Estimation of False Discovery Rates in Multiple Testing: Application to Gene Microarray Data

Tsai, Chen‐An; Hsueh, Huey-Miin; Chen, James J.

doi:10.1111/j.0006-341x.2003.00123.x

Cited by 138 publications

(114 citation statements)

References 21 publications

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“…9 A variety of genetic lesions including mutations of AML1, N-RAS, K-RAS, C/EBP␣, and the FLT-3 receptor have been identified in varying proportions of these cases. 6,[54][55][56][57][58][59][60] Whether unique expression signatures can be defined for some of these lesions remains unknown. Although only a minority of the cases in our study were analyzed for these lesions, we nevertheless were able to detect a suggestion of clustering for a small subset of the cases containing FLT-3 activating mutations (data not shown).…”

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confidence: 99%

Gene expression profiling of pediatric acute myelogenous leukemia

Ross

Mahfouz

Onciu

et al. 2004

Blood

398

416

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Contemporary treatment of pediatric acute myeloid leukemia (AML) requires the assignment of patients to specific risk groups. To explore whether expression profiling of leukemic blasts could accurately distinguish between the known risk groups of AML, we analyzed 130 pediatric and 20 adult AML diagnostic bone marrow or peripheral blood samples using the Affymetrix U133A microarray. Class discriminating genes were identified for each of the major prognostic subtypes of pediatric AML, including t(15;17) [ PML-RAR␣], t(8;21)[AML1-ETO], inv 16 [CBF␤-MYH11], MLL chimeric fusion genes, and cases classified as FAB-M7. When subsets of these genes were used in supervised learning algorithms, an overall classification accuracy of more than 93% was achieved. Moreover, we were able to use the expression signatures generated from the pediatric samples to accurately classify adult de novo AMLs with the same genetic lesions. The class discriminating genes also provided novel insights into the molecular pathobiology of these leukemias. Finally, using a combined pediatric data set of 130 AMLs and 137 acute lymphoblastic leukemias, we identified an expression signature for cases with MLL chimeric fusion genes irrespective of lineage. Surprisingly, AMLs containing partial tandem duplications of MLL failed to cluster with MLL chimeric fusion gene cases, suggesting a significant difference in their underlying mechanism of transformation. IntroductionAcute myeloid leukemia (AML) is a relatively rare malignancy in the pediatric population, comprising only 15% to 20% of the acute leukemias diagnosed in this age group. 1 Nevertheless, it remains a challenging disease with an inferior treatment outcome compared with pediatric acute lymphoblastic leukemia (ALL). Despite the introduction of new drugs, the aggressive use of allogeneic and autologous bone marrow transplantation, and improvements in supportive care, overall cure rates of AML in most contemporary treatment protocols remain below 60%. [2][3][4][5] Further improvements in cure rates are likely to come from a better understanding of both the molecular abnormalities responsible for the formation and growth of the leukemic cells, and the mechanisms underlying drug resistance.Increasingly, contemporary treatment protocols are incorporating methods for both accurate diagnosis and subsequent risk stratification. To achieve this requires not only distinguishing myeloblasts from lymphoblasts, but also assessing the extent of lineage commitment and differentiation, as well as the presence of specific molecular lesions or chromosomal abnormalities. Efforts over the last several decades have revealed AML to be a heterogeneous disease, with marked differences in cure rates between various genetic subtypes. [6][7][8][9] Acute promyelocytic leukemia was the first clear example of a clinically distinct AML subtype, being characterized by FAB-M3 morphology and expression of the t(15;17)-encoded promyelocytic leukemia-retinoic acid receptor alpha (PML-RAR␣) fusion protein. [10][11][12][13][14] ...

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confidence: 99%

Gene expression profiling of pediatric acute myelogenous leukemia

Ross

Mahfouz

Onciu

et al. 2004

Blood

398

416

View full text Add to dashboard Cite

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“…In such a case, it is known that the sumX i of N dependent Bernouilli trials can be modelled with a beta-binomial distribution, see e.g. Ahn and Chen (1995) or Tsai, et al (2003). This distribution is defined asX Note that, for the sake of simplicity, the beta distribution Beta(α i , σ θ ) in the above is characterized by its mean α i and variance σ θ , instead of its two shape parameters.…”

Section: Discussionmentioning

confidence: 99%

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation

Pinson

McSharry

Madsen

2010

Quart J Royal Meteoro Soc

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Reliability is seen as a primary requirement when verifying probabilistic forecasts, since a lack of reliability would introduce a systematic bias in subsequent decisionmaking. Reliability diagrams comprise popular and practical diagnostic tools for the reliability evaluation of density forecasts of continuous variables. Such diagrams relate to the assessment of the unconditional calibration of probabilistic forecasts. A reason for their appeal is that deviations from perfect reliability can be visually assessed based on deviations from the diagonal. Deviations from the diagonal may, however, be caused by both sampling effects and serial correlation in the forecastverification pairs. We build on a recent proposal, consisting of associating reliability diagrams with consistency bars that would reflect the deviations from the diagonal that are potentially observable even if density forecasts are perfectly reliable. Our consistency bars, however, reflect potential deviations originating from the combined effects of limited counting statistics and serial correlation in the forecastverification pairs. They are generated based on an original surrogate consistency resampling method. Its ability to provide consistency bars with a significantly better coverage against the independent and identically distributed (i.i.d.) resampling alternative is shown from simulations. Finally, a practical example of the reliability assessment of non-parametric density forecasts of short-term wind-power generation is given.

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“…Recently, controlling the FDR has been considered to be more relevant for eliminating false positives, especially in the exploratory studies. A lot of measures, estimation methods, and properties have been examined (Benjamini and Hochberg 2000;Efron et al 2001;Tibshirani 2003;Efron and Tibshirani 2002;Genovese and Wasserman 2002;Storey 2002Storey , 2003Tsai et al 2003;Black 2004;Fernando et al 2004;Pounds and Cheng 2004;Storey et al 2004). In addition to the strategies to control either FWER or FDR, another type of twostage strategy using both FWER and FDR criteria has been proposed.…”

Section: Discussionmentioning

confidence: 99%

Optimum two-stage designs in case–control association studies using false discovery rate

2006

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Genetic association studies using case-control designs are often done to identify loci associated with disease susceptibility. These studies are often expensive to perform, due to a large number of genetic markers. Several types of two-stage designs are proposed and used from the point of cost effectiveness. We proposed to control the false discovery rate for multipletesting correction in two-stage designs, using optimal sample sizes and criteria for selecting markers associated with a disease in each stage to minimize the cost of genotyping. The expected power and cost of two-stage designs were compared with those of one-stage designs, under the assumptions that the genetic markers are independent and total sample size is fixed. The results showed that the proposed two-stage procedure usually reduced the cost of genotyping by 40-60%, with a power similar to that of the one-stage designs. In addition, the sample size and selection criteria, which are optimized parameters, are defined as a function of a prior probability that marker-disease association is true. So, the effects of mis-specification of a prior probability on efficiency were also considered.

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Estimation of False Discovery Rates in Multiple Testing: Application to Gene Microarray Data

Cited by 138 publications

References 21 publications

Gene expression profiling of pediatric acute myelogenous leukemia

Gene expression profiling of pediatric acute myelogenous leukemia

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation

Optimum two-stage designs in case–control association studies using false discovery rate

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