The phrase "abundance error" is not a standard expression in written English, but it can be understood in context.
You can use it when discussing a mistake or misconception related to an excess or overabundance of something, particularly in fields like statistics or economics.
Example: "The abundance error in the data analysis led to inflated projections that misrepresented the actual market conditions."
Alternatives: "overestimation mistake" or "excess error".
Isotopomer abundance error tolerance was set to 15% if all three isotopes were present and higher if all isotopes were not present.
These methods constitute the algorithmic foundation of RNA-Seq based approaches, but are not directly applicable to RNA-Seq data owing to the extensive differences in abundance, error sources and biases of the underlying expression measurements.
This initial list was filtered for the presence of silicon (required given the sample preparation and the templates used by the MIDA algorithm) and then subjected to further attrition by the Seven Golden Rules using a 15% isotopomer abundance error (IAE) threshold.
The process of denoising is reliant on abundance information from within a given sample, using high abundance (error-free) reads to correct low abundance ones.
Again, isotopomer abundance errors are on average less than 5%, with median errors less than 2.5%.
For that set of assignments, the distribution of mass and isotopomer abundance errors are displayed in Figure 5B.
The mean mass error for annotated mass spectral features was 2.4 ppm (σ = 2.1 ppm, median = 2.5 ppm); the mean percent error for isotopomer abundances (including, first, second, and third isotopomer abundance errors) was 2.9% (σ = 2.9%, median = 1.7%).
These data indicate that the GC/Q-Orbitrap, with the combination of very high mass accuracy, high resolution, and isotopomer abundance errors on average less than 5%, will be a powerful tool for the determination of unique elemental composition for mass spectral features of interest.
Failure to account for PCR efficiency results in errors in the quantification of miRNA abundance (this error grows depending on the ΔCq such that larger fold changes between samples will be more exaggerated).
Based on the obtained components, only four endmembers are extracted, and Figure 6 shows the obtained abundance and error maps.
The khmer library can be used in various k-mer processing such as abundance filtering, error trimming, graph size filtering or partitioning.
