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We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data.
The controlled changes introduced into real expression data do not necessary make the changed expression really significantly different.
Since the ground truth is unknown for real expression data, comparisons of performance are first conducted on synthetic data and then on biological data.
However, in real expression data, only a portion of gene sets have such strong correlations.
Future work will include evaluating the PM under the kinds of correlation structures found in real expression data.
To do so, we generated simulated datasets by randomly choosing the same number of arrays from the real expression data.
Similar(39)
Since the real expression array data are usually mixed with noises, the comparison between two genes is always disturbed by noise.
We subsequently test the insights and hypotheses generated by the theoretical analysis and simulation studies with real gene expression data.
Similar step - introduced changes in the real gene expression data - was used earlier for demonstration of the breakdown of Lowess normalization after "one direction changes" [15].
The greatest strength of our strategy is the creation of test datasets through the introduction of controlled changes into real gene expression data.
In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com