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PHENOMIN offers professional assistance to tailor your prospective project to your needs. In consultation with our staff, you will have the opportunity to design the project that maximizes the value of your research.

Model Preparation

Our panel of  genetic tools and grafted models provide various starting points for analysis.

Disease Examination / Phenotyping tests

Your field of interest is likely already represented among our validated tests on offer.  We can walk you through the specific benefits and drawbacks of tests sorted by  disease involvement .

Statistical Needs

PHENOMIN proudly offers assistance from resident statisticians for statistical prediction based on prior testing and application of the  appropriate tests

Representative Data

We offer  representative data for each of our tests that you will know in advance how you want the analysis tailored to your question.


Various  housing, archiving, and distribution capabilities are at your disposal for planning your work.  We also have numerous archived lines that may be applicable to your project. 

Test Execution / Reporting

Our project managers provide centralized and informed supervision of the tasks as they progress through PHEMOMIN, including detailed reports incorporating interpretation of our experts. 


PHENOMIN performs different statistical analysis on your phenotypic results to guarantee their validity. From basic outlier detection, up to cohort size optimisation, here are some of the analysis that are done.

Power analysis

The power of a statistical test is the probability to detect a significant difference between groups if it exists.
Group size, effect size, significance level and power are linked together with mathematical relationship. In these conditions we can assess the group size necessary to get the best chances to detect significant differences between 2 groups of mice for given effect size, power and significant level.
Power is important to calculate in order to optimize experimental protocols and also to assess what we were really able to see.
We usually try to reach a power of 80-90%.

Icon Procedure to estimate the requested animal number (189.4 KB)



Data transformation

In order to perform parametric statistical tests assuming normal distribution of data, we checked the distribution of raw data and determined the transform that should be applied to get a normal distribution. For each parameter, distribution of the raw data, decimal logarithm and square root transformations were considered. We assessed normality using visual inspection of the distributions, box plots, Q-Q plots (quantile-quantile plots), and by performing the Shapiro-Wilk test.


If none of these transformations allows a normal distribution, non-parametric tests should be used.



Parameter stability and variability

For each parameter, arithmetic mean and coefficient of variation (CV: ratio of the standard deviation (s) divided by mean, in percentages) were used as indicators of  central value and variability of parameters values.

Stability across time of CV was evaluated using a χ² (khi2) goodness of fit test with a significance level of 5% to see whether the observed CV distribution follows a theoretical uniform distribution which values equal to total CV.

Stability across time of mean is evaluated using Tukey's HSD (Honestly Significant Difference) test. Each possible combination of pair of cohorts was tested. We calculated a new indicator (SCP), which is the percentage of comparisons showing a significant difference (with a significance level of 5%), reported to the total number of comparisons performed. Based on this indicator, we determined the thresholds at which we can estimate that the mean is stable or not. These thresholds were established by examining the results of the body weight parameter. For SCP below 12%, parameter’s mean is assessed stable. For SCP above 18%, parameter’s mean is assessed not stable. There is a batch effect. Stability is mixed for SCP between 13 and 17%.

For parameters with non-normal distribution, stability is assessed using a non-parametric test for multicomparisons: Kruskal Wallis.



Reference range

It is the interval between which a certain percentage of measured values of a reference population fall into. It allows to assess the range of physiologic values for a parameter.

About 120 experimental measures are necessary to evaluate it.

Calculation of reference range 95% (which comprises 95 % of values)

RefRange (95%)    = Mean  ± 2. SD

It can be used to detect phenotypes when small groups or partials groups are measured or when there is no control group to compare to. In this case we recommend to when at least 30% of values fall out of  RefRange (95%).


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