Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes.

ABSTRACT:

Background

Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain aspects of differences between population means and often assumes a relatively simple data distribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may not be well met by the datasets of interest. Making insufficient distributional assumptions can lead to inferior results when dealing with complex differential expression patterns.

Results

We propose to capture differential expression information more comprehensively by integrating multiple test statistics, each of which has relatively limited capacity to summarize t

SUBMITTER: Bei Y 

PROVIDER: S-EPMC5168810 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature