metaArray

[…] package ., for each cancer training/validation dataset, we normalized the gene expression level into the scale of [−1, 1] by poe (probability of expression) algorithm , implemented in the metaarray library of the r statistical package . based on the gene expression and clinical outcome data from the training dataset, we can assign a wald statistic generated by univariate cox […]

[…] a non-parametric ‘rank product’ method [, ]. rankprod is among the most popular tool which utilizes a non-parametric statistical method and outperforms other meta-analysis methods, including metaarray [], genemeta [], and mama [], by ranking the differentially expressed genes based on false discovery rate. matched diagnosis and relapse samples represent the most ideal biological samples […]

[…] from different platforms is that some genes might be captured with varying quality across platforms. it is unclear what was done to identify problematic probe sets or genes. various r packages (e.g. metaarray) for example calculate integrative correlation scores. these scores identify probe sets which behave differently across platforms in terms of co-expressed genes. another challenge […]

[…] de via distance synthesis (deds) [] to aggregate multiple de measurements. the performance of mdeds was compared with existing meta-analysis methods, such as fisher’s inverse chi-square, genemeta, metaarray, rankprod, and naïve meta-methods, using a simulation study and two case studies []. the results mostly showed better performance for mdeds, while some cases favored the fisher’s inverse […]

[…] in stage ii tumor, we further included an additional lung cancer dataset (gse41271) here. we merged the subjects of stage ii from three independent cohorts (lung1, lung2, and gse41271) using the “metaarray” package in bioconductor (see for details). we found that n39-positive patients (with stage ii tumor) exhibited a significantly increased risk (1.68-fi, p = 0.049 by univariate cox […]

[…] package ., for each cancer training/validation dataset, we normalized the gene expression level into the scale of [−1, 1] by poe (probability of expression) algorithm , implemented in the metaarray library of the r statistical package . based on the gene expression and clinical outcome data from the training dataset, we can assign a wald statistic generated by univariate cox […]