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To statistically test clonality within the samples, we utilized "Equivalence Acceptance Criteria" by performing "Two one-sided test procedure (TOST)" on the normalized RGB values.
To determine the breed structure of the 119 samples we utilized the STRUCTURE [29] PLINK (http://pngu.mgh.harvard.edu/purcell/plink) and HCLUST (http://cran.r-project.org/) programs.
To fill in missing genotypes for some samples, we utilized genotypes from the HapMap public release #28 (Phase I, II+III).
For blood samples, we utilized the tool Cn.
To more directly test for a correlation in patient samples, we utilized the RNAseq database generated by TCGA.
To assess the performance of our Nanostring assay in FFPE samples, we utilized an independent cohort of patient-matched fresh-frozen and FFPE specimens (N = 45).
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Based on the Latin hypercube sampling, we utilized the back propagation neural network (BPNN) and the least squares support vector machine (LS-SVM) to establish the mathematical models.
In that case, a selection bias would be introduced to the cross-sectional sampling we utilized, leading directly to the observed difference between the two groups.
To address these issues in a broad sample, we utilized multiple years of a nationally representative dataset to assess whether the practice of performing LP for EONS varied by child, hospital or insurance characteristics for normal-birth weight infants.
To evaluate phylogenetic informativeness as a procedure for selecting loci to sequence for phylogenetic studies that incorporate broad taxon sampling, we utilized empirical data sets for which the process of evolution may only be approximated, as well as simulated data for which we could specify the process and the true tree.
To test the validity of these findings in a larger sample, we utilized the Swedish Childhood Cancer Registry, which contained clinical information and data on the copy number of MYCN in tumors for 240 cases of neuroblastoma that were diagnosed during almost the same time period as the cases in the present study.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com