For principal component analysis, read counts were transformed into counts per million and then log2-transformed.
We apply FDA, and the corresponding functional version of principal component analysis, to analyze FM count data from a Norwegian prospective cohort study of women with singleton pregnancies.
Principal component analysis (PCA) of normalized counts for host genes confirmed separate groupings of non-infected (controls) and infected lungs (Fig. 1).
For principal component analysis (PCA), the abundance counts for each index subcategory were normalized to the total number of sequences in the index for a given sample.
Principal component analysis, based on total counts of each polar amino acid type, showed a distinct separation of species from cold and warm climates, with tropical octopods being most distant from Antarctic and Arctic octopods.
To test whether the presence or absence of EBV is a major determinant of genetic requirements, we performed principal component analysis (PCA) on normalized sgRNA counts (Fig. 1f).
Principal component analysis (PCA), using log2 sense tag counts per gene, was used to plot each sample relative to all others (Additional file 1).
As part of the quality control a principal component analysis plot was created using rlog-transformed count data; the plot indicated that samples clustered predominantly according to time-point (Supplementary Figure 11).
Running wavelet PCA for all 148 FM counting series, the first three temporal principal component curves explained 75.3%, 8.6%, and 3.3% (total, 87.2%) of the total variation among FM counting charts, respectively.
Principal Component Analysis (PCA) using batch-effects-corrected raw counts was used to identify potential sample outliers.
Principal component was used to derive a hemolytic component from reticulocyte count, lactate dehydrogenase, aspartate transaminase and bilirubin.
