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RR and EPD thresholds are then generated for the stage I test, while ensuring error rates are maintained for the entire study.
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Subsequently, matrices of column models were generated for the second stage (Stage II) of the parametric study.
Receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC), 95% confidence intervals, and P values were generated for the MEP score, clinical stage, tumor grade, and a combination of these variables.
Rules were generated for stage II such that the null hypothesis (Hnul) was rejected if either RR or TTP met desired thresholds, and accepted if both did not.
Stopping rules were generated for stage I such that Hnul was accepted and the study stopped if both RR and EPD were unacceptable.
Genotype data were generated for 197 individuals (stage 1).
For zebrafish transcripts, profiles were generated for each developmental stage with a 3P-seq dataset.
These studies were very influential on the prevalence estimates generated for these treatment stages.
A total of 8,725, 9,537, 5,260 and 6,625 ESTs were generated for each respective developmental stage.
Next, normalised read counts were generated for each of the three different biological stages using the DESeq algorithm [ 19], thereby allowing a comparison of gene transcript levels between different samples.
To this end, TSS seq data was generated for larvae and pupae developmental stages and mapped to the tsetse genome (version GmorY1) to characterize the promoters and delineate the primary TSS location.
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