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We counted the overlap of DE gene symbols in this mouse dataset with gene symbols Trdmt1, Bhmt, Dmgdh, Sardh, Gnmt, Ckb, Ckmt1, Mat1a, Mat2a, Ckm, Gatm, Gamt and 12 randomly sampled gene symbols.

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The validation set was composed of one dataset of S.cerevisiae with expression data of several knockout yeast for transcription factor involved in oleate response, and two mouse datasets with experiments belonging at three category of experimental design mentioned above.

To examine the robustness of the MD statistic to changes in replication, we considered a comparison between two mouse datasets with larger replication, the ESCs dataset of (Booth et al., 2014) with four replicates and the neural dataset of Varela-Rey et al.

We chose to work with the mouse dataset because it contained several tissue types with high data coverage.

These were then combined with the wild mouse dataset and a PCA was performed on this combined dataset.

With our human and mouse dataset (Fig. 3), and all other datasets we have considered, this histogram is strongly bimodal.

The correlations were calculated per mouse dataset.

Similarly, correlations of non-muscle human tissue in dataset H3 to mouse dataset M are lower and do not show a significantly higher correlation to any mouse muscle group – with one exception in the ST3 sample profile characterization where among non-muscle human tissue, brain and spinal cord had significantly higher correlation with mouse soleus than with non-soleus muscle profiles.

The previous human dataset and a mouse dataset were used for the comparison.

For mouse datasets, the probe numbers with high CV were too small to conduct a similar analysis.

We began with the mouse datasets because they included more stimulators and denser kinetics than the human datasets.

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