figure 3.  Performance evaluations with simulated data.  (A) In the case of relatively high heterogeneity and comparably low signal strength, the spherical transformation increases sensitivity and specificity of the simulated effect, i.e
Figure 6.  Detailed results of the differentiation time series analyses.  (A) Line plots of most relevant PhysioScores for the three differentiation time series comparing scores from PhysioSpaces 1 and 3

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