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Detecting similar high-dimensional responses to experimental factors from human and model organism
Published on Jan 23, 20123639 Views
We present a Bayesian model for analysing the effect of multiple experimental factors in two-species studies without the requirement of a priori known matching. From model studies of human diseases,
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Chapter list
Detecting similar high-dimensional responses to experimental factors from human and model organism00:00
Introduction00:20
Motivation01:18
Introduction: Experimental design01:45
Introduction: Multi-way decomposition02:39
Introduction: Univariate two-way decomposition (1)03:04
Introduction: Univariate two-way decomposition (2)06:19
Bayesian model for high-dimensional multi-way data06:26
Alignment of unknown time covariate08:11
One data set: Starting point08:27
One data set: Clustering08:35
One data set: Latent variables09:10
One data set: Multi-way decomposition09:18
Generalization to two data sets (1)09:33
Generalization to two data sets (2)10:01
Research question10:16
Multi-way decompostition of two data sets10:37
Matching11:12
Model11:44
Experiments12:39
Experiments: Toy data12:58
Experiments: Matching of lipid groups, ground truth15:11
Experiments: Matching of lipid and metabolite groups16:44
Conclusions (1)17:40
Conclusions (2)18:12