The phrase "a set of significantly" is not complete and lacks context, making it difficult to assess its correctness in written English.
It could be used in contexts where you are discussing a collection or group of items that have a significant quality or characteristic.
Example: "The researchers identified a set of significantly different variables that influenced the outcome of the experiment."
Alternatives: "a group of notably" or "a collection of considerably".
In order for the latter to be a viable competitor, it has to enable a set of significantly more favourable external (packing) interactions in comparison to the former.
To identify differentially expressed (DE) genes we use a conglomerate classifier that for a given FDR threshold combines three existing methods, limma [18], Cyber-T [19] and SAM [20], to produce a set of significantly DE genes.
Interestingly, GO "biological process" classifications revealed that a set of significantly abnormally expressed miRNAs are associated with disease relapse, which implies that these dysregulated miRNAs might promote the progression of ALL by regulating genes involved in the pathway of the disease development.
To identify significant changes, the following criteria were used: 1) p-value less than 0.05; 2) a fold change of 1.3 was used to define a set of significantly up- and down regulated genes; 3) the over 1.3 fold change should be reproducible across all comparisons.
A set of significantly differentially expressed miRNAs were identified in HCC tissues.
Using hierarchical clustering, a widely used data-mining tool, we identified a set of significantly differentially expressed lncRNAs.
Our efforts focused on a set of 31 significantly down-regulated miRNAs and a set of 71 significantly up-regulated mRNAs identified by the t-test for colon cancer vs. the control samples after multiple hypotheses corrections.
In order to finalize a set of genes significantly up-regulated in a given group compared to another group, among the genes that passed the aforementioned steps, we filtered the ones that are "present" in at least half of the samples in the enriched group using Affymetrix' MAS5 Presence/Absence (P/A) calls.
The model also provided us a set of TFs significantly contributed to cell cycle gene regulation.
In particular a set of variables significantly associated with success in univariate analyses were included in a multiple logistic regression analysis.
For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model.
