Sentence examples for datasets are normalized to from inspiring English sources

Since all datasets are normalized to allow integrating datasets from multiple sources, a large number of samples from different tissues can be compiled into a single data set where each row represents one gene and each column represents one tissue.

The abundance of each dataset was normalized to RP10M (reads per 10 million).

In order to enrich for mRNAs that are predominantly regulated by changes in translational efficiency (as opposed to transcript abundance), the dataset was normalized to transcript levels in unfractionated RNA.

Because the Experiment 1 dataset represented a large diversity of read lengths, greatly impacting inference capacity [ 38], the dataset was normalized to assess sequencing platform biases rather than read length impacts as follows: (i) the longer Ion Torrent and 454 reads were trimmed to 100bp, and (ii) only reads ≥100 bp were used from Illumina data.

All 17 datasets are normalized and transformed into log scale.

When individual datasets are normalized separately, the regression coefficients and intercepts can be different.

Thus, to account for this variability across datasets, all base kernels are normalized to unit trace norm in the experiments discussed later in the text.

All 3 datasets were normalized prior to network inference.

Datasets were normalized prior to analysis [ 10, 11].

First, the datasets were normalized using quantile normalization to ensure that inherent large-scale expression differences in the datasets based on different sources and laboratories were minimized.

For this analysis, each nCounter dataset was normalized to two cell-type-specific control genes, while microarray datasets were not normalized to control genes.