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A twofold change expression cut-off for ratio experiments was applied together with anti-correlation of ratio profiles rendering the microarray analysis set highly significant (P-value<0.01), robust and reproducible.
RNA was extracted from all tissues for oligonucleotide microarray analysis (set 1 only) and gene expression quantitation by real time PCR.
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Microarray transcript analysis sets itself a very ambitious goal in attempting to measure transcription changes in more than 5000 yeast genes simultaneously.
Using differential display of gene expression by microarray analysis, one set of 101 upregulated RA-related genes and one set of 300 gene transcripts considered to be downregulated in RA were detected and are now available for further research.
By in silico comparative genomics and DNA microarray analysis, a set of different regions (DFRs) were identified in the genomes of different Y. pestis strains [19], [20].
Before proceeding to microarray analysis each replicate set was checked for effective FGF inhibition.
For example, Schaner et al (2003), using cDNA microarray analysis, identified a set of genes that are differentially expressed in clear cell tumours compared to other ovarian subtypes.
The microarray analysis identified a set of genes related to heat shock as well as olfaction that showed altered expression in response to temperature increases.
Where possible, MR-GSE is used with moderated t-statistics rather than ordinary t-statistics, as these are preferable for microarray analysis including gene set testing [ 56, 58].
After microarray analysis, data sets were analyzed using GeneSpring GX 11.0 software for gene expression, clustering, gene ontology, and significant pathway signaling.
For microarray analysis, three sets of RNA samples representing the three biological replicates for each time point (5 min, 30 min, 60 min and 120 min) and condition (control and elicitor-treated) were prepared.
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