Remaining missing values represent truly absent metabolites in the samples and are typically imputed by taking an estimate of the background noise (see also reference [ 52]).
These missing values represent spots that did not pass the quality selection criteria for various reasons, either technical (e.g., bad spotting of the DNA on the microarray) or biological (e.g., no transcripts of that gene in this fraction).
It is still an open question whether to consider the ICF qualifiers "8 – not specified" and "9 – not applicable" missing values represents the best strategy to cope with these response alternatives.
Missing values are represented as white in the heatmap.
In the LLSimpute method [ 18], a target gene with missing values is represented as a linear combination of k similar genes.
Further, if we consider the missing values as representing participants who lack statistical competencies, this could result in a biased underestimation of these competencies.
Exclusion of 150 individuals from the sample because of missing values might represent another weakness.
SVM-based models could classify according to zero values representing missing values and not true zero intensities.
Missing values in Z represent cases in which the mRNA signal in the microarray was too low to be reliably measured.
Missing values in Table 1 represent failed measurements, where the milk sample froze too early or did not freeze at all. Figure 4 Individual measurements at days 1, 4 and 7. From an analytical point of view, milk is a rather complicated sample.
Due to general limitation of MALDI technology, a significant fraction of these zero values could represent missing values rather than true zero intensities.
