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In general, single gene expression analysis looks into highly differentially expressed genes under a certain cutoff (e.g., P value, fold-change).
In addition to identification of novel interactions, single gene expression profiles permit identification of genes that are not expressed at significant levels in the brain region of interest.
The former ones are composed of single gene expression modules.
Average gene set expression or single gene expression (counts or UMIs per 10,000 reads) was visualized using a customized FeaturePlot function with both color and size proportional to expression value plotted in ascending order of expression.
Although these databases are of significant biomedical value, they do hold significant drawbacks, especially concerning the reliability of single gene expression profiles obtained by microarray data.
Advanced single-cell microscopy techniques together with mathematical models for single gene expression led to important insights in elucidating the sources of intrinsic noise in prokaryotic and eukaryotic cells.
One strategy to resolve this heterogeneity is to make use of multiple gene expression patterns that are more powerful in defining individual characteristics and predicting outcomes than any single gene expression pattern.
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Values obtained from each plant's two dye-swap replicates were combined into a single gene-expression vector (ignoring missing values).
To annotate the clusters found, we used clinicopathologic characteristics, 10 IHC markers, 17 gene-expression signatures (GES), GO biological process terms enrichment, and intuitive single gene-expression approach.
Cluster functional annotation results are detailed in the five following sections: clinicopathologic characteristics, IHC, GES, single gene-expression intuitive approach and GO biological process terms enrichment.
To analyse differences between experimental groups at single-gene expression level, the STATA statistical package was used [45].
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