Novel rank-based statistical methods reveal microRNAs with differential expression in multiple cancer types.

PloS One
Roy NavonZohar Yakhini

Abstract

MicroRNAs (miRNAs) regulate target genes at the post-transcriptional level and play important roles in cancer pathogenesis and development. Variation amongst individuals is a significant confounding factor in miRNA (or other) expression studies. The true character of biologically or clinically meaningful differential expression can be obscured by inter-patient variation. In this study we aim to identify miRNAs with consistent differential expression in multiple tumor types using a novel data analysis approach. Using microarrays we profiled the expression of more than 700 miRNAs in 28 matched tumor/normal samples from 8 different tumor types (breast, colon, liver, lung, lymphoma, ovary, prostate and testis). This set is unique in putting emphasis on minimizing tissue type and patient related variability using normal and tumor samples from the same patient. We develop scores for comparing miRNA expression in the above matched sample data based on a rigorous characterization of the distribution of order statistics over a discrete state set, including exact p-values. Specifically, we compute a Rank Consistency Score (RCoS) for every miRNA measured in our data. Our methods are also applicable in various other contexts. We compare ou...Continue Reading

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Datasets Mentioned

BETA
GSE14985

Methods Mentioned

BETA
flow cytometry
Feature Extraction
xenografts
xenograft

Software Mentioned

Agilent
minRCoS
SAM
RCoS
statistics
Feature Extraction
GeneView

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