Sample-Path Large Deviations for Generalized Processor Sharing Queues with Gaussian Inputs

Package‘StAMPP’October12,2022Type PackageTitle Statistical Analysis of Mixed Ploidy PopulationsDepends R(>=3.2.0),pegasImports parallel,doParallel,foreach,adegenet,methods,utilsVersion1.6.3Date2021-07-30Author LW PembletonMaintainer LW Pembleton<*********************>Description Allows users to calculate pairwise Nei's Genetic Distances(Nei1972),pairwise Fixation Indexes(Fst)(Weir&Cockerham1984)and also Genomic Relationship matrixes follow-ing Yang et al.(2010)in mixed and singleploidy populations.Bootstrapping across loci is implemented during Fst calculation to gener-ate confidence intervals and p-valuesaround pairwise Fst values.StAMPP utilises SNP geno-type data of any ploidy level(with the ability to handle missing data)and is coded toutilise multithreading where available to allow efficient analy-sis of large datasets.StAMPP is able to handle genotype data from genlight objectsallowing integration with other packages such adegenet.Please refer to LW Pembleton,NOI Cogan&JW Forster,2013,Molecular Ecology Re-sources,13(5),946-952.<doi:10.1111/1755-0998.12129>for the appropriate cita-tion and user manual.Thank you in advance.URL https:///lpembleton/StAMPPLicense GPL-3RoxygenNote7.1.1Encoding UTF-8NeedsCompilation noRepository CRANDate/Publication2021-08-0804:20:05UTC1R topics documented:potato (2)potato.mini (4)stampp2genlight (5)stamppAmova (6)stamppConvert (7)stamppFst (8)stamppGmatrix (9)stamppNeisD (10)stamppPhylip (11)Index12 potato Example genotype input formatDescriptionA data frame containing Solcap potato genotype data in tetraploid and diploid format as an smallexample of the input format required by StAMPPUsagedata(potato)FormatA data frame with30rows and48variables:Sample Sample namesPop Population namePloidy Ploidy levelFormat Format of genotype datasolcap_snp_c1_1genotype datasolcap_snp_c1_1000genotype datasolcap_snp_c1_10000genotype datasolcap_snp_c1_10001genotype datasolcap_snp_c1_10011genotype datasolcap_snp_c1_10012genotype datasolcap_snp_c1_10031genotype datasolcap_snp_c1_10042genotype datasolcap_snp_c1_10050genotype datasolcap_snp_c1_10054genotype datasolcap_snp_c1_10109genotype datasolcap_snp_c1_10130genotype datasolcap_snp_c1_10157genotype datasolcap_snp_c1_10202genotype datasolcap_snp_c1_10252genotype datasolcap_snp_c1_10253genotype datasolcap_snp_c1_10255genotype datasolcap_snp_c1_1029genotype datasolcap_snp_c1_10295genotype datasolcap_snp_c1_10297genotype datasolcap_snp_c1_10351genotype datasolcap_snp_c1_10384genotype datasolcap_snp_c1_10397genotype datasolcap_snp_c1_10457genotype datasolcap_snp_c1_10491genotype datasolcap_snp_c1_10492genotype datasolcap_snp_c1_10494genotype datasolcap_snp_c1_10579genotype datasolcap_snp_c1_10646genotype datasolcap_snp_c1_10669genotype datasolcap_snp_c1_10715genotype datasolcap_snp_c1_10737genotype datasolcap_snp_c1_10743genotype datasolcap_snp_c1_10762genotype datasolcap_snp_c1_10855genotype datasolcap_snp_c1_10873genotype datasolcap_snp_c1_10879genotype datasolcap_snp_c1_10900genotype datasolcap_snp_c1_10932genotype datasolcap_snp_c1_1094genotype datasolcap_snp_c1_11137genotype datasolcap_snp_c1_11144genotype datasolcap_snp_c1_11196genotype datasolcap_snp_c1_11206genotype dataSourceThe example genotype data is a subset of data from the publically avaliable Solcap potato dataset which was re-scored in GenomeStudio in diploid and tetraploid formats4potato.mini potato.mini Smaller example genotype input formatDescriptionA data frame containing Solcap potato genotype data in tetraploid and diploid format as an smallexample of the input format required by StAMPPUsagedata(potato.mini)FormatA data frame with6rows and48variables:Sample Sample namesPop Population namePloidy Ploidy levelFormat Format of genotype datasolcap_snp_c1_1genotype datasolcap_snp_c1_1000genotype datasolcap_snp_c1_10000genotype datasolcap_snp_c1_10001genotype datasolcap_snp_c1_10011genotype datasolcap_snp_c1_10012genotype datasolcap_snp_c1_10031genotype datasolcap_snp_c1_10042genotype datasolcap_snp_c1_10050genotype datasolcap_snp_c1_10054genotype datasolcap_snp_c1_10109genotype datasolcap_snp_c1_10130genotype datasolcap_snp_c1_10157genotype datasolcap_snp_c1_10202genotype datasolcap_snp_c1_10252genotype datasolcap_snp_c1_10253genotype datasolcap_snp_c1_10255genotype datasolcap_snp_c1_1029genotype datasolcap_snp_c1_10295genotype datasolcap_snp_c1_10297genotype datastampp2genlight5solcap_snp_c1_10351genotype datasolcap_snp_c1_10384genotype datasolcap_snp_c1_10397genotype datasolcap_snp_c1_10457genotype datasolcap_snp_c1_10491genotype datasolcap_snp_c1_10492genotype datasolcap_snp_c1_10494genotype datasolcap_snp_c1_10579genotype datasolcap_snp_c1_10646genotype datasolcap_snp_c1_10669genotype datasolcap_snp_c1_10715genotype datasolcap_snp_c1_10737genotype datasolcap_snp_c1_10743genotype datasolcap_snp_c1_10762genotype datasolcap_snp_c1_10855genotype datasolcap_snp_c1_10873genotype datasolcap_snp_c1_10879genotype datasolcap_snp_c1_10900genotype datasolcap_snp_c1_10932genotype datasolcap_snp_c1_1094genotype datasolcap_snp_c1_11137genotype datasolcap_snp_c1_11144genotype datasolcap_snp_c1_11196genotype datasolcap_snp_c1_11206genotype dataSourceThe example genotype data is a subset of data from the publically avaliable Solcap potato dataset which was re-scored in GenomeStudio in diploid and tetraploid formatsstampp2genlight Convert StAMPP genotype data to genlight objectDescriptionConverts a StAMPP formated allele frequency data frame generated from the stamppConvert func-tion to a genlight object for use in other packagesUsagestampp2genlight(geno,pop=TRUE)6stamppAmovaArgumentsgeno a data frame containing allele frequency data generated from stamppConvert pop logical.True if population IDs are present in the StAMPP genotype data,False if population IDs are absent.DetailsStAMPP only exports to genlight objects as they are able to handle mixed ploidy datasets unlike genpop and genloci objects.The genlight object allows the intergration between StAMPP and other common R packages such as ADEGENETValueA object of class genlight which contains genotype data,individual IDs,population IDs(if present)and ploidy levelsAuthor(s)Luke Pembleton<luke.pembleton at .au>Examples#import genotype data and convert to allele frequeciesdata(potato.mini,package="StAMPP")potato.freq<-stamppConvert(potato.mini,"r")#Convert the StAMPP formatted allele frequency data frame to a genlight objectpotato.genlight<-stampp2genlight(potato.freq,TRUE)stamppAmova Analysis of Molecular VarianceDescriptionCalculates an AMOV A based on the genetic distance matrix from stamppNeisD()using the amova() function from the package PEGAS for exploring within and between population variationUsagestamppAmova(dist.mat,geno,perm=100)Argumentsdist.mat the matrix of genetic distances between individuals generated from stampp-NeisD()geno a data frame containing allele frequency data generated from stamppConvert,ora genlight object containing genotype data,individual IDs,population IDs andploidy levelsperm the number of permutations for the tests of hypothesesstamppConvert7DetailsUses the formula distance~populations,to calculate an AMOV A for population differentiation and within&between population variation.This function uses the amova function from the PEGAS package.ValueAn object of class"amova"which is a list containing a table of sum of square deviations(SSD), mean square deviations(MSD)and the number of degrees of freedom as well as the variance com-ponentsAuthor(s)Luke Pembleton<luke.pembleton at .au>ReferencesParadis E(2010)pegas:an R package for population genetics with an integrated-modular approach.Bioinformatics26,419-420.<doi:10.1093/bioinformatics/btp696>Examples#import genotype data and convert to allele frequeciesdata(potato.mini,package="StAMPP")potato.freq<-stamppConvert(potato.mini,"r")#Calculate genetic distance between individualspotato.D.ind<-stamppNeisD(potato.freq,FALSE)#Calculate AMOVAstamppAmova(potato.D.ind,potato.freq,100)stamppConvert Import and ConvertDescriptionImports biallelic AB formated or allele A frequency genotype data.If the data is in imported in biallelic AB format this function also converts it to allele frequenciesUsagestamppConvert(genotype.file,type="csv")Argumentsgenotype.file the genotype inputfile.This should be a R matrix object or afile path for a csvfile containing the genotype data in either bialleleic AB format or allele’A’frequency format,or a genlight object containing genotype data type the type offile the genotype data is being imported from;"csv"=comma seper-atedfile,"r"=data frame in the R workspace,"genlight"=genlight object.8stamppFst ValueAn object of class data.frame which contains allele frequency data for use in other StAMPP func-tionsAuthor(s)Luke Pembleton<luke.pembleton at .au>Examples#Import example data into the R workspacedata(potato.mini,package="StAMPP")#Convert to allele frequenciespotato.freq<-stamppConvert(potato.mini,"r")stamppFst Fst ComputationDescriptionThis function calculates pairwise Fst values along with confidence intervals and p-values between populations according to the method proposed by Wright(1949)and updated by Weir and Cocker-ham(1984)UsagestamppFst(geno,nboots=100,percent=95,nclusters=1)Argumentsgeno a data frame containing allele frequency data generated from stamppConvert,ora genlight object containing genotype data,individual IDs,population IDs andploidy levelsnboots number of bootstraps to perform across loci to generate confidence intervals and p-valuespercent the percentile to calculate the confidence interval aroundnclusters number of proccesor treads or cores to use during calculations.DetailsIf possible,using multiple processing threads or cores is recommended to assist in calculating Fst values over a large number of bootstraps.stamppGmatrix9 ValueAn object list with the components:Fsts a matrix of pairwise Fst values between populations Pvalues a matrix of p-values for each of the pairwise Fst values containined in the’Fsts’matrix Bootstraps a dataframe of each Fst value generated during Bootstrapping and the associated confi-dence intervals If nboots<2,no bootstrapping is performed and therefore only a matrix of Fst values is returned.Author(s)Luke Pembleton<luke.pembleton at .au>ReferencesWright S(1949)The Genetical Structure of Populations.Annals of Human Genetics15,323-354.<doi:10.1111/j.1469-1809.1949.tb02451.x>Weir BS,Cockerham CC(1984)Estimating F Statis-tics for the ANalysis of Population Structure.Evolution38,1358-1370.<doi:10.2307/2408641> Examples#import genotype data and convert to allele frequeciesdata(potato.mini,package="StAMPP")potato.freq<-stamppConvert(potato.mini,"r")#Calculate pairwise Fst values between each populationpotato.fst<-stamppFst(potato.freq,100,95,1)stamppGmatrix Genomic Relationship CalculationDescriptionThis function calculates a genomic relationship matrix following the method decribed by Yang et al (2010)UsagestamppGmatrix(geno)Argumentsgeno a data frame containing allele frequency data generated from stamppConvert,ora genlight object containing genotype data,individual IDs,population IDs andploidy levelsValueAn object of class matrix which contains the genomic relationship values between each individual10stamppNeisDAuthor(s)Luke Pembleton<luke.pembleton at .au>ReferencesYang J,Benyamin B,McEvoy BP,et al(2010)Common SNPs explain a large proportion of the heritability for human height.Nat Genet42,565-569.<doi:10.1038/ng.608>Examples#import genotype data and convert to allele frequeciesdata(potato.mini,package="StAMPP")potato.freq<-stamppConvert(potato.mini,"r")#Calculate genomic relationship values between each individualpotato.fst<-stamppGmatrix(potato.freq)stamppNeisD Genetic Distance CalculationDescriptionThis function calculates Nei’s genetic distance(Nei1972)between populations or individualsUsagestamppNeisD(geno,pop=TRUE)Argumentsgeno a data frame containing allele frequency data generated from stamppConvert,ora genlight object containing genotype data,individual IDs,population IDs andploidy levelspop logical.True if genetic distance should be calculated between populations,false if it should be calculated between individualValueA object of class matrix which contains the genetic distance between each population or individualAuthor(s)Luke Pembleton<luke.pembleton at .au>ReferencesNei M(1972)Genetic Distance between Populations.The American Naturalist106,283-292.stamppPhylip11Examples#import genotype data and convert to allele frequeciesdata(potato.mini,package="StAMPP")potato.freq<-stamppConvert(potato.mini,"r")#Calculate genetic distance between individualspotato.D.ind<-stamppNeisD(potato.freq,FALSE)#Calculate genetic distance between populationspotato.D.pop<-stamppNeisD(potato.freq,TRUE)stamppPhylip Export to Phylip FormatDescriptionConverts the genetic distance matrix generated with stamppNeisD into Phylip format and exports it as a textfileUsagestamppPhylip(distance.mat,file="")Argumentsdistance.mat the matrix containing the genetic distances generated from stamppNeisD to be converted into Phylip formatfile thefile path and name to save the Phylip format matrix asDetailsThe exported Phylip formated textfile can be easily imported into sofware packages such as DAR-Win(Perrier&Jacquemound-Collet2006)to be used to generate neighbour joining treesAuthor(s)Luke Pembleton<luke.pembleton at .au>ReferencesPerrier X,Jacquemound-Collet JP(2006)DARWin-Dissimilarity Analysis and Representation for Windows.Agricultural Research for DevelopmentExamples#import genotype data and convert to allele frequeciesdata(potato.mini,package="StAMPP")potato.freq<-stamppConvert(potato.mini,"r")#Calculate genetic distance between populationspotato.D.pop<-stamppNeisD(potato.freq,TRUE)#Export the genetic distance matrix in Phylip format##Not run:stamppPhylip(potato.D.pop,file="potato_distance.txt")Index∗datasetspotato,2potato.mini,4potato,2potato.mini,4stampp2genlight,5stamppAmova,6stamppConvert,7stamppFst,8stamppGmatrix,9stamppNeisD,10stamppPhylip,1112。

International Journal ofMolecular SciencesReviewBioinformatics Approaches for Fetal DNA Fraction Estimation in Noninvasive Prenatal TestingXianlu Laura Peng1,2and Peiyong Jiang1,2,*1Li Ka Shing Institute of Health Sciences,The Chinese University of Hong Kong,Hong Kong,China;laurapeng@.hk2Department of Chemical Pathology,The Chinese University of Hong Kong,Prince of Wales Hospital, Hong Kong,China*Correspondence:jiangpeiyong@.hk;Tel.:+852-3763-6056Academic Editor:William Chi-shing ChoReceived:18January2017;Accepted:11February2017;Published:20February2017Abstract:The discovery of cell-free fetal DNA molecules in plasma of pregnant women has created a paradigm shift in noninvasive prenatal testing(NIPT).Circulating cell-free DNA in maternal plasma has been increasingly recognized as an important proxy to detect fetal abnormalities in a noninvasive manner.A variety of approaches for NIPT using next-generation sequencing have been developed,which have been rapidly transforming clinical practices nowadays.In such approaches, the fetal DNA fraction is a pivotal parameter governing the overall performance and guaranteeing the proper clinical interpretation of testing results.In this review,we describe the current bioinformatics approaches developed for estimating the fetal DNA fraction and discuss their pros and cons. Keywords:noninvasive prenatal testing;circulating cell-free DNA;fetal DNA fraction1.IntroductionThe discovery of circulating cell-free fetal DNA in maternal plasma[1]has created a paradigm shift in noninvasive prenatal testing(NIPT),which has rapidly made its way into clinical practices worldwide,for example,cell-free DNA-based chromosomal aneuploidy detection[2–8]and diagnosis of monogenic diseases[9–15].The circulating cell-free DNA(cfDNA)in a pregnant woman is a mixture of predominant maternal DNA derived from the hematopoietic system of the mother[16,17]and fetal DNA released through the apoptosis of cytotrophoblast cells during fetal development[18,19]. The proportion of fetal DNA molecules among the total cfDNA molecules in maternal circulation is expressed as fetal DNA fraction,which is a paramount factor for determining the overall performance of NIPT[15,20–22]and interpreting clinical assessments[7,23–25].In noninvasive fetal aneuploidy detection,the fetal DNA fraction in maternal plasma is linearly correlated with the extent of chromosomal abnormalities present in plasma of pregnant women[3,6,7]. The fetal DNA concentration below4%in a maternal plasma sample would suggest a potential issue present in the quality control(QC)step,because the limited amount of fetal DNA molecules to be detected and analyzed may give rise to a false negative result[20,26–28].Therefore,it is important to estimate the fetal DNA fraction accurately,making sure that it has passed the QC threshold to guarantee a sufficient amount of fetal DNA present in a testing sample and make it possible to arrive at a proper interpretation of the sequencing result.In addition,the fetal DNA fraction has been incorporated into bioinformatics diagnostic algorithms by a number of laboratories[7,23,24].Monogenic diseases comprise a larger proportion of genetic diseases than chromosomal aneuploidies[15].However,the cfDNA-based NIPT for single-gene diseases is much more challenging, because the cfDNA in maternal plasma is generally of minor population,hampering the reliable deduction of the maternal inherence of fetus at single-nucleotide resolution.Technologically,the Int.J.Mol.Sci.2017,18,453;doi:10.3390//journal/ijmsdevelopment of relative haplotype dosage analysis(RHDO),which utilizes information regarding parental haplotypesflanking the variants of interest,has been demonstrated to greatly improve the accuracy of single-gene disorder detection[9,10,13].More recently,researchers have illustrated that the use of linked-read sequencing technology allows for directly ascertaining parental haplotypes surrounding the genes of interest,making RHDO analysis a universal NIPT method for single-gene diseases[29].This work has made an important step forward towards the real clinical utility regarding cfDNA-based single-gene disease testing.Such RHDO analysis took advantage of the fetal DNA fraction as a key parameter to determine the statistical thresholds,indicating if a particular maternal haplotype presumably inherited by the fetus exhibits a statistically significant over-presentation in maternal plasma of a pregnant woman[9,23].In this review,we discuss a number of existing approaches for the determination of fetal DNA fraction,as well as their advantages and disadvantages(Table1).The simplified principles for these approaches are diagrammatically depicted in Figure1.Figure1.Schematic illustration of current approaches for the determination of fetal DNA fraction in maternal circulating cell-free DNA(cfDNA).(a)Y chromosomal(chr)sequence-based fetal DNA fraction estimate[3,22];(b)Single-nucleotide polymorphism(SNP)-based approach.A direct way to estimate the fetal DNA fraction is to use the SNP loci,where both mother and father are homozygous but with different alleles.The resulting fetal genotype is obligately heterozygous.In maternal plasma,the fetal DNA fraction can be directly deduced by calculating the proportion of fetal specific alleles[9,30].Based on this concept,two extended versions of SNP-based methods for fetal DNA fraction estimate have been developed,namely FetalQuant and FetalQuant SD,which can be used without the need of both paternal and maternal genotype information[31,32];(c)cfDNA count-based approach.Read densities across the genome-wide50KB windows arefitted into a neural network model to predict the fetal DNA fraction[33];(d)Differential methylation-based approaches[17,26,34,35];(e)cfDNA size-based approach.The proportion of short cfDNA molecules is correlated with fetal DNA fraction[36];(f)Nucleosome track-based approach.Cell-free DNA distribution at the nucleosomal core and linkerregions is correlated with fetal DNA fraction[37].Table1.The summary of current approaches for estimating fetal DNA fraction. Approaches Advantages LimitationsY Chromosome[3,22]Simple and accurate NOT applicable for pregnancies with female fetusesMaternal plasma DNA sequencingdata with parental genotypes[9,30]Direct and accuratePaternal DNA may notbe availableTargeted sequencing of maternal plasma DNA(FetalQuant)[31]Sequencing maternal plasma DNAonly;accurateHigh sequencing depth is requiredShallow-depth sequencing ofmaternal plasma DNA coupled with maternal genotypes(FetalQuant SD)[32]Shallow-depth sequencing ofmaternal plasma DNA;accurateMaternal genotype requirementwill add additional costs;therecalibration curve is required tobe rebuilt for different sequencingand genotyping platformsShallow-depth maternal plasma DNA sequencing data(SeqFF)[33]Only shallow-depth sequencing ofmaternal plasma DNA;single-endsequencing;easy to be integratedinto the routine noninvasiveprenatal testing(NIPT)Large-scale samples are needed totrain the neutral network;need toimprove the accuracy when thefetal DNA fraction is below5%Differantial methylation [17,26,34,35]AccurateEither bisulfite conversion ordigestion withmethylation-sensitive restrictionenzymes may affect the accuracy;genome-wide bisulfite sequencingis too expensive and prohibitivefor the routine NIPTcfDNA fragment size[36]Only shallow-depth sequencing ofmaternal plasma DNA;easy to beintegrated into the routine NIPTModerate accuracy;paired-endsequencing would increasethe costsNucleosome track[37]Only shallow-depth sequencing ofmaternal plasma DNALower accuracy;high-depthsequencing data is requiredduring the training step2.Current Approaches Developed to Estimate Fetal DNA Fraction2.1.Y Chromosome-Based ApproachIn the early works,genetic markers located on Y chromosome which are paternally inherited, such as gene SRY,DYS14and ZFY,were used to indicate the fraction of fetal DNA molecules based on PCR assays[23,38,39].For instance,the ratio of the concentration of the sequences from Y chromosome to that of an autosome was used for the determination of fetal DNA fraction.In the context of NIPT using massively parallel sequencing,the proportion of all sequence reads from Y chromosome can be translated to the fetal DNA fraction[3,22].Although these methods are simple and accurate,they are only applicable to pregnancies carrying male fetuses.2.2.Maternal Plasma DNA Sequencing Data with Parental Genotype-Based ApproachWith the use of parental genotypes,fetal-specific alleles in maternal plasma can be readily identified from the sequence reads.Briefly,the fetal genotypes are obligately heterozygous at single-nucleotide polymorphism(SNP)loci,where both father and mother are homozygous but with different genotypes(e.g.,A/A for paternal genotype and C/C for maternal genotype).Then the fetal DNA fraction can be quantified by calculating the ratio of fetal-specific alleles(A)to the total alleles in plasma DNA[7,9,30,40].Even though this method is a direct and accurate way to assess the fetal DNA fraction and generally considered as a gold standard[9],the feasibility of this approachis sometimes hindered by the requirement of parental genotypes,because(1)only maternal blood samples would be collected and maternal plasma DNA are subject to sequence for NIPT in most clinical settings;and(2)it is not uncommon that the genotype of the biological father may not be available in practice[41].2.3.High-Depth Sequencing Data of Maternal Plasma DNA-Based ApproachTo obviate the requirement of parental genotype information,an approach called FetalQuant was developed to measure the fetal DNA fraction through the analysis of maternal plasma DNA sequencing data at high depth using targeted massively parallel sequencing[31].In this method, a binomial mixture model was employed tofit the observed allelic counts with the use of the underlying four types of maternal-fetal genotype combinations(AA AA,AA AB,AB AA,AB AB,where the main text and subscript represent the maternal and fetal genotypes,respectively).In this model,the fetal fraction was determined through the maximum likelihood estimation.The predicted result of this method is very close to the one deduced by the parental genotypes-based approach(the correlation coefficient is not available).However,the limitation of this approach would be that the sequencing depth is required to be as high as~120×by targeted sequencing to robustly determine the fetal alleles[31]. 2.4.Shallow-Depth Maternal Plasma DNA Sequencing Data with Maternal Genotype-Based ApproachAs an extended version of FetalQuant,FetalQuant SD[32]was recently developed based on shallow-depth sequencing data coupled with only maternal genotype information.The rationale of this approach is to take advantage of the fact that any alternative allele(non-maternal alleles)present at an SNP locus where the mother is homozygous would theoretically suggest a fetal-specific DNA allele. Briefly,the homozygous sites in a pregnant woman were identified by genotyping her blood cells using microarray technologies.Then,plasma DNA molecules with alleles different from the maternal homozygous sites(i.e.,non-maternal alleles)were identified,which were specifically derived from the father in theory.Thus,the fractions of such non-maternal alleles were hypothesized to correlate with fetal DNA fractions under the assumption that the error rates stemmed from sequencing and genotyping platforms are relatively constant across different cases.Therefore,a linear regression model wasfirst trained between the fraction of non-maternal alleles and actual fetal DNA fraction estimated by parental genotypes-based approach,and then the fetal DNA fractions were predicted with the use of the trained model in an independent validation dataset,exhibiting a very high accuracy(r=0.9950, p<0.0001,Pearson correlation)even using1million sequencing reads.However,the parameters in this model might be varied according to sequencing and genotyping platforms,because various platforms are characterized with different error properties,which may contribute to the measured non-maternal alleles.On the other hand,the extent of heterozygosity might be different in different ethnic groups,which could confound the accuracy of fetal DNA fraction prediction.The advantage of this model is that once thefinal well-trained model is achieved,it could be readily applied to any datasets,as long as they are generated from the same platform and population.2.5.Shallow-Depth Maternal Plasma DNA Sequencing Data-Based ApproachRecently,a new approach,named SeqFF,has been developed,attempting to make it possible to directly estimate fetal DNA fraction from the routine data of NIPT without any additional effort. In this approach,using single-end random sequencing of the maternal plasma,read count within each50KB autosomal region was analyzed tofit a high-dimensional regression model[33].The normalized read counts in50KB bins originating from chromosomes except chromosomes13,18,21, X,and Y were used as predictor variables,and the model coefficients were determined by making use of elastic net(Enet)and reduced-rank regression model[33].SeqFF showed a good correlation with Y chromosome-based method in two independent cohorts(r=0.932and0.938,respectively,Pearson correlation)[33].However,such high-dimensional model would require large-scale samples during training,and the performance appeared to be greatly deteriorated when the fetal DNA fraction isbelow5%,possibly because the number of cases with fetal DNA fraction<5%was not sufficient to train the Enet model.2.6.Fetal Methylation Marker-Based ApproachDNA methylation is a process by which a methyl group is added to cytosine nucleotides[42,43]. In mammalian somatic cells,the DNA methylation of cytosine in CpG dinucleotides is frequently methylated(~70%of the CpGs)[44].Different organs have been suggested to show variable methylation profiles,which would allow us to identify the tissue of origin analyzing the regions with differential methylation states[17,45].Indeed,researchers used the placenta-specific methylation markers to estimate the fetal DNA concentration[26,34].For example,a methylation-sensitive restriction enzyme has been used to digest hypomethylated maternal-derived RASSF1A promoter sequences,while it left the methylated counterparts of the fetal-derived sequences unaffected, thus allowing the discrimination of the methylated fetal DNA molecules from the unmethylated maternal background for the calculation of fetal DNA fraction[34].Similarly,based onfive differentially methylated regions comparing placental tissue and maternal buffy coat mined by using methyl-cytosine immunoprecipitation and CpG island microarrays,Nygren et al.developed a fetal quantitative assay(FQA)permitting the calculation of fetal DNA fraction in a plasma sample[26]. In FQA,by measuring the copy number of total DNA(maternal and fetal)and fetal methylated DNA after methylation-sensitive restriction enzyme digestion,the assay achieved good agreement with Y chromosome-based quantification(r=0.85,p<0.001,Pearson correlation).However,the analytical process used for quantifying these epigenetic markers involves digestion with methylation-sensitive restriction enzymes,and thus its stability needs to be further verified in large-scale datasets generated from different research centers.Furthermore,massively parallel bisulfite sequencing provides an alternative way to estimate the fetal DNA fraction according to the ratio of fetal-derived DNA molecules within differentially methylated regions[35].Using such bisulfite sequencing,the placenta has been demonstrated to exhibit a different methylation profile compared with other tissues[17,35].Therefore,a general approach, referred to as plasma DNA tissue mapping,for disentangling tissue contributors to cell-free DNA has been developed by leveraging the principle that different tissues within the body show different DNA methylation ing whole-genome bisulfite sequencing,the methylation profile of cell-free DNA across over5800DNA methylation markers was used to correlate the tissue-related methylation profiles,for the inference of the proportional contributions from different tissues in plasma[17].Using this new approach,placenta contribution was verified by genotype-based approaches.However,this genome-wide bisulfite sequencing-based tissue mapping algorithm in the present version would be too expensive for routine NIPT.2.7.Cell-Free DNA Size-Based ApproachFetal-derived and maternal-derived DNA molecules in a plasma sample have been observed to exhibit different fragmentation patterns,namely,fetal DNA being generally shorter than maternal DNA[9,46].Therefore,a higher fetal DNA fraction should be theoretically associated with an increased percentage of short DNA ing paired-end sequencing,Yu et al.developed a new method to estimate fetal DNA concentration based on the ratio between the count of fragments ranging from 100to150bp and from163to169bp[36].These size cutoffs gave their optimal performance among multiple size combinations.In the training dataset consisting of36samples,a linear regression model was established between the size ratio and fetal DNA concentration determined by the proportion of chromosome Y sequences(r=0.827,p<0.0001).Then using the derived model,the size ratio was translated to the fetal DNA fraction for each sample in the validation dataset.Intriguingly,the authors also proposed to calculate the size ratio using capillary electrophoresis of sequencing libraries directly, which is readily available before sequencing without additional costs.2.8.Cell-Free DNA Nucleosome Track-Based ApproachRecently,the investigation of nucleosomal origin of plasma DNA has been increasingly recognized as an appealing direction,which has been discussed in a number of studies[9,36,37,47].One important clue directing to such origin has been unravelled in two studies with the use of the high-resolution size profiling of maternal plasma DNA[9,36].It has been reported that the size distribution of the total maternal plasma DNA is characterized by a166bp major peak with a series of small peaks occurring at10bp periodicities,suggesting that a predominant population of plasma DNA molecules have a size of166bp.In contrast,fetal DNA molecules were found to have a dominant population with 143bp in size.It has been speculated that the166bp molecules would represent cfDNA containing the nucleosome core plus the linker[9].However,the143bp molecules would suggest molecules subject to the trimming of linker DNA[9].On the basis of this hypothetical model,Straver et al.pooled maternal plasma DNA from298cases to generate a hypothetical“nucleosome track”[37].Interestingly, the frequency of reads starting within73bp upstream and downstream regions of the inferential center of nucleosome was found to be positively correlated with the fetal DNA fraction,however,giving a relatively lower correlation coefficient than other methods(r=0.636,p=1.61×10−18,Pearson correlation).Thus,further development of a“nucleosome track”-based approach is needed for the clinical requirement.3.ConclusionsThe past decade has witnessed a tremendous advance in the technologies and bioinformatics algorithms for the analysis of circulating cfDNA.With the availability of massively parallel sequencing, noninvasive prenatal testing has become increasingly popular and presented itself as an exemplar in translational medicine research.In NIPT,a rapid,simple,accurate and cost-effective way to estimate fetal DNA fraction is highly desired,typically for the endeavors to make NIPT for single-gene diseases clinically practical.In particular,the accuracy of the estimation of low fetal DNA fraction is essential for determining the QC states and interpreting the clinical outcomes.On the other hand,the fetal DNA fraction could be related to pregnancy outcome;for example,the low fetal DNA fraction may be associated with small or dysfunctional placentas[48],suggesting its potential diagnostic value.Therefore,a large-scale validation for the accuracy of low-fetal DNA fraction estimation would still be needed for some aforementioned approaches,for example,size-,count-and nucleosome profile-based methodologies.We may expect that further in-depth analyses for such properties regarding size and nucleosome profiles would shed new insights into the mechanisms of cell-free DNA generation.As reported in the latest ultra-deep plasma DNA study[49],it was revealed that a number of preferred DNA ends in maternal plasma carry information directing to their tissue of origin(fetal-or maternal-derived DNA).The ratio of the number of fetal-preferred ends to maternal-preferred ends is positively correlated with the fetal DNA fraction in maternal plasma[49].This novel direction of cfDNA exploration regarding fragment ends has opened up new possibilities to study the complexity associated with non-randomness of plasma DNA ends,providing a new way to investigate the highly orchestrated cfDNA fragmentation patterns.More studies are needed to elucidate the relationship between the various factors as well as their interactions,for example,methylation[17],nucleosome footprints[47],and the underlying mechanisms governing the end-cutting patterns of plasma DNA. 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中国质量协会2015年注册黑带考试题一、单项选择题（84道题，84分）1．试验设计是质量改进的有效工具，最早基于农业试验提出方差分析与试验设计理论的是：A．休哈特（W.A.Shewhart）B．道奇和罗米格（H.F.Dodge and H.G.Romig）C．费希尔（R.A.Fisher）D．戴明（R.E.Deming）2．在六西格玛推进过程中，黑带的主要角色是：A．带领团队使用六西格玛方法完成项目B．合理分配资源C．确定公司发展战略D．核算六西格玛项目收益3．在对老产品改型的六西格玛设计（DMADV）项目中，测量阶段的主要工作是：A．测量产品新设计的关键质量特性（CTQ）B．基于关键质量特性（CTQ）确定测量对象C．识别顾客需求D．确定新过程能力4．SWOT分析是战略策划的基础性分析工具。

在使用SWOT分析为组织制定六西格玛推进战略时，以下哪项不是．．主要内容？A．分析组织能够成功推进六西格玛的有利条件B．分析组织推进六西格玛的必要性C．确定组织推进六西格玛的具体负责部门D．分析六西格玛推进方法的比较优势5．水平对比又称为标杆管理（benchmarking）。

以下关于水平对比的说法中，错误．．的是：A．水平对比可用于发现改进机会B．水平对比可以用于确定六西格玛项目的目标C．不同类型的企业也可以进行水平对比D．标杆企业或产品的选择应该随机6．在评价六西格玛项目的收益时，若收益率为10%，净现值为零，说明该项目：A．投资回报率低于10% B．项目收益为零，经济上不可行C．每年的净现金流量为零D．投资回报率等于10%7．以下关于六西格玛项目目标的描述，符合SMART原则是：A．公司产品的毛利率要在未来实现翻一番B．公司产品的市场占有率要达到行业第一C．公司某产品的终检不良率要在5个月内从1%降低到0.3%D．公司要通过技术创新，在未来三年使产品的市场占有率有突破性提高8．某六西格玛项目的目标是缩短生产周期，该项目涉及生产、检测、工艺等部门。

R语言两层2^k析因试验设计（因子设计）分析工厂产量数据和Lenth方法检验显著性可视化数据分享原文链接：/?p=25921假设调查人员有兴趣检查减肥干预方法的三个组成部分。

这三个组成部分是：•记录食物日记（是/否）•增加活动（是/否）•家访（是/否）调查员计划调查所有，实验条件的组合。

实验条件为•要执行因子设计，您需要为多个因子（变量）中的每一个选择固定数量的水平，然后以所有可能的组合运行实验。

•这些因素可以是定量的或定性的。

•定量变量的两个水平可以是两个不同的温度或两个不同的浓度。

•定性因素可能是两种类型的催化剂或某些实体的存在和不存在。

符号：- 因子数 (3) - 每个因子的水平数 (2) - 设计中有多少实验条件 ()因子实验可以涉及具有不同水平数量的因子。

测试：考虑一个设计。

•有多少因子？•每个因子有多少个水平？•多少实验条件？答案：(a) 有 2+2+1 = 5 个因数。

(b) 两个因素有4个水平，2个因素有3个水平，1个因素有2个水平。

(c) 有 288 个实验条件。

向下滑动查看答案▼方差分析和因子设计之间的区别在 ANOVA 中，目标是比较各个实验条件。

让我们考虑一下上面的食物日记研究。

我们可以通过比较食物日记设置为 NO（条件 1-4）的所有条件的平均值和食物日记设置为YES（条件5-8）的所有条件的平均值来估计食物日记的效果。

这也被称为食物日记的主效应，形容词主要是提醒这个平均值超过了其他因素的水平。

食物日记的主效应是：体育锻炼的主效应是：家访的主效应是：使用了所有实验对象，但重新排列以进行每次比较。

受试者被回收以测量不同的效应。

这是析因实验更有效的原因之一。

执行因子设计要执行因子设计：•为每个因子选择固定数量的水平。

•以所有可能的组合运行实验。

我们将讨论每个因子只有两个水平的设计。

因素可以是定量的或定性的。

两个水平的定量变量可以是两个不同的温度或浓度。

定量变量的两个级别可以是两种不同类型的催化剂或某些实体的存在/不存在。

SPSS术语中英文对照【常用软件】SPSS术语中英文对照Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA （analysis of variance）, 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interac tion Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层（复数）Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。

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《孟德尔随机化研究指南》中英文版全文共3篇示例，供读者参考篇1Randomized research is a vital component of scientific studies, allowing researchers to investigate causal relationships between variables and make accurate inferences about the effects of interventions. One of the most renowned guides for conducting randomized research is the "Mendel Randomization Research Guide," which provides detailed instructions and best practices for designing and implementing randomized controlled trials.The Mendel Randomization Research Guide offers comprehensive guidance on all aspects of randomized research, from study design and sample selection to data analysis and interpretation of results. It emphasizes the importance of randomization in reducing bias and confounding effects, thus ensuring the validity and reliability of study findings. With clear and practical recommendations, researchers can feel confident in the quality and rigor of their randomized research studies.The guide highlights the key principles of randomization, such as the use of random assignment to treatment groups, blinding of participants and researchers, and intent-to-treat analysis. It also discusses strategies for achieving balance in sample characteristics and minimizing the risk of selection bias. By following these principles and guidelines, researchers can maximize the internal validity of their studies and draw accurate conclusions about the causal effects of interventions.In addition to the technical aspects of randomized research, the Mendel Randomization Research Guide also addresses ethical considerations and practical challenges that researchers may face. It emphasizes the importance of obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring the safety and well-being of study subjects. The guide also discusses strategies for overcoming common obstacles in randomized research, such as recruitment and retention issues, data collection problems, and statistical challenges.Overall, the Mendel Randomization Research Guide is a valuable resource for researchers looking to improve the quality and validity of their randomized research studies. By following its recommendations and best practices, researchers can conductstudies that produce reliable and actionable findings, advancing scientific knowledge and contributing to evidence-based decision making in various fields.篇2Mendel Randomization Study GuideIntroductionMendel Randomization Study Guide is a comprehensive and informative resource for researchers and students interested in the field of Mendel randomization. This guide provides anin-depth overview of the principles and methods of Mendel randomization, as well as practical advice on how to design and conduct Mendel randomization studies.The guide is divided into several sections, each covering a different aspect of Mendel randomization. The first section provides a brief introduction to the history and background of Mendel randomization, tracing its origins to the work of Gregor Mendel, the father of modern genetics. It also discusses the theoretical foundations of Mendel randomization and its potential applications in causal inference.The second section of the guide focuses on the methods and techniques used in Mendel randomization studies. This includesa detailed explanation of how Mendel randomization works, as well as guidelines on how to select instrumental variables and control for potential confounders. It also discusses the strengths and limitations of Mendel randomization, and provides practical tips on how to deal with common challenges in Mendel randomization studies.The third section of the guide is dedicated to practical considerations in Mendel randomization studies. This includes advice on how to design a Mendel randomization study, collect and analyze data, and interpret the results. It also provides recommendations on how to report Mendel randomization studies and publish research findings in scientific journals.In addition, the guide includes a glossary of key terms and concepts related to Mendel randomization, as well as a list of recommended readings for further study. It also includes case studies and examples of Mendel randomization studies in practice, to illustrate the principles and techniques discussed in the guide.ConclusionIn conclusion, the Mendel Randomization Study Guide is a valuable resource for researchers and students interested in Mendel randomization. It provides a comprehensive overview ofthe principles and methods of Mendel randomization, as well as practical advice on how to design and conduct Mendel randomization studies. Whether you are new to Mendel randomization or looking to deepen your understanding of the field, this guide is an essential reference for anyone interested in causal inference and genetic epidemiology.篇3"Guide to Mendelian Randomization Studies" English VersionIntroductionMendelian randomization (MR) is a method that uses genetic variants to investigate the causal relationship between an exposure and an outcome. It is a powerful tool that can help researchers to better understand the underlying mechanisms of complex traits and diseases. The "Guide to Mendelian Randomization Studies" provides a comprehensive overview of MR studies and offers practical guidance on how to design and carry out these studies effectively.Chapter 1: Introduction to Mendelian RandomizationThis chapter provides an overview of the principles of Mendelian randomization, including the assumptions andlimitations of the method. It explains how genetic variants can be used as instrumental variables to estimate the causal effect of an exposure on an outcome, and outlines the key steps involved in conducting an MR study.Chapter 2: Choosing Genetic InstrumentsIn this chapter, the guide discusses the criteria for selecting appropriate genetic instruments for Mendelian randomization. It covers issues such as the relevance of the genetic variant to the exposure of interest, the strength of the instrument, and the potential for pleiotropy. The chapter also provides practical tips on how to search for suitable genetic variants in public databases.Chapter 3: Data Sources and ValidationThis chapter highlights the importance of using high-quality data sources for Mendelian randomization studies. It discusses the different types of data that can be used, such asgenome-wide association studies and biobanks, and offers advice on how to validate genetic instruments and ensure the reliability of the data.Chapter 4: Statistical MethodsIn this chapter, the guide explains the various statistical methods that can be used to analyze Mendelian randomization data. It covers techniques such as inverse variance weighting, MR-Egger regression, and bi-directional Mendelian randomization, and provides guidance on how to choose the most appropriate method for a given study.Chapter 5: Interpretation and ReportingThe final chapter of the guide focuses on the interpretation and reporting of Mendelian randomization results. It discusses how to assess the strength of causal inference, consider potential biases, and communicate findings effectively in research papers and presentations.ConclusionThe "Guide to Mendelian Randomization Studies" is a valuable resource for researchers who are interested in using genetic data to investigate causal relationships in epidemiological studies. By following the guidance provided in the guide, researchers can enhance the rigor and validity of their Mendelian randomization studies and contribute to a better understanding of the determinants of complex traits and diseases.。

THE CIPIC HRTF DATABASEV.R.Algazi,R.O.Duda and D.M.ThompsonCIPICU.C.DavisDavis,CA95616,USA******************.eduC.AvendanoCreative Advanced Technology Center 1500Green Hills RoadScotts Valley,CA95066********************.comABSTRACTThis paper describes a public-domain database of high-spatial-resolution head-related transfer functions measured at the U.C.Davis CIPIC Interface Laboratory.Release1.0includes head-related impulse responses for45subjects at25different azimuths and50differ-ent elevations(1250directions)at approximately5angular incre-ments.In addition,the database contains anthropometric measure-ments for each subject.Statistics of anthropometric parametersand correlations between anthropometry and some temporal and spectral features of the HRTFs are reported.1.INTRODUCTIONHead-related transfer functions(HRTFs)capture the sound local-ization cues created by the scattering of incident sound waves bythe body,and play a central role in spatial audio systems.Most HRTF-based commercial systems convolve the input signal witha single,“standard”head-related impulse response(HRIR),and several studies have employed the public-domain dataset for the KEMAR mannequin[1].However,it is well known that HRTFs vary significantly from person to person,and that serious percep-tual distortions(particularly front/back confusion and elevation er-rors)can occur when one listens to sounds spatialized with a non-individualized HRTF[2].Although the determination of individual HRTFs can addressedin a number of ways,most recently by numerical computations based on a detailed geometric mesh of the human body[3,4],the study of individual variations requires a database of uniformly measured HRTFs.Several laboratories have developed HRTF data-bases to support their own research(e.g.,[5]).However,the only publicly available database is the AUDIS catalog[6],which is lim-ited to12subjects measured at approximately120positions in space,and cannot be used for commercial purposes.The CIPIC Interface Laboratory at U.C.Davis has measured HRTFs at high spatial resolution for more than90subjects.Re-lease1.0—a public-domain subset for45subjects(including KE-MAR with large and with small pinnae)—is available by down-loading from our web site().In addition to including impulse responses for1250directions for each ear of each subject,the database includes a set of anthro-pometric measurements that can be used for scaling studies.This paper describes the content of the database,and briefly describesthe characteristics of the data.Additional technical documenta-tion and MATLAB utility programs for inspecting the data are provided with the databasefiles.2.MEASUREMENTSExcluding the KEMAR mannequin,the43human subjects(27 men and16women)were either U.C.Davis students or visitors to the CIPIC Interface Laboratory.All HRTFs were measured with the subject seated at the center of a1-m radius hoop whose axis was aligned with the subject’s interaural axis.The position of the subject’s head was not constrained,but the subject could monitor his or her head position[7].1Bose Acoustimass loudspeakers(5.8-cm cone diameter)were mounted at various positions along the hoop.A modified Snapshot system from Crystal River Engineering generated Golay-code sig-nals.The subject’s ear canals were blocked,and Etymwere uniformly sampled in steps fromto.To obtain roughly uniform density on the sphere, azimuths were sampled at,from toin steps of,at,and.This leads to spatial sampling at1250points,as illustrated in Fig.1.3.ANTHROPOMETRYAlthough the exact HRTF is complicated,its general behavior can be estimated from fairly simple geometric models of the torso, head and pinnae[8,9,10].These models can be individualized to particular listeners if appropriate anthropometric measurements are available[11].However,specifying a general set of well-defined and relevant measurements is problematic.The problem is particularly difficult for the pinna,where small variations can pro-duce large changes in the HRTF.Anthropometric measurements, even if imperfect,enable the investigation of possible correspon-dences or correlations between physical dimensions and HRTF features.The choice of anthropometry relevant to understanding or es-timating HRTFs lead us to follow an approach proposed by Genuit [8],and to define a set of27anthropometric measurements—17 for the head and torso(Fig.2)and10for the pinna(Fig.3).4Figure2:Head and torso measurementsThe range of variation for the individuals in the CIPIC database can be measured by some statistics for the anthropometric mea-surements.In general,histograms of the individual measurements indicate a basically normal distribution of values.The means and standard deviations for the anthropometric parameters are listed inTable1.5Here distances are measured in cm and angles in degrees, and the percentage variation is in percent.For example,the mean head width was14.49cm,and,assuming a normal distribu-tion,95%of the cases were within%of the mean.Excludingthe offset measurements and,for which percentage de-viation is not meaningful,we see that the average percentage de-head width14.490.9513head height21.46 1.2412head depth19.96 1.2913pinna offset down 3.030.6643pinna offset back0.460.59254neck width11.68 1.1119neck height 6.26 1.6954neck depth10.52 1.2223torso top width31.50 3.1920torso top height13.42 1.8528torso top depth23.84 2.9525shoulder width45.90 3.7816head offset forward 3.03 2.29151height172.4311.6113seated height88.83 5.5312head circumference57.33 2.479shoulder circumference109.4310.3019cavum concha height 1.910.1819cymba concha height0.680.1235cavum concha width 1.580.2835fossa height 1.510.3344pinna height 6.410.5116pinna width 2.920.2718intertragal incisure width0.530.1451cavum concha depth 1.020.1632pinna rotation angle24.01 6.5955pinnaflare angle28.53 6.7047Table1.Anthropometric statistics,Correlations between these measurements may be of interest, since one might conjecture that a subject with a large head would also have large pinnae.Indeed,this is the basic assumption be-hind Middlebrooks’s procedure for scaling HRTFs to account for changes in body size[14].In general,there are statistically signif-icant but weak correlations between most pairs of measurements.6 Scatterplots and correlation coefficients for four interesting ex-amples are shown in Fig.4.We focus on the important but difficult to measure pinna di-mensions.Fig.4a shows that there is a fairly good correlation be-tween pinna height and cavum concha height().There is also some correlation between head depth and cavum concha width(Fig.4b,).Interestingly,there is not much correlation between these two concha dimensions(Fig.4c,).Per-haps more surprising,there is little correlation between head height and pinna height(Fig.4d,),and about the same corre-lation between head height and cavum concha height. In general,there appears to be relatively little correlation between the sizes of large and small anatomical features,and accurate esti-mation of pinna dimensions from head and torso measurements is problematic.Figure4:Selected scatterplots4.HRTF V ARIATIONOne of the advantages of measuring HRTF data at high spatial res-olution is that the data can be represented as an image.Fig.5a shows such an image representation of the impulse response for KEMAR’s right ear.Each column in this image is one impulse re-sponse at a particular azimuth,7with brightness coding the strength of the response.The variation of arrival time with azimuth is clearly seen in the roughly sinusoidal shape of the top envelope of the response.The weakening of the response as the azimuth approaches the opposite side of the head shows the effect of head shadow.Fig.5b shows the spectrum for this same case.Here each col-umn is the magnitude of the HRTF in dB,after the power spec-trum was smoothed by a constant-filter().The gener-ally darker appearance of the right half of the image shows the effect of head shadow.The strong response on the ipsilateral side around5kHz corresponds to the quarter-wavelength depth reso-nance identified by Shaw[9],and the weak response around9kHz is the so-called“pinna notch.”In addition,other interesting butFigure5:Horizontal plane(a)HRIR(b)HRTF These images give some idea of the HRTF variability for a sin-gle subject.It is more difficult to characterize the range of HRTF variability between subjects.However,two simple measures—the maximum interaural time difference ITD and the pinna-notch frequency—are simple,perceptually relevant parameters thatcharacterize the variability that exists.For the subjects in the database,ITD is approximately nor-mally distributed,with sec and sec,which corresponds to a%variation.Not surprisingly,ITD is strongly correlated with head size(see Fig.6),and it can be es-timated quite accurately using simple linear regression.The best single predictor is the head width,with a correlation coefficient of between the estimated and the actual ITD.The best pair are the head width and the head depth,for which.For a more detailed presentation of the estimation of ITD from anthro-pometry,see[11].Figure6:Scatterplots for estimation of the ITDIn the frequency-domain,most HRTFs exhibit the prominent depth resonance around3to4kHz,followed by the pinna“notch”[9].Fig.7shows the HRTF magnitudes for for a set of54subjects.8The pinna notches are indicated by the black dots, and the graphs are sorted by the pinna-notch frequency.Statistically,is approximately normally distributed,with Hz and Hz,which corresponds to a rather large%variation.As expected,is correlated with the pinna measurements,but the relationship is not strong,and linear regression is not as successful in estimating from anthropom-etry.The best single predictor of is the cavum concha height ().Somewhat surprisingly,the best pair of predictors are the two angles and(),and the best triple adds to these the fossa height).These results reflect the fact that the scattering of incident waves by the pinna is a complex pro-cess related to detailed features,and that accurate estimation of may well require additional concha parameters not included in our measurements.However,simple regression analysis does help identify the most significant of the measured parameters or indicates the need for additional measurements.It our view that the effective customization of HRTFs will requires a deeper under-standing of the perceptually important characteristics of the HRTF and of their dependence on detailed pinna features.9Figure7:HRTF magnitudes for5.CONCLUSIONSHigh-spatial-resolution HRTF measurements clarify the physical sources of HRTF behavior.A uniform database of HRTFs en-ables the study of person-to-person differences and of the relation of temporal and spectral characteristics of the HRTF to the anthro-pometric data.We hope that public availability of the CIPIC HRTF database,augmented with anthropometric measurements,will fa-cilitate further research in the understanding,modeling,and use of individualized HRTFs.6.ACKNOWLEDGMENTSThis work was supported by the National Science Foundation un-der grants IRI-96-19339and ITR-00-86075,and by the University of California DiMI program,with additional support from Aureal。

gallager随机构造法Gallager随机构造法是一种用于构造纠错码的方法，它是由Robert G. Gallager在1962年提出的。

该方法通过随机地选择编码矩阵的一些列，来生成纠错码，以提高通信系统的可靠性和容错性。

在通信系统中，数据传输过程中常常会出现噪声和错误，导致接收端收到的数据与发送端发出的数据不一致。

为了解决这个问题，人们提出了纠错码的概念，通过在发送的数据中加入一些冗余信息，使得接收端能够检测和纠正部分错误。

纠错码的设计是一项非常重要和复杂的任务，而Gallager随机构造法为我们提供了一种简单而有效的构造方法。

Gallager随机构造法的基本思想是通过随机选择编码矩阵的一些列来生成纠错码。

具体来说，首先确定编码矩阵的大小，即确定码长和信息位的个数。

然后，随机选择矩阵的列，并将这些列作为编码矩阵的一部分。

最后，通过对编码矩阵进行线性变换，得到纠错码。

通过这种随机构造方法生成的纠错码具有以下特点：1. 容错性强：Gallager随机构造法生成的纠错码能够有效地纠正和检测错误。

这是因为随机选择的编码矩阵的性质使得纠错码在不同位置上的冗余信息分布较为均匀，从而提高了纠错码的容错性能。

2. 码长可变：Gallager随机构造法生成的纠错码的码长可以根据需要进行调整。

通过增加或减少编码矩阵的列数，可以改变纠错码的码长，从而适应不同的通信需求。

3. 算法简单：Gallager随机构造法的实现相对简单，不需要复杂的计算和复杂的纠错码编解码算法。

这使得该方法成为一种实用的纠错码构造方法。

然而，Gallager随机构造法也存在一些限制和不足之处。

首先，随机选择编码矩阵的列可能导致生成的纠错码不能满足一些特定的性能要求。

其次，随机构造的编码矩阵可能存在冗余或不充分的情况，从而影响纠错码的性能。

因此，在使用Gallager随机构造法生成纠错码时，需要进行一些优化和调整，以满足具体的通信需求。

Gallager随机构造法是一种简单而有效的纠错码构造方法，通过随机选择编码矩阵的列来生成纠错码。

gallager随机构造法Gallager随机构造法是一种常用于编码理论中的方法，用于构造能够纠正通信中的错误的编码方案。

在通信过程中，由于噪声等原因，传输的信息可能会出现错误。

为了提高通信的可靠性，可以使用编码方案来检测和纠正这些错误。

Gallager随机构造法是由Robert G. Gallager在1962年提出的一种构造码字的方法。

这种方法以概率分布为基础，通过随机选择编码方案中的各个参数，来构造一种能够纠正通信中错误的编码方案。

这种方法的优点是可以灵活地根据实际情况选择参数，从而使得编码方案更加适应通信环境的变化。

在Gallager随机构造法中，首先需要确定纠错码的参数，包括码长（n）和码字长度（k）。

码长是指编码方案中码字的长度，而码字长度是指编码方案中用于表示有效信息的位数。

根据通信系统的需求，可以选择适当的码长和码字长度。

接下来，需要随机生成一个生成矩阵。

生成矩阵是一个k行n列的矩阵，其中的元素由0和1随机组成。

生成矩阵的每一行对应编码方案中的一个码字，每一列对应编码方案中的一个位。

生成矩阵的构造要求是任意两行之间的汉明距离（即两个码字间不同位的个数）大于等于3，并且生成矩阵的每一列中至少有两个1。

构造生成矩阵的方法可以是随机生成，也可以是根据特定规则生成。

生成矩阵的构造方法直接影响了编码方案的性能。

Gallager随机构造法通过随机选择生成矩阵中的元素，从而使得生成矩阵具有良好的纠错性能。

在生成矩阵确定之后，可以使用生成矩阵来进行编码和解码。

编码是将待传输的信息转换为码字的过程，而解码是将接收到的码字转换为原始信息的过程。

编码和解码的过程可以通过矩阵运算来实现。

具体来说，编码是将待传输的信息乘以生成矩阵，而解码是将接收到的码字乘以生成矩阵的转置矩阵。

通过使用Gallager随机构造法构造的编码方案，可以有效地检测和纠正通信中的错误。

由于生成矩阵的构造是随机的，因此每个码字之间的关系是独立的，从而可以提高编码方案的纠错能力。

edgeR的使⽤1）简介转⾃：https:///djx571/p/9647011.htmledgeR作⽤对象是count⽂件，rows 代表基因，⾏代表⽂库，count代表的是⽐对到每个基因的reads数⽬。

它主要关注的是差异表达分析，⽽不是定量基因表达⽔平。

edgeR works on a table of integer read counts, with rows corresponding to genes and columns to independent libraries. The counts represent the total number of reads aligning to each gene (or other genomic locus).edgeR is concerned with differential expression analysis rather than with the quantification of expression levels. It is concerned with relative changes in expression levels between conditions,but not directly with estimating absolute expression levels.edgeR作⽤的是真实的⽐对统计，因此不建议⽤预测的转录本Note that edgeR is designed to work with actual read counts. We not recommend that predicted transcript abundances are input the edgeR in place of actual counts.归⼀化原因：技术原因影响差异表达分析：1）Sequencing depth：统计测序深度(即代表的是library size)；2）RNA composition：个别异常⾼表达基因导致其它基因采样不⾜3）GC content： sample-specific effects for GC-content can be detected4）sample-specific effects for gene length have been detected注意：edgeR必须是原始表达量，⽽不能是rpkm等矫正过的。

【R高级教程】专题二：差异表达基因的分析应学生及个别博友的要求，尽管专业博文点击率和反应均很差，但在去San Diego参加PAG会议之前，还是抽时间给出【R高级教程】的第二专题。

专题一给出了聚类分析的示例，本专题主要谈在表达谱芯片分析中如何利用Bioconductor鉴定差异表达基因。

鉴定差异表达基因是表达谱芯片分析pipeline中必须的分析步骤。

差异表达基因分析是根据表型协变量（分类变量）鉴定组间差异表达，它属于监督性分类的一种。

在鉴定差异表达基因以前，一般需要对表达值实施非特异性过滤（在机器学习框架下属于非监督性分类），因为适当的非特异性过滤可以提高差异表达基因的检出率、甚至是功效。

R分析差异表达基因的library有很多，但目前运用最广泛的Bioconductor包是limma。

本专题示例依然来自GEO数据库中检索号为GSE11787 的Affymetrix芯片的数据，数据介绍参阅专题一。

>library(limma)>design <- model.matrix(~ -1+factor(c(1,1,1, 2,2,2)))这个是根据芯片试验设计，对表型协变量的水平进行design，比如本例中共有6张芯片，前3张为control对照组，后3张芯片为实验处理组，用1表示对照组，用2表示处理组。

其他试验设计同理，比如2*2的因子设计试验，如果每个水平技术重复3次，那么可以表示为：design <- model.matrix(~ -1+factor(c(1,1,1, 2,2,2,3,3,3, 4,4,4)))。

接上面的程序语句继续：>colnames(design) <- c("control", "LPS")>fit <- lmFit(eset2, design)>contrast.matrix <- makeContrasts(control-LPS, levels=design)>fit <- eBayes(fit)>fit2 <- contrasts.fit(fit, contrast.matrix)>fit2 <- eBayes(fit2)>results<-decideTests(fit2, method="global", adjust.method="BH",p.value=0.01, lfc=1.5)>summary(results)>vennCounts(results)>vennDiagram(results)比较遗憾的是，目前limma自带的venn作图函数不能做超过3维的高维venn图，只能画出3个圆圈的venn图，即只能同时对三个coef进行venn作图。

细胞实验技术之细胞周期检测导读细胞周期是指细胞分裂结束到下一次细胞分裂结束所经历的时间，它代表着生命从一代向下一代传递的连续过程，与前几期我们介绍过的细胞学实验（细胞增殖、克隆形成等）一样，细胞周期也是评价细胞增殖功能的重要实验。

流式细胞仪是检测细胞周期最常用的方法，然而我们会碰到细胞量不够、细胞碎片太多等原因，导致实验一次次重复，本文就一起看看如何把细胞周期的数据变的更加漂亮，准确！一、细胞周期简介主要分为以下2大过程：1.分裂间期：间期又分为三期、即DNA合成前期（G1期）、DNA合成期（S期）与DNA合成后期（G2期）；2.分裂期M期：细胞分裂期，指细胞分裂开始到结束。

细胞周期图（来自网络）•注：G0期是指某些细胞在分裂结束后会暂时离开细胞周期，停止细胞分裂；但在一定适宜刺激下，又可进入周期；•因为分裂间期持续的时间远远比分裂期持续时间长，在一个正常细胞周期中，分裂间期时间会占整个细胞周期的90%～95%；•不同类型细胞的G1期时间长短不同，所以其细胞周期时间存在差异。

如：人类胃上皮细胞为24小时，骨髓细胞为18小时，HeLa细胞为21小时。

二、常用的实验方法细胞周期常用检测方法有流式检测法、BrdU(5-溴脱氧尿嘧啶核苷)掺入法及同位素标记法等，其中流式检测法因适用于大量样品检测，可快速分析单个细胞的多种特性，是目前最为常用的测定细胞周期的一种方法，下面就详细介绍如何利用流式细胞仪进行周期分析。

1. 流式检测的实验原理由于细胞周期各时相的DNA含量不同，因此，可通过特异性与DNA结合染料来检测细胞内的DNA含量来测定细胞周期。

流式中常用碘化丙啶(Propidium，简称PI)与DNA结合，其荧光强度与DNA 含量成正比。

因此，通过流式细胞仪对细胞内DNA含量进行检测，同时获得的流式直方图对应的各细胞周期可通过特殊软件计算各时相的细胞百分率。

2. 流式细胞仪的实验步骤A. 收集细胞取适量的对数生长期细胞接种于6cm中，在相应的条件下（如药物）处理相应时间后，倒去培养基，用胰酶适度消化细胞，离心收集细胞，弃去上清；Tips:•细胞数量：一般情况下，由于在细胞周期中分析的细胞数应达到1.0*104~3.0*104才具有统计学意义。

孟德尔随机化转换样本量
孟德尔随机化是一种实验设计方法，用于减少实验中可能存在的偏差和混杂因素对结果的影响。

在进行孟德尔随机化时，需要确定适当的样本量以获得可靠的研究结果。

确定样本量的步骤如下：
1. 确定所需的显著性水平（α）和统计功效（1-β）。

显著性水平是指拒绝零假设的概率阈值，通常设置为0.05或0.01。

统计功效是指能够检测到真实效应的概率，常见的统计功效水平为0.8或0.9。

2. 根据研究的特点和先前的经验，估计所需的效应大小。

这可以通过文献回顾、先前的实验结果或专家意见来确定。

3. 使用适当的统计方法，根据以上信息计算所需的样本量。

常用的方法包括方差分析、t检验、卡方检验等。

4. 考虑实际可行性和资源限制，确定最终的样本量。

有时候，实验者不得不在可行性和科学要求之间做出权衡。

在孟德尔随机化设计中，样本量的确定是至关重要的，因为样本量大
小直接影响到实验的统计效力和结果的可靠性。

通过合理地确定样本量，可以确保研究具有足够的统计能力来探测真实效应，并提高研究的科学价值。

手把手教你看懂测序类文章随着测序愈发的普及，测序相关的文章也是越来越多，但是刚接触科研的童鞋看到这方面的文章仍旧是一头雾水，今天就给大家讲一讲测序类的文章怎么看，怎么写。

所谓测序类的文章是指——在整个文章中，测序这一实验方法对得到文章结论的得出起到了重要的支撑作用。

既然要做测序，那么样本准备是前期必要的工作，你是用的动物模型，细胞模型还是临床样本，这是你首先要讲明白的。

所以Methods里面的第一部分一般是Sample的介绍，细胞、动物怎么养的啦，临床样本怎么取的，临床特征如何等等。

结果里自然就是细胞培养的图片啦，动物组织的免疫组化等等说明建模成功的结果，如果说是比较常规的模型，这些结果一般放在supplymentary里面就可以了。

大鼠模型的免疫组化结果（Untreated and CCl4-treated rats）样本送去测序之后，测序公司会有一份详细的测序报告，接下来就需要详细的报告结果的解读，但并非所有的东西都是要说的。

一般来说第一步是样本中总RNA的提取（Total RNA extraction），试剂盒上都会有详细的说明，照着抄吧。

第二步就是关于测序过程的了，包括测序流程、RNA文库构建等等，这一部分测序结果的报告中会有说明，照着抄吧X2。

第三步是测序结果的数据分析，测序结果中我们会得到下面这样一个表达矩阵样的文件，Y轴探针编号，X轴是样本名称。

差异基因分析是测序中非常重要的一步，在作这样一个分析之前，会对这样一个表达数据进行标准化的处理，所以我们经常会看到这样一个箱式图，箱式图中的中位数（矩形中间的那条黑线）基本处于同一水平线上，从而达到一个质量控制的效果。

那结果中怎么写呢？大体上就是说我们对数据进行了标准化处理，你看呐，我们这个箱式图的中位数这根线对齐的很好，说明我们标准化处理这一步处理的不错（基本上有箱式图的测序文章，你都能看到上面这样类似的话，当然了，是英文的）此外还有主成分分析（PCA），有三维的有二维的，图中一个点代表一个样本。

简单使用DESeq做差异分析–生信笔记DESeq这个R包主要针对count data，其数据来源可以是RNA-Seq或者其他高通量测序数据。

类似地，对于CHIP-Seq数据或者质谱肽段数据也是使用的。

由于DESeq是一个R包，因此使用它需要一点点R基础语法。

1.首先需要读入一个数据框，列代表每个sample，行代表每个gene2.database_all <- read.table(file = "readcount", sep = "\t", header = T, s = 1)database <- database_all[,1:6]这里主要对于两两比较的数据，因此我取了数据的前6列，分别是两组样品，每组3个生物学重复3.设定分组信息，也就是样本分组的名称type <- factor(c(rep("LC_1",3), rep("LC_2",3)))我这里是样品1是LC_1，样品2是LC_24.由于DESeq包要求接下来的count data必须要整数型，因此我们需要对数据进行取整,然后将数据database和分组信息type读入到cds对象中5.database <- round(as.matrix(database))cds <- newCountDataSet(database,type)6.接下里对于不同类型的数据要进行不同的处理，可以粗略分为有生物学重复数据、有部分生物学重复数据以及无生物学重复数据4.1 有生物学重复cds <- estimateSizeFactors(cds)cds <- estimateDispersions(cds)res <- nbinomTest(cds,"LC_1","LC_2")其通过estimate the dispersion并对count data进行标准化，然后得到每个gene做T test检验4.2 对于部分样品有生物学重复cds <- estimateSizeFactors(cds)cds <- estimateDispersions(cds)res <- nbinomTest(cds,"LC_1","LC_2")其步骤跟有上述的一样的，DESeq会根据有生物学重复的样品来estimate the dispersion，当然要保证unreplicated condition does not have larger variation than the replicated one4.3 对于没有生物学重复cds <- estimateDispersions(cds, method="blind", sharingMode="fit-only" )res <- nbinomTest(cds,"LC_1","LC_2")注意参数met hod=”blind” 和sharingMode=”fit-only”即可7.最后就是查看符合阈值的差异基因有多少个即可，然后将结果输出到csv文件中方便查看8.table(res$padj <0.05)9.res <- res[order(res$padj),]10.sum(res$padj<=0.01,na.rm = T)write.csv(resdata,file = "LC_1_vs_LC_2_DESeq.csv")SummaryDESeq在前几年的文章中经常被使用，但是现在有了其升级版DESeq2，后者相比前者对于犯第一类错误卡的并不是那么严格了，所以在同样的padj的阈值下，筛选到的差异基因的数目也会多一点。

转录组edgeR分析差异基因生信菜鸟团转录组edgeR分析差异基因edgeR是一个研究重复计数数据差异表达的Bioconductor软件包。

一个过度离散的泊松模型被用于说明生物学可变性和技术可变性。

经验贝叶斯方法被用于减轻跨转录本的过度离散程度，改进了推断的可靠性。

该方法甚至能够用最小重复水平使用，只要至少一个表型或实验条件是重复的。

该软件可能具有测序数据之外的其他应用，例如蛋白质组多肽计数数据。

可用性：程序包在遵循LGPL许可证下可以从Bioconductor网站。

一：下载安装该软件下载安装edgeR这个R包，因为这是一次讲R包的下载，我就啰嗦一点，这种生物信息学的包不同于普通的R包，是需要用biocLite 来安装的，命令如下安装成功之后会有以下提示。

但是我加载碰到一个很幼稚的错误，因为我的电脑太差了，这是一个测试的电脑，是300块钱在二手市场里面淘的，所以内存不够。

我简单搜索了一下，才知道是虚拟内存太小了，需要调整重启电脑，就成功啦二：准备数据就是对tophat的bam文件用HTseq计数后的count文件，见前一篇文章三：运行命令因为主要是在R里面操作，我就只讲R里面的命令了，首先要把那些HTseq产生的文件拷贝到R的工作目录，我这里是自己设置了工作目录setwd("D:\\项目\\RNA-seq\\htseq")a=read.table("case1.sam.count")b=read.table("case2.sam.count")c=read.table("control.sam.count")counts=data.frame(case1=a[,2],case2=b[,2],control=c[,2 ])rownames(counts)=a[,1]这样就读入了一个counts数据框可以看到有三个样本，涉及到了23373个基因，每个样本的测序量约50M的reads可以看到，有很多基因的计数不到30次。