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Robert R. Delongchamp,Mehdi Razzaghi,이태원 한국유전학회 2011 Genes & Genomics Vol.33 No.5
Large numbers of mRNA transcripts, proteins, metabolites,and single nucleotide polymorphisms can be measured in a single tissue sample using new molecular biological techniques. Accordingly, the interpretation of ensuing hypothesis tests should manage the number of comparisons. For example,cDNA microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. In this context, the false discovery rate (FDR)and false non-discovery rate (FNR) are used to account for multiple comparisons. In this study, we propose non-parametric estimates of FDR and FNR that are conceptually and computationally straightforward. Additionally, to illustrate their properties and use in a procedure for an optimum subset of significant tests, an example from a functional genomics study is presented.
Use of p-value plots to diagnose and remedy problems with statistical analysis of microarray data
이태원,Robert R. Delongchamp,김원국,Robert J. Shmookler Reis 한국유전학회 2016 Genes & Genomics Vol.38 No.1
Microarray and RNA-sequencing technologies measure thousands of genes per biological sample. Because of the large number of genes, empirical distributions over genes for statistics computed over samples resolve properties of the data that can be exploited to define the expressed genes, diagnose problems with hypothesis tests, and even remedy some of these problems. The empirical distribution of the average expressions for genes was first used to partition the interrogated genes into two subsets, ‘non-expressed’ genes and ‘expressed’ genes. The p-values from tests for treatment effects were computed for both subsets and their empirical distributions were examined next. A plot of empirical distributions of p-values (p-value plot) indicated that the ‘non-expressed’ genes do not follow the anticipated null distribution, which implies that the pvalues for expressed genes may also misrepresent their true significance. In simulations we were able to produce a similar departure from the null distribution with dye effects, suggesting that comparable confounding may account for the observed discrepancies. By using the empirical distribution of non-expressed genes as the null distribution, p-values for the expressed genes were adjusted with the goal of mitigating biases introduced by systematic distortions such as a dye effect. A plot of the empirical distribution for a statistic computed per endpoint provides a succinct visualization of the distributional properties, which can be compared to expected properties. Such comparisons are effective at identifying problems with analyses, and indicating adjustments that can be applied to generate more reliable lists of affected genes based on false-discovery criteria.
이태원,Lee, Tae-Won,Delongchamp, Robert R. 한국통계학회 2012 응용통계연구 Vol.25 No.2
In microarray data analysis, recent efforts have focused on the discovery of gene sets from a pathway or functional categories such as Gene Ontology terms(GO terms) rather than on individual gene function for its direct interpretation of genome-wide expression data. We introduce a meta-analysis method that combines $p$-values for changes of each gene in the group. The method measures the significance of overall treatment-induced change in a gene group. An application of the method to a real data demonstrates that it has benefits over other statistical methods such as Fisher's exact test and permutation methods. The method is implemented in a SAS program and it is available on the author's homepage(http://cafe.daum.net/go.analysis). 마이크로어레이 분석은 특이 발현하는 개별적인 유전자보다 유전자 온톨로지(Gene Ontology)와 같이 기능적 분류나 생물학적 경로(pathway)와 관련된 유전자군을 찾아내는 것이 그 해석의 용이성 때문에 최근 더욱 많은 연구가 진행되고 있다. 약물 처리에 의한 생물학적 반응을 연구할 때, 한 유전자군에 속하는 유전자들 각각의 특이 발현 여부의 유의성을 나타내는 $p$-value들을 취합하여 그 유전자군의 유의성을 결정하는 통계 검증 방법을 본 논문에서 소개하였다. 본 논문에 제시된 유전자군 분석(Gene group analysis) 방법은 Fisher's exact test나 permutation test와 같은 기존의 대표적인 방법들보다 더 정확하고 적용범위가 넓음을 실재 생물학 실험 자료의 분석을 통해 보였다. 제시된 유전자군 분석 방법은 SAS 프로그램으로 구현되었고 저자의 홈페이지(http://cafe.daum.net/go.analysis)에서 내려 받아 사용할 수 있다.
Connecting Dopant Bond Type with Electronic Structure in N-Doped Graphene
Schiros, Theanne,Nordlund, Dennis,Pá,lová,, Lucia,Prezzi, Deborah,Zhao, Liuyan,Kim, Keun Soo,Wurstbauer, Ulrich,Gutié,rrez, Christopher,Delongchamp, Dean,Jaye, Cherno,Fischer, Daniel American Chemical Society 2012 Nano letters Vol.12 No.8
<P>Robust methods to tune the unique electronic properties of graphene by chemical modification are in great demand due to the potential of the two dimensional material to impact a range of device applications. Here we show that carbon and nitrogen core-level resonant X-ray spectroscopy is a sensitive probe of chemical bonding and electronic structure of chemical dopants introduced in single-sheet graphene films. In conjunction with density functional theory based calculations, we are able to obtain a detailed picture of bond types and electronic structure in graphene doped with nitrogen at the sub-percent level. We show that different N-bond types, including graphitic, pyridinic, and nitrilic, can exist in a single, dilutely N-doped graphene sheet. We show that these various bond types have profoundly different effects on the carrier concentration, indicating that control over the dopant bond type is a crucial requirement in advancing graphene electronics.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/nalefd/2012/nalefd.2012.12.issue-8/nl301409h/production/images/medium/nl-2012-01409h_0005.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/nl301409h'>ACS Electronic Supporting Info</A></P>