A standardized fold change method for microarray differential expression analysis used to reveal genes involved in acute rejection in murine allograft models

Abstract

Murine transplantation models are used extensively to research immunological rejection and tolerance. Here we studied both murine heart and liver allograft models using microarray technology. We had difficulty in identifying genes related to acute rejections expressed in both heart and liver transplantation models using two standard methodologies: Student's t test and linear models for microarray data (Limma). Here we describe a new method, standardized fold change ( SFC ), for differential analysis of microarray data. We estimated the performance of SFC , the t test and Limma by generating simulated microarray data 100 times. SFC performed better than the t test and showed a higher sensitivity than Limma where there is a larger value for fold change of expression. SFC gave better reproducibility than Limma and the t test with real experimental data from the MicroArray Quality Control platform and expression data from a mouse cardiac allograft. Eventually, a group of significant overlapping genes was detected by SFC in the expression data of mouse cardiac and hepatic allografts and further validated with the quantitative RT ‐ PCR assay. The group included genes for important reactions of transplantation rejection and revealed functional changes of the immune system in both heart and liver of the mouse model. We suggest that SFC can be utilized to stably and effectively detect differential gene expression and to explore microarray data in further studies.

High dimensional model representation of log-likelihood ratio: binary classification with expression data

“…Note the top ranking IPA gene network is identified with cellular development, cellular growth, and cell cycle functions. The log fold changes, computed using method of [45], were used to identify over/under-expressed genes in the network; however, as data is highly heterogeneous, these effects might not be highly pronounced. Many of the selected genes are connected directly or indirectly with only one gene in between.…”