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Tree diagrams constructed by cluster analyses, using eight length factors in a given GPCR, revealed possible length relations among GPCRs and defined at least three groups.
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We constructed two spatial cluster analyses using participant HRQoL responses.
Cluster 3.0 [69] was used to perform hierarchical cluster analyses using uncentered correlation and average linkage.
Then do the hierarchical clustering analyses using default parameters.
Hierarchical clustering analyses used Cluster developed by the Eisen lab [23].
Interestingly, the variables that participate most to the definition of the patients subgroups identified by cluster analyses actually do not fit with those used in the GOLD A-B-C-D classification (see Table 2).
Results of our simulation studies indicated at least four significant cases (Q i (k ) p = 0.001 and Q i, t (k ) p ≤ 0.05) nearby one another to declare a region a potential cluster, accompanied by confirmatory analyses using spatial-only scanning windows (SaTScan).
We can quantify the evolution of the height R of the clusters by the AFM analyses using the SPMLabAnalyses V7.00 software that define each grain area by the surface image sectioning of a plane that was positioned at half grain height.
To investigate which aspects of the LUC expression underlie the observed spatial organisation detected by the cluster analyses, we used two statistical approaches including notch‐filtering of the Fourier spectra and spectral embedding to investigate a dimensionally reduced signal.
The markers were clustered into linkage groups by two-point analyses using RH2PT and a criterion of a lod score greater than or equal to 4. Multipoint analyses were performed on linkage groups as well as sparse linkage groups together with markers from other linkage groups.
The clustered sequences were then annotated based on similarity searches performed by batch BLAST analyses using the Greengene interface.
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