Sentence examples for microarray data series from inspiring English sources

For the comparative transcriptome analyses between rice and Arabidopsis, we downloaded Arabidopsis Affymetrix microarray data series GSE5630, GSE5631, GSE5632, GSE5633, GSE6162, GSE6696, GSE12316, GSE17343, and GSE27281.

In addition, we analyzed the meta-expression patterns of Arabidopsis PRX genes in six tissues/organs using the Arabidopsis Affymetrix microarray data series GSE5630, GSE5633, GSE5631, GSE5632, GSE5634, GSM943445, and GSM943446.

Microarray data series were deposited under accession codes GSE 13623 and GSE 2479, respectively, at Gene Omnibus.

Next, in a published microarray data series of 117 LUAD patients (GSE37138), we confirmed that the expression level of p27 negatively correlated with that of LUADT1.

Microarray data series were submitted to the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) under the accession number GSE38700.

Expression analyses in response to abiotic and biotic stresses were based on microarray data (series matrix accession numbers GSE31594, GSE31677, GSE6404, GSE12842 and GSE31660) downloaded from the NCBI gene expression omnibus (GEO) datasets.

Three types of microarray data (time series, non-time series and mixed type) were used as benchmark datasets, and the performance of each algorithm was evaluated using three kinds of measures (NRMSE, CPP and BLCI) and the average of these measures (called the average index).

Microarray data (GEO series GSE19368) were filtered by omitting features with a lack of sequence information, known ribosomal content, or that had faint array signal (<2 SD above background).

The microarray platform, data series, and raw data including the.tif image and.spot files are available from the Gene Expression Omnibus website [ 24] under accessions GPL3365, GSE7499, and GSM181765-GSM181823.

In order to compare gene expression profiles from these samples using RNA-seq and microarray technologies, we first reanalysed the microarray data using a series of Bioconductor packages.

To analyze the data we developed a computational method that combines Fast Fourier Transform (FFT) and Gene Set Enrichment Analysis (GSEA) to correct for temporal differences between samples and compare microarray data between time series.