Sentence examples for a pca of from inspiring English sources

The phrase "a pca of" is not correct in standard written English without additional context.
It may be used in specific contexts related to statistics or data analysis, where "PCA" stands for Principal Component Analysis.
Example: "In our study, we conducted a PCA of the dataset to identify the main factors influencing the results."
Alternatives: "a principal component analysis of" or "an analysis of principal components".

Exact(60)

A: PCA of the mobilome in 41 enterococcal strains.

Secondly, ST-scales and T-scales consist of a PCA of mostly topological properties [26, 28].

Fig. 2 Spatial distribution of water sources Fig. 3 Trends in water sources development in Karamoja Fig. 4 a PCA of grass species at the waterholes.

The results from a PCA of the individual data sets shows that all samples could be grouped based on their source (Figure 8).

The first ProtFP descriptor (described in more detail below) is based on a PCA of the remaining indices employing 3, 5 or 8 principal components (ProtFP (PCA3), ProtFP (PCA5) or ProtFP (PCA8)), which allows for quantitative comparison of AAs.

Furthermore, a PCA of these 11 genomic characteristics revealed that the "bad bugs" were characterized by a greater number of TA systems compared to the controls (Figure 1).

Partial warp scores (shape variables), including uniform scores, were obtained from these aligned coordinates and were then used in a PCA of each lake assemblage separately, and a PCA on all lakes combined.

In a PCA of the mtDNA haplogroup frequencies, the Iban and southwestern populations (from Sumatra, Java, Bali, Lombok, Melayu Malay, Thailand, and Orang Asli) are separated from all other populations on PC1 (Fig. 4).

Further a deviation in the spectra is seen in the NIR area and below 435 nm. Figure 9 shows a score plot (PC1 versus PC2) of a PCA of the entire dataset after autoscaling.

A PCA of head width, maxillae span, mean maxilla length, mean mandible length and mandible length asymmetry (i.e. weaponry dimensions) resulted in two principal components (PCs) that explained over 97% of the variance in the five weaponry dimensions (Table 3).

With the procedure, we are in line with current investigations in this area, like the work by Torralba et al. [34], who did a PCA of the power spectrum using 5000 images of size 256×256.

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