Lemon William J, Liyanarachchi Sandya, You Ming
Department of Surgery, 4940 Parkview Place, 10130 Wohl Clinics, Washington University in St Louis, St Louis, MI 63110, USA.
Genome Biol. 2003;4(10):R67. doi: 10.1186/gb-2003-4-10-r67. Epub 2003 Sep 10.
Logit-t employs a logit-transformation for normalization followed by statistical testing at the probe-level. Using four publicly-available datasets, together providing 2,710 known positive incidences of differential expression and 2,913,813 known negative incidences, performance of statistical tests were: Logit-t provided 75% positive-predictive value, compared with 5% for Affymetrix Microarray Suite 5, 6% for dChip perfect match (PM)-only, and 9% for Robust Multi-array Analysis at the p < 0.01 threshold. Logit-t provided 70% sensitivity, Microarray Suite 5 provided 46%, dChip provided 53% and Robust Multi-array Analysis provided 63%.
Logit-t采用对数变换进行归一化，然后在探针水平进行统计检验。使用四个公开可用的数据集，总共提供了2710个已知的差异表达阳性事件和2913813个已知的阴性事件，统计检验的性能如下：在p<0.01阈值时，Logit-t的阳性预测值为75%，相比之下，Affymetrix微阵列套件5为5%，仅dChip完美匹配（PM）为6%，稳健多阵列分析为9%。Logit-t的灵敏度为70%，微阵列套件5为46%，dChip为53%，稳健多阵列分析为63%。
