Description
Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential
opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without
replicates (TCWR ) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor),
differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution
calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG
is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive
simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%<DEGs<20% (precision>90%,
recall>75%, false_positive_rate<1%) and 30%<DEGs<40% (precision=recall~90%), respectively.
The proposed iDEG method borrows localized distribution information from the same individual, a strategy that
improves accuracy to compare transcriptomes in absence of replicates at low DEGs conditions.
http://www.lussiergroup.org/publications/iDEG
Learning Objective: The attendee will learn how to borrow distribution information from related genes in order to interpret differentially expressed genes between two conditions, each with one sample without replicate.
Authors:
QIke Li, University of Arizona
Samir Rachid Zaim (Presenter)
University of Arizona
DIllon Aberasturi, University of Arizona
Joanne Berghout, University of Arizona
Haiquan Li, University of Arizona
Francesca Vitali, University of Arizona
Colleen Kenost, University of Arizona
Helen Zhang, University of Arizona
Yves Lussier, University of Arizona
Presentation Materials: