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To determine if the methylation we saw correlated with gene expression in the tumour cells, we compared our data to the expression array analysis previously reported for this same bank of tumours (Palmer et al, 2008).
The high rank correlation we have observed between data arising from different starting amounts of RNA gives cause for optimism that a simple quantile-style normalization of the 50 ng data to the expression profile of the 250 ng data will prove successful.
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The theory can be applied to the determination of the rate constants of the chemical reaction by simply fitting data to the expressions for the potential time curves or using working curves.
Within each data set a further normalisation was performed to standardise the expression data to the median expression value for each probe-set across all replicates (i.e. n = 3, as defined by the original experimenters).
Enabling users to upload expression data to the SGN expression database means it has the potential to become a very useful resource for all researchers in the Solenaceae.
Indeed, in the differential expression analysis all data to the maximum expression are included.
As an initial validation of our hypothesis that GWASs and microarrays tend to identify the same genes, we used a meta-analysis of the Oncomine gene-expression data to assess the expression of the GWAS-identified genes (Table 1).
We compared these data with our miRNA expression data to define the expression pattern of the stomach tissue.
We used the data to analyze the expression of X-linked mature miRNAs across different tissues in mouse.
We used our RNA-Seq data to determine the expression of the 5' and 3' genes in each sample.
Within the existing literature, there are a number of studies that provide corroborating data to validate the expression data in the ABA.
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