Sentence examples for microarray datasets consisted from inspiring English sources

Because cell type greatly influences expression patterns [ 28], and because our microarray datasets consisted of cell-type-specific batches that confound effects from these two variables, each cell type was treated as a separate dataset for processing.

We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method.

(Additional file 1: Table S3) To further validate the use of these tissue-specific GETs as predictors of normal human tissues, an extensive tissue-prediction analysis was carried out on a total of 61 microarray datasets consisting of 797 samples from 16 different human tissues.

This was accomplished by generating co-expression clusters using LSTs as seeds to cluster other genes from two microarray datasets consisting of transcript profiles from 18 cattle-tissues and liver of animals fed two different diets at several peripartal time-points.

We used a microarray dataset consisting of 22 different murine tissues, with 3 5 replicates for each tissue (in total 70 microarrays).

The analysis was done on a microarray dataset consisting of 65 prostate cancer and 63 cancer-adjacent control tissue samples from the same patients [37].

The analysis was done on a microarray dataset consisting of 39 paired pancreatic cancer and cancer-adjacent control tissue samples from the same patients [32].

The analysis was done on a microarray dataset consisting of 58 lung cancer tissue and 49 cancer-adjacent control tissue samples from the same patients [26].

The analysis was done on a microarray dataset consisting of 89 stomach cancer and 23 cancer-adjacent control tissues from the same patients [43].

Our analysis was done on a microarray dataset consisting of 53 colon cancer and 28 cancer-adjacent control tissues from the same patients (some of the cancer samples have no reference samples) [18].

For each neurodegenerative disease studied, we trained our age predictor on a reference dataset assaying gene expression in normal controls from the same brain region used in the target dataset (microarray dataset consisting of diseased individuals and their neurologically normal controls).