iDEG: A
single-subject method for assessing gene differential
expression from two transcriptomes of an individual
Qike Li1,2,4, Samir
Rachid Zaim1,2,4, Dillon Aberasturi1,2,4, Joanne Berghout1-3,
Haiquan Li1,2,4, Colleen Kenost1,2,5, Helen Hao Zhang4,7*,
Yves A. Lussier1-6*
Center for Biomedical
Informatics and Biostatistics(CB2)1, Department of Medicine2,
The Center for Applied Genetics and Genomic Medicine3, Graduate
Interdisciplinary Program in Statistics4, BIO5 Institute5, University of
Arizona Cancer Center6, Department of Mathematics7, The
University of Arizona, Tucson, AZ 85721, USA
Email: hzhang@math.arizona.edu, yves@email.arizona.edu
Accurate profiling of gene
expression in a single-subject has the potential to be a powerful precision
medicine tool, useful for unveiling individual disease mechanisms and responses.
However, most expression analysis tools for RNA-sequencing (RNA-Seq) data
require replicate samples to estimate gene-wise data variability and make
inferences, which is costly and not easily obtainable in clinical practice. We
propose the iDEG method to identify individualized Differentially
Expressed Genes in a single subject sampled under two conditions
without replicates, i.e. a baseline sample (e.g., unaffected tissue) vs. a case
sample (e.g., tumor). iDEG borrows information across different genes from the
same individual using a partitioned window in order to strategically bypass the
requirement of replicates per condition. It then transforms RNA-Seq data such
that, under the null hypothesis, differences of transformed expression counts
follow a distribution and variance calculated across a local partition of
related transcripts at baseline expression. This transformation enables
modeling genes with a two-group mixture model, from which the probability of
differential expression for each gene is then estimated by an empirical Bayes
approach with a local false discovery rate control. Our extensive simulation
studies demonstrate iDEG’s improved performance compared to DESeq, edgeR, and
DEGseq under various scenarios. Finally, iDEG is applied to a triple negative
breast cancer single-subject dataset in which a personal set of differentially
expressed genes are identified as an exemplar case report. Software available
at: http://www.lussiergroup.org/publications/iDEG
Keywords: transcriptome, mRNA
expression, RNAseq, differentially expressed genes, single-subject, N-of-1,
RNA-Seq, iDEG.