Overall, both the sample impurity and landscape heterogeneities can largely affect the classification accuracy while only configurational heterogeneity has significant influence on crop area estimation.
The objective of this study is to analyze the accuracy of crop classification and area estimation affected by spatial heterogeneities, especially for sample impurity and landscape heterogeneity.
Although the distribution of variant allele fraction provides an indication about the clonal architecture, the analysis of this data is affected by sample impurity and copy number alterations.
In both samples, impurity concentrations, principally oxygen and nitrogen, were found to be low relative to boron dopant levels.
This is a good illustration of the power of mass spectrometry to detect sample impurities and distinguish between closely related species.
Analysis of cancer sequencing data has unique challenges, including: methods for analysing matched cancer normal samples to distinguish germline polymorphism from somatic variation; genome rearrangements that do not align well to the reference; and cancer sample heterogeneity from subclonal variation and sample impurity (Ding et al., 2010; Gundry and Vijg, 2012; Meyerson et al., 2010).
There are additional sources of error in NMR spectra, including missing signals, chemical shift degeneracy, sample impurity, water bands, artifacts and experimental errors [2].
This is a particularly significant problem in cancer sequencing, as subclonal variation and sample impurity give rise to mutations at the same low allelic fractions as aggregations of systematic error.
Second, there are various sources of errors in NMR spectra, including random noise, sample impurities, artifacts and water bands, which makes peak picking a very challenging problem.
Regions with more homogenous pixels are more likely to be accurately classified and vice versa; (2) the crop area estimation error is less sensitive to sample impurity.
