Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here, we present a multiple imputation method that accommodates high dimensional gene expression data by incorporating principal component analysis of the transcriptome into the multiple imputation prediction models to avoid bias. Simulation studies using three datasets show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R Bioconductor package, RNAseqCovarImpute that integrates with the limma-voom pipeline for differential expression analysis.

RNA-sequencing (RNA-seq) technology has led to a surge of neuroscience research using animal models to probe the complex molecular mechanisms underlying brain function and behavior, including substance use disorders. However, findings from rodent studies often fail to be translated into clinical treatments. Here, we developed a novel pipeline for narrowing candidate genes from preclinical studies by translational potential and demonstrated its utility in 2 RNA-seq studies of rodent self-administration. This pipeline uses evolutionary conservation and preferential expression of genes across brain tissues to prioritize candidate genes, increasing the translational utility of RNA-seq in model organisms. Initially, we demonstrate the utility of our prioritization pipeline using an uncorrected P-value. However, we found no differentially expressed genes in either dataset after correcting for multiple testing with false discovery rate (FDR < 0.05 or <0.1). This is likely due to low statistical power that is common across rodent behavioral studies, and, therefore, we additionally illustrate the use of our pipeline on a third dataset with differentially expressed genes corrected for multiple testing (FDR < 0.05). We also advocate for improved RNA-seq data collection, statistical testing, and metadata reporting that will bolster the field\'s ability to identify reliable candidate genes and improve the translational value of bioinformatics in rodent research.

This paper presents a consensus-based approach that incorporates three microarray and three RNA-Seq methods for unbiased and integrative identification of differentially expressed genes (DEGs) as potential biomarkers for critical disease(s). The proposed method performs satisfactorily on two microarray datasets (GSE20347 and GSE23400) and one RNA-Seq dataset (GSE130078) for esophageal squamous cell carcinoma (ESCC). Based on the input dataset, our framework employs specific DE methods to detect DEGs independently. A consensus based function that first considers DEGs common to all three methods for further downstream analysis has been introduced. The consensus function employs other parameters to overcome information loss. Differential co-expression (DCE) and preservation analysis of DEGs facilitates the study of behavioral changes in interactions among DEGs under normal and diseased circumstances. Considering hub genes in biologically relevant modules and most GO and pathway enriched DEGs as candidates for potential biomarkers of ESCC, we perform further validation through biological analysis as well as literature evidence. We have identified 25 DEGs that have strong biological relevance to their respective datasets and have previous literature establishing them as potential biomarkers for ESCC. We have further identified 8 additional DEGs as probable potential biomarkers for ESCC, but recommend further in-depth analysis.