Sentence examples for between the two normalization from inspiring English sources

In this table, it is difficult to say which is better between the two normalization strategies.

Our experimental results in Tables 4 and 5 and 6 show that the choice between the two normalization strategies is problem dependent.

We observed high correlations between the two normalization methods: for FDG-PET, the r's ranged from 0.85 (HIP) to 0.98 (IP) (p's < .001), and for PIB-PET the r's ranged from 0.87 (STG) to 0.92 (IP) (p's < .001).001

Between the two normalization strategies, TrinityNorm outperformed DigiNorm at each reconstruction level in terms of numbers of mouse genes found and percentage of coding sequence reconstructed (Table  2 and Figure  1).

In fact, the correlation between the outputs of the two normalization methods was quite limited, as evidenced by plotting the two corresponding genotype p-values for each probe (r2 = 0.07) - although we did observe a stronger correlation among the lower p-values (data not shown).

The only difference between these two normalization methods is the gene length, which is constant for a given gene across samples.

Because most of the probes are predicted to have different signals between the two channels, traditional normalization methods, which usually assume a bulk of non-differentially expressed genes in the data set, cannot be readily used.

The Splicing Index (SI) represents the log ratio of the exon intensities between the two tissues after normalization to the gene intensities in each sample: SIi = log2((e1i/g1j)/(e2i/g2j)), for the i-th exon of the j-th gene in tissue type 1 or 2. The splicing indices are then subjected to a t-test to probe for differential inclusion of the exon into the gene.

In the last step, Concordance Assessment, we assess concordance between the three FREEC normalizations.

The SI represents the ratio of the exon intensities between the two tested conditions following normalization to the gene intensities and serves to measure the exon inclusion level.

The splicing index is defined as the log-ratio of the exon intensities between the two sample types after normalization to the gene intensities in each sample: SI i  = log2((e1i/g1j) / (e2i/g2j) ), for the i-th exon of the j-th gene in sample type 1 or 2. If a given exon provides the same signal as the whole gene, the exon/gene ratio will be 1 and the normalized value (log2) will be 0.