Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine.

BACKGROUND: Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more 'precision' approach that integrates individual variability including 'omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an "all-against-one" framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed "all-against-one" framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates). RESULTS: Eight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n = 42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n = 7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, ~ 50% and ~ 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (> 90% in Yeast, > 0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs. CONCLUSIONS: The "all-against-one" framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision> 90% and obtained moderate levels of recall. http://www.lussiergroup.org/publications/EnsembleBiomarker.

Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes.

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%90%, recall>75%, false_positive_rate<1%) and 30%