Efficiency Analysis of Competing Tests for Finding Differentially Expressed Genes in Lung Adenocarcinoma

Abstract

In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA (http://bioinformatics2.pitt.edu/GE2/GEDA.html) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The ‘best’ test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the ‘best’ test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.

Methylome repatterning in a mouse model of Maternal PKU Syndrome

“…Differential methylation was determined using intelligent methods optimization by Efficiency Analysis [30]. This approach selects the most reproducible combination of transformation, normalization, test for differential coverage, and a learned threshold of significance via resampling.…”

“…Methylation of coding gene sets were calculated from .bam files as average coverage of the total sum of exons represented in a canonical protein coding locus, and methylation of non-protein coding annotated genes were represented by the average coverage of the annotated locus. Calculations of average coverage utilized RNASequnator software [30].…”

Environmental Enrichment as a Viable Neurorehabilitation Strategy for Experimental Traumatic Brain Injury

“…The researchers concluded that DAT expression in males undergoes larger decreases after injury, compared to females, but EE exposure has larger effects on postinjury DAT expression in females versus males. 137 Shin and colleagues 138 characterized the mRNA expression profile in substantia nigra (SN) and ventral tegmental area (VTA) at 4 weeks postinjury alone and in combination with enriched housing by using efficiency analysis of microarray data 139 to assess differential gene expression. Aside from alterations in genes involved in inflammation and cellular plasticity, there were also chronic alterations in genes important for calcium and DA signaling pathways in TBI animals exposed to EE, such as down-regulation of tyrosine hydroxylase and up-regulation of alpha-synuclein, which may represent compensatory mechanisms to counteract the detrimental effects of TBI.…”

Altered DNA methylation in PAH deficient phenylketonuria

“…Calculation of average coverage utilized RNASequnator software [16]. Differential methylation was determined by optimized means using Efficiency Analysis [17] which selects the most reproducible combination of transformation, normalization, test for differential coverage, and a learned threshold of significance via resampling. The result is selection of a low-bias measure of differences in average coverage between group-wise comparisons between experimental and control data sets.…”