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Imputing gene expression from selectively reduced probe sets
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DOI：10.1038/NMETH.2207.png)
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摘要
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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 imputed. We developed several algorithms to simultaneously select probes and impute 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.
Keyword:
MISSING VALUE ESTIMATION
CELL-CYCLE
IDENTIFICATION
PREDICTION
DISCOVERY
PROFILES
MOUSE
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32.1
论文数:
7.2K
被引数:
12.7W
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