Sentence examples for feature abundance from inspiring English sources
Statistical analysis was conducted on the rarefied and trimmed feature abundance matrices using RV.3.0.1.
Richness was calculated on the rarefied, not normalized, feature abundance matrices on the given level.
Feature abundance matrices were transformed by adding 1 to each feature and calculating log10 subsequently, avoiding negative infinite values for absent features.
From OTU abundance and their respective taxonomic classifications, feature abundance matrices were calculated at different taxonomic levels, representing OTU and taxa abundance per sample.
To make comparisons, sample-wise feature abundances (i.e. the abundance of different phyla, classes, families, genera and OTUs in different samples) derived from the QIIME pipeline were statistically analyzed on normalized feature-abundance matrices using R 2.11.0 (freeware; http://www.r-project.org/).org/
Typically, feature abundances are estimated by peak integration and the resulting peak areas are used for relative quantification and comparison of different experimental states.
Multiple charge states were detected per glycan as a product of the electrospray ionization, and the feature abundances were added together via the GlyQ-IQ software.
The significance of differential abundance between groups of samples was calculated on the transformed feature abundances using a t-test (P-value) that was corrected for multiple testing by calculating the Benjamini Hochberg false discovery rate (q-value).
Another advantage of the NMF approach over the standard FLIM data analysis methods is that the NMF features (abundance maps) can be obtained directly from the spectro-temporal FLIM intensity data in a computationally efficient manner without the need for time deconvolution.
Metagenomic features abundances significantly associated with host phenotype.
Feature-abundance matrices were transformed by adding 1 to each feature and calculating log10, avoiding negative-infinite values for absent features.
