Sentence examples for mean normalization method from inspiring English sources

The global mean normalization method [48] used in our dataset appeared to represent a best fit for the dataset after further validation on selected individual miRNAs.

The proposed weighted mean normalization method could not be compared to quantile normalization in the same fashion as the other methods because quantile normalization does not have a value analogous to CT0 which could be evaluated for stability and compared to weighted mean normalization.

Note that, while the power parameter in the weighted mean normalization method provides a convenient way of adjusting how much one wishes to let the less stable microRNAs influence the normalization of other microRNAs, its optimization currently requires enumeration of different values and using the one with the best overall stability.

The smRNA libraries were scaled by the geometric mean normalization method implemented in DESeq 1.12.0 [ 95] and tested for differential expression via pairwise comparison of experimental treatments (i.e. 4°C, 16°C, 34°C, 36°C, 20 g, 60 g, DC, DS) to the selected reference condition noon with an FDR of 0.1.

We repeat analysis this for the top 10 genes, with the lowest standard deviation in 3. We see slightly higher standard deviations in the weighted mean normalization method compared to the top- k calculations, but the weighted means' CT 0 values are determined to be more stable by geNorm (the lower the value the more stable).

Then, the four microbial abundance matrixes and four environment factor matrixes of spring, autumn, fall, and winter seasons were constructed by normalizing every OTU and environment factor vector with zero-mean normalization method.

Temporal normalization was applied to the filtered waveforms to suppress the effects of earthquakes, instrumental irregularities, and local non-stationary noise sources around the stations by using the running-absolute-mean normalization method proposed by Bensen et al. (2007) with a window length of 10 s.

The zero-mean normalization method [ 16] was used to convert the genewise data from different scales to a common scale in such a way so that mean and standard deviation of the converted genewise data should be zero and one, respectively.

The spectral data were first pre-processed by logarithm linearization, 1st derivative filtering using the Savitzky Golay method and mean normalization.

Note that in Table 3, SIGN_1 means normalization on signal data using method 1 and FEAT_1 means normalization on feature data (i.e. after feature extraction) using method 1.

The three endogenous controls were combined using the dataAssist software V1.0 and "arithmetic mean" as normalization method.