The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA.
The Department of Medicine, College of Medicine Tucson, 1501 N. Campbell Ave, Tucson, AZ, 85724-5035, USA.
BMC Med Genomics. 2019 Jul 11;12(Suppl 5):96. doi: 10.1186/s12920-019-0513-8.
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).
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.
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.
基因表达谱分析通过在分子候选物和系统水平上提供临床相关的见解，使医学受益。然而，要采用更“精确”的方法，将个体变异（包括“组学”数据）整合到风险评估、诊断和治疗决策中，需要对单个受试者进行有意义的全转录组表达解读。我们提出了一种“一对一”框架，该框架使用同基因条件下的生物学重复来测试单个受试者（ss）中差异表达基因（DEGs），而无需适当的外部参考标准或重复。为了评估我们提出的“一对一”框架，我们使用五种常规重复锚定分析（NOISeq、DEGseq、edgeR、DESeq、DESeq2）构建参考标准（RS），其余的则分别作为单个受试者样本对（无重复）进行 ss 分析。
在酵母（亲本系与 snf2 缺失突变体；n=42/条件）和 MCF7 乳腺癌细胞系（基线与用雌二醇刺激；n=7/条件）中，比较了八种用于识别差异表达基因的 ss 方法（NOISeq、DEGseq、edgeR、混合物模型、DESeq、DESeq2、iDEG 和集成）。在两个数据集的五个 RS 中，为每个 ss 方法确定了接收器工作特征（ROC）和精度-召回曲线。与这些数据的先前分析一致，无论 RS 方法如何，酵母和 MCF7 数据集分别获得了约 50%和 15%的差异表达基因。NOISeq、edgeR 和 DESeq 是创建 RS 最一致的方法。NOISeq、DEGseq 和集成学习者的单主体版本在没有重复的情况下比较两个转录组时，实现了最佳的中位数 ROC-曲线下面积，无论 RS 方法和数据集如何（酵母>90%，MCF7>0.75）。此外，根据差异表达基因的不同比例，不同的特定单主体方法表现更好。
“一对一”框架为单个受试者 DEG 研究提供了一个诚实的评估框架，因为这些方法是通过设计来评估由不相关的 DEG 方法产生的参考标准的。在这个保守的评估框架中，ss-集成方法是唯一能够可靠地在所有测试条件下产生更高准确性的方法。然而，用于从配对样本中识别 DEG 的单个受试者方法需要改进，因为没有一种方法的精度>90%，召回率适中。
