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We examined whether each variable was normally distributed before statistical testing, and logarithmic transformation was performed for skewed variables.
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To minimize the impact of the high degree of correlation among the features on the feature selection step, the feature data were clustered before statistical testing by Affinity Propagation method (Frey and Dueck, 2007) to only keep features representing the most variability.
To visualize different outcomes in terms of a profile, observed values were transformed to standard z scores (with mean = 0 and standard deviation = 1) using the overall means and standard deviations for the total patient group before statistical testing.
Before statistical testing, the Kolmogorov-Smirnov test was used to analyze each continuous variable for its normal distribution.
Tests for normal distribution were performed before statistical testing.
Before statistical testing, each continuous variable was analysed for normal distribution (Kolmogorov-Smirnov test).
Before statistical testing data were explored in order to assess their pattern of distribution.
Before statistical testing, we natural log transformed skewed variables to obtain normality.
Before statistical testing, the data were transformed to follow a normal distribution.
Firstly, before statistical testing, we screened out transcripts which had minimal or no coverage (see Additional file 1).
To normalize the ratios, a logarithmic transformation was applied before statistical testing for association with bone marrow positivity.
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