Thioguanine nucleotides (TGN) are considered the principal active metabolites exerting the antileukemic effects of mercaptopurine (MP). Numerous clinical studies have reported substantial ...inter-patient variability in intracellular TGN concentrations during continuation therapy of acute lymphoblastic leukemia (ALL). To identify genes whose expression is related to the intracellular accumulation of TGN in leukemia cells after in vivo treatment with MP alone (MP) or in combination with MTX (MP+MTX), we used oligonucleotide microarrays (Affymetrixâ HG-U95Av2) to analyze the expression of approximately 9,670 genes in bone marrow leukemic blasts obtained at diagnosis from 82 children with ALL. TGN levels were determined in bone marrow aspirates of these patients 20 hours after mercaptopurine infusion (1 g/m2 I.V). Because, as previously reported, patients treated with MP alone achieved higher levels of intracellular TGN compared to those treated with the combination, we used Spearman's rank correlation to identify genes associated with TGN levels separately for the 33 patients treated with MP alone and the 49 with the combination (MP: median TGN: 2.46 pmol/5x106 cells, range: 0.01–19.98; and MTX+MP: median TGN: 0.55 pmol/5x106 cells, range: 0.005–3.31). Hierarchical clustering using these selected probe sets clearly separated the 33 patients treated with MP alone into two major groups according to TGN concentration (< 2.46 and > 2.46 pmol/5x106 cells; n=60 genes) and two major branches were also found for patients treated with the combination (< 0.55 and > 0.55 pmol/5x106 cells; n=75 genes). Interestingly, there was no overlap between the two sets of genes, indicating that different genes influence the accumulation of TGN when this drug is given alone or in combination with MTX. The association between gene expression profiles and TGN levels determined by leave-one-out cross-validation using support vector machine (SVM) based on Spearman correlation, was rho=0.60 (p<0.001) for MP alone and rho=0.65 (p<0.001) for MTX+MP, with false discovery rate (FDR) computed using Storey's q-value (MP: 50% true positive, MTX+MP: 70% true positive respectively). Genes highly associated with the post-treatment TGN level in ALL patients treated with MP alone encode transporters, enzymes involved in the MP metabolic pathway and cell proliferation. Genes associated with post-treatment levels of TGN after combined therapy have been implicated in protein and ATP biosynthesis. Together, these in vivo data provide new insights into the basis of inter-patient differences in TGN accumulation in ALL cells, revealing significant differences between treatment with MP alone or in combination with MTX.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Regimen-related toxicity, one of the main reasons for discontinuation of chemotherapy in acute lymphoblastic leukemia (ALL), can not only be life-threatening but may affect the risk of relapse. In ...246 children with newly-diagnosed ALL who were treated using a single protocol (St. Jude Total XIIIB), we used a candidate-gene approach to determine whether acute toxicities gastrointestinal (stomatitis or diarrhea), hyperbilirubinemia, infection, or neurotoxicity were related to 16 common polymorphisms in genes plausibly linked to the pharmacodynamics of the drug therapy. During the high-dose methotrexate consolidation therapy, only genotypes related to methotrexate disposition were associated with grade 3–4 gastrointestinal toxicity, adjusting for demographic factors and ALL risk group. Patients having the RFC AA/AG (p = 0.025) genotypes or those having the MTHFR 677 TT (p = 0.048) genotypes had a higher risk of the toxicity (OR = 10.4 and 3.2 respectively), than those with RFC GG or MTHFR CT/CC. In addition, the risk of hyperbilirubinemia during consolidation was higher among those with the lower activity UGT1A1 7/7 genotype than those with other UGT1A1 genotypes (OR = 12.2, p < 0.0001). Adjusting for time at risk over the entire therapeutic period, RFC AA/AG was again prognostic for gastrointestinal toxicity (p < 0.0001); the GSTP1 AA/AG genotypes (p = 0.021) and TPMT heterozygous genotypes (p = 0.047) were also risk factors. The RFC AA/AG genotype was also a modest risk factor for infection (p = 0.05). There were only weak predictors of neurotoxicity, with the MDR1 exon 26 TT genotype a risk factor (p = 0.025) for presumed methotrexate-related neurotoxicity over the entire therapeutic period. We conclude that germline polymorphisms influence the toxicity of antileukemic therapy and their identification may provide a tool for tailoring therapy in childhood ALL.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The methodological advancement in microarray data analysis on the basis of false discovery rate (FDR) control, such as the q-value plots, allows the investigator to examine the FDR from several ...perspectives. However, when FDR control at the ``customary" levels 0.01, 0.05, or 0.1 does not provide fruitful findings, there is little guidance for making the trade off between the significance threshold and the FDR level by sound statistical or biological considerations. Thus, meaningful statistical significance criteria that complement the existing FDR methods for large-scale multiple tests are desirable. Three statistical significance criteria, the profile information criterion, the total error proportion, and the guide-gene driven selection, are developed in this research. The first two are general significance threshold criteria for large-scale multiple tests; the profile information criterion is related to the recent theoretical studies of the connection between FDR control and minimax estimation, and the total error proportion is closely related to the asymptotic properties of FDR control in terms of the total error risk. The guide-gene driven selection is an approach to combining statistical significance and the existing biological knowledge of the study at hand. Error properties of these criteria are investigated theoretically and by simulation. The proposed methods are illustrated and compared using an example of genomic screening for novel Arf gene targets. Operating characteristics of q-value and the proposed significance threshold criteria are investigated and compared in a simulation study that employs a model mimicking a gene regulatory pathway. A guideline for using these criteria is provided. Splus/R code is available from the corresponding author upon request.
Abstract
The methodological advancement in microarray data analysis on the basis of false discovery rate (FDR) control, such as the q-value plots, allows the investigator to examine the FDR from ...several perspectives. However, when FDR control at the ``customary" levels 0.01, 0.05, or 0.1 does not provide fruitful findings, there is little guidance for making the trade off between the significance threshold and the FDR level by sound statistical or biological considerations. Thus, meaningful statistical significance criteria that complement the existing FDR methods for large-scale multiple tests are desirable. Three statistical significance criteria, the profile information criterion, the total error proportion, and the guide-gene driven selection, are developed in this research. The first two are general significance threshold criteria for large-scale multiple tests; the profile information criterion is related to the recent theoretical studies of the connection between FDR control and minimax estimation, and the total error proportion is closely related to the asymptotic properties of FDR control in terms of the total error risk. The guide-gene driven selection is an approach to combining statistical significance and the existing biological knowledge of the study at hand. Error properties of these criteria are investigated theoretically and by simulation. The proposed methods are illustrated and compared using an example of genomic screening for novel Arf gene targets. Operating characteristics of q-value and the proposed significance threshold criteria are investigated and compared in a simulation study that employs a model mimicking a gene regulatory pathway. A guideline for using these criteria is provided. Splus/R code is available from the corresponding author upon request.
Submitted: May 5, 2004 · Accepted: November 29, 2004 · Published: December 19, 2004
Recommended Citation
Cheng, Cheng; Pounds, Stanley B.; Boyett, James M.; Pei, Deqing; Kuo, Mei-Ling; and Roussel, Martine F.
(2004)
"Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data,"
Statistical Applications in Genetics and Molecular Biology:
Vol. 3
:
Iss.
1, Article 36.
DOI: 10.2202/1544-6115.1064
Available at: http://www.bepress.com/sagmb/vol3/iss1/art36
Synthetic aperture radar (SAR) automatic target recognition (ATR) is an essential field in SAR application. However, a sufficient number of labeled training SAR images for each target type plays a ...crucial role in existing SAR ATR methods, while the acquisition and annotation of SAR images are difficult and time-consuming in practice. Therefore, the recognition under the limited labeled training SAR images is the basic and crucial problem in SAR application. In this paper, we propose a novel hierarchically-designed lightweight method (HDLM) by recognition in label and discrimination in feature to address the problem of limited data in SAR ATR. The proposed method is hierarchically designed from top to bottom. In the top phase, the framework is constructed by dual loss to force the deep model to optimize by label recognition and feature discrimination, which is noted as recognition in label and discrimination in feature. In the middle phase, the architecture of the network is built up using a novel lightweight extractor and multi-level cross fusion to boost the amount and diversity of the features for the framework. In the bottom phase, two modules, coordinate attention, and depth-wise separable convolution modules are employed to enhance the feature quality and density with fewer parameters for the phases above. The experimental results on MSTAR and OpenSARship showed that the proposed HDLM performs better than the existing methods under the limited training samples.
Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large ...amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00% when 10 training samples each class.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Marine radar plays a significant role in ship navigation. However, when contending with interference among cosailing navigation radars, the echo data may be unintentionally corrupted, and it becomes ...challenging to obtain high-quality imagery using current radar imaging methods. To overcome this problem, an efficient anti-interference imaging framework is presented in this article based on the theory of nonuniform sampling. First, a beam-recursive anti-interference method based on the signal-to-interference-plus-noise ratio (SINR) estimation is proposed to compensate for the shortcoming of the traditional interference rejection method. Second, a nonuniform sampling model is established to well model the echo data with missing samples, which facilitates reconstructing the marine radar imagery from the missing echo data. Finally, a fast super-resolution method based on the dimension-reduction iterative adaptive approach (DRIAA) is proposed to reconstruct the distribution of sea-surface targets at a much lower computational complexity. Simulated and experimental results demonstrate that our anti-interference imaging framework can provide radar imagery with higher quality and lower computational complexity than the existing radar imaging methods in the presence of unintentional interference.
Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and ...an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension of azimuthal data increases. In this article, to efficiently improve the angular resolution of RAR, a generalized adaptive asymptotic minimum variance (GAAMV) estimator that relies on a normalized projection array (NPA) model is proposed. On the one hand, the traditional convolution model of RAR is transformed into an NPA model to compress the data dimension. The proposed NPA model can normalize the signal model to make it independent of the sampling parameters. On the other hand, based on the NPA model, a GAAMV estimator is proposed to efficiently reconstruct the targets by adaptively updating each grid. Moreover, the penalty parameter is extended as a generalized case to improve its adaptability to different scenes. Based on the proposed model and method, the computational complexity can be decreased, especially for high-dimensional azimuthal data. Simulations and experimental data verify the proposed model and method.
