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If this is the case, the normalized data based on different regression coefficients will still have potential mean differences, which needs to be adjusted before combining the datasets.
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Before combining the dataset for the five years using appropriate direct standardisation macros, each yearly dataset was compared to exclude significant demographic differences.
Combining the datasets resulted in 280 differentially expressed genes.
There was never an intention to combine the datasets.
Next, to compare populations in the two datasets directly, we combined the datasets.
25 minutes before: Combine the sauces.
We considered the following strategies for combining the available datasets.
Since the global median-normalized and quantile-normalized data correlated well (see Additional file 4), we have used the former normalization method, with the least number of data manipulation steps, before combining these two datasets.
A gold standard dataset was created by combining all the datasets.
Data fusion combines the two datasets into one single database.
Before combining scores across different datasets and module definitions, we first normalized every score by dividing it by an average score of 500 permuted versions of the known modules (Supplementary Fig. 25).
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