Abstract:
:Measuring complete gene expression profiles for a large number of experiments is costly. We propose an approach in which a small subset of probes is selected based on a preliminary set of full expression profiles. In subsequent experiments, only the subset is measured, and the missing values are inputed. We developed several algorithms to simultaneously select probes and input missing values, and we demonstrate that these 'probe selection for imputation' (PSI) algorithms can successfully reconstruct missing gene expression values in a wide variety of applications, as evaluated using multiple metrics of biological importance. We analyze the performance of PSI methods under varying conditions, provide guidelines for choosing the optimal method based on the experimental setting, and indicate how to estimate imputation accuracy. Finally, we apply our approach to a large-scale study of immune system variation.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Donner Y,Feng T,Benoist C,Koller Ddoi
10.1038/nmeth.2207subject
Has Abstractpub_date
2012-11-01 00:00:00pages
1120-5issue
11eissn
1548-7091issn
1548-7105pii
nmeth.2207journal_volume
9pub_type
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