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Although different statistical approaches have been proposed for analyzing microarray time-course data, method for analyzing such data collected using the popular case control design in clinical investigations has not been proposed perhaps due to the increased complexity for the existing parametric or non-parametric approaches.
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This paper presents missing data methods for repeated measures data in small samples.
Next, using plant and operator data, methods for creating informed prior distributions for Bayesian analyses are covered.
We investigated five missing data methods, for which code is freely available within the R statistical software (Table 3).
To facilitate interpretation of metabolomics data, methods for identifying the chemical formula of detected features are greatly needed.
This approach permits complete data methods for analysis, reduces bias due to missing data, and incorporates imputation error into standard errors.
With the availability of high throughput genomics data, methods for cancer class classification and prediction based on molecular information have been vigorously pursued in recent years.
We will use several missing data methods for imputing data and re-analyze using intention to treat (as randomized) to assess the impact of missing data on our conclusions as recommended [ 41].
Genes with higher scores are those with higher probability of being disease D. According to the type of input data, methods for gene prioritization can be classified into text and data mining methods, as well as network-based methods.
Here, we have presented a semiautomated data processing method for extracting XANES data from STXM data sets.
After preprocessing, time series analysis for each subject was based on a wavelet-based data resampling method for fMRI data [Bullmore et al., 1999, 2001].
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