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For each sample, gene expression PCR was carried out using 2 µl of cDNA template with sequence specific primers.
The shrinkage estimate for partial correlation in [7] was formulated specifically for the inference from small sample gene expression data.
For each sample, gene expression was quantified using a standard curve and normalized against the expression of the endogenous control genes GAPDH and ACTB.
Sample gene expression was then normalized against rRNA expression levels.
For each sample, gene expression was normalized using human elongation factor 1 mRNA (HsEEF1A1).
We applied the method to a sample gene expression data of S. cerevisiae.
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As observed gene expressions usually do not fit specific parametric distributions [ 28], we randomly sample gene expressions of 200 subjects without replacement from D1-D3 combined (the lymphoma datasets).
Therefore, based on partial correlation, we propose a directed approach specifically targeted at small-sample gene expression data.
The technique can be applied in almost any multi-sample gene expression experiment, and holds equal promise for protein abundance experiments.
Approximately 350,000 SNP-containing probesets were filtered out before summarizing gene expression intensities in the CEU and YRI samples (Gene Expression Omnibus Accession: GSE9703 [9], http://www.ncbi.nlm.nih.gov/projects/geo/).nih.gov/projects/geo/
Indeed, for a significant proportion of samples, gene expression based classification and IHC based classification differ, and it is currently unclear whether these cases behave clinically more like true ER positive or true ER negative cases[5].
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